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jcmarchi · 2 months ago

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Steve Wilson, Chief AI and Product Officer at Exabeam – Interview Series

New Post has been published on https://thedigitalinsider.com/steve-wilson-chief-ai-and-product-officer-at-exabeam-interview-series/

Steve Wilson, Chief AI and Product Officer at Exabeam – Interview Series

Steve Wilson is the Chief AI and Product Officer at Exabeam, where his team applies cutting-edge AI technologies to tackle real-world cybersecurity challenges. He founded and co-chairs the OWASP Gen AI Security Project, the organization behind the industry-standard OWASP Top 10 for Large Language Model Security list.

His award-winning book, “The Developer’s Playbook for Large Language Model Security” (O’Reilly Media), was selected as the best Cutting Edge Cybersecurity Book by Cyber Defense Magazine.

Exabeam is a leader in intelligence and automation that powers security operations for the world’s smartest companies. By combining the scale and power of AI with the strength of our industry-leading behavioral analytics and automation, organizations gain a more holistic view of security incidents, uncover anomalies missed by other tools, and achieve faster, more accurate and repeatable responses. Exabeam empowers global security teams to combat cyberthreats, mitigate risk, and streamline operations.

Your new title is Chief AI and Product Officer at Exabeam. How does this reflect the evolving importance of AI within cybersecurity?

Cybersecurity was among the first domains to truly embrace machine learning—at Exabeam, we’ve been using ML as the core of our detection engine for over a decade to identify anomalous behavior that humans alone might miss. With the arrival of newer AI technologies, such as intelligent agents, AI has grown from being important to absolutely central.

My combined role as Chief AI and Product Officer at Exabeam reflects exactly this evolution. At a company deeply committed to embedding AI throughout its products, and within an industry like cybersecurity where AI’s role is increasingly critical, it made sense to unify AI strategy and product strategy under one role. This integration ensures we’re strategically aligned to deliver transformative AI-driven solutions to security analysts and operations teams who depend on us most.

Exabeam is pioneering “agentic AI” in security operations. Can you explain what that means in practice and how it differentiates from traditional AI approaches?

Agentic AI represents a meaningful evolution from traditional AI approaches. It’s action-oriented—proactively initiating processes, analyzing information, and presenting insights before analysts even ask for them. Beyond mere data analysis, agentic AI acts as an advisor, offering strategic recommendations across the entire SOC, guiding users toward the easiest wins and providing step-by-step guidance to improve their security posture. Additionally, agents operate as specialized packs, not one cumbersome chatbot, each tailored with specific personalities and datasets that integrate seamlessly into the workflow of analysts, engineers, and managers to deliver targeted, impactful assistance.

With Exabeam Nova integrating multiple AI agents across the SOC workflow, what does the future of the security analyst role look like? Is it evolving, shrinking, or becoming more specialized?

The security analyst role is definitely evolving. Analysts, security engineers, and SOC managers alike are overwhelmed with data, alerts, and cases. The real future shift is not just about saving time on mundane tasks—though agents certainly help there—but about elevating everyone’s role into that of a team lead. Analysts will still need strong technical skills, but now they’ll be leading a team of agents ready to accelerate their tasks, amplify their decisions, and genuinely drive improvements in security posture. This transformation positions analysts to become strategic orchestrators rather than tactical responders.

Recent data shows a disconnect between executives and analysts regarding AI’s productivity impact. Why do you think this perception gap exists, and how can it be addressed?

Recent data shows a clear disconnect: 71% of executives believe AI significantly boosts productivity, but only 22% of frontline analysts, the daily users, agree. At Exabeam, we’ve seen this gap grow alongside the recent frenzy of AI promises in cybersecurity. It’s never been easier to create flashy AI demos, and vendors are quick to claim they’ve solved every SOC challenge. While these demos dazzle executives initially, many fall short where it counts—in the hands of the analysts. The potential is there, and pockets of genuine payoff exist, but there’s still too much noise and too few meaningful improvements. To bridge this perception gap, executives must prioritize AI tools that genuinely empower analysts, not just impress in a demo. When AI truly enhances analysts’ effectiveness, trust and real productivity improvements will follow.

AI is accelerating threat detection and response, but how do you maintain the balance between automation and human judgment in high-stakes cybersecurity incidents?

AI capabilities are advancing rapidly, but today’s foundational “language models” underpinning intelligent agents were originally designed for tasks like language translation—not nuanced decision-making, game theory, or handling complex human factors. This makes human judgment more essential than ever in cybersecurity. The analyst role isn’t diminished by AI; it’s elevated. Analysts are now team leads, leveraging their experience and insight to guide and direct multiple agents, ensuring decisions remain informed by context and nuance. Ultimately, balancing automation with human judgment is about creating a symbiotic relationship where AI amplifies human expertise, not replaces it.

How does your product strategy evolve when AI becomes a core design principle instead of an add-on?

At Exabeam, our product strategy is fundamentally shaped by AI as a core design principle, not a superficial add-on. We built Exabeam from the ground up to support machine learning—from log ingestion, parsing, enrichment, and normalization—to populate a robust Common Information Model specifically optimized to feed ML systems. High-quality, structured data isn’t just important to AI systems—it’s their lifeblood. Today, we directly embed our intelligent agents into critical workflows, avoiding generic, unwieldy chatbots. Instead, we precisely target crucial use-cases that deliver real-world, tangible benefits to our users.

With Exabeam Nova, you’re aiming to “move from assistive to autonomous.” What are the key milestones for getting to fully autonomous security operations?

The idea of fully autonomous security operations is intriguing but premature. Fully autonomous agents, across any domain, simply aren’t yet efficient or safe. While decision-making in AI is improving, it hasn’t reached human-level reliability and won’t for some time. At Exabeam, our approach isn’t chasing total autonomy, which my group at OWASP identifies as a core vulnerability known as Excessive Agency. Giving agents more autonomy than can be reliably tested and validated puts operations on risky ground. Instead, our goal is teams of intelligent agents, capable yet carefully guided, working under the supervision of human experts in the SOC. That combination of human oversight and targeted agentic assistance is the realistic, impactful path forward.

What are the biggest challenges you’ve faced integrating GenAI and machine learning at the scale required for real-time cybersecurity?

One of the biggest challenges in integrating GenAI and machine learning at scale for cybersecurity is balancing speed and precision. GenAI alone can’t replace the sheer scale of what our high-speed ML engine handles—processing terabytes of data continuously. Even the most advanced AI agents have a “context window” that is vastly insufficient. Instead, our recipe involves using ML to distill massive data into actionable insights, which our intelligent agents then translate and operationalize effectively.

You co-founded the OWASP Top 10 for LLM Applications. What inspired this, and how do you see it shaping AI security best practices?

When I launched the OWASP Top 10 for LLM Applications in early 2023, structured information on LLM and GenAI security was scarce, but interest was incredibly high. Within days, over 200 volunteers joined the initiative, bringing diverse opinions and expertise to shape the original list. Since then, it’s been read well over 100,000 times and has become foundational to international industry standards. Today, the effort has expanded into the OWASP Gen AI Security Project, covering areas like AI Red Teaming, securing agentic systems, and handling offensive uses of Gen AI in cybersecurity. Our group recently surpassed 10,000 members and continues to advance AI security practices globally.

Your book, ‘The Developer’s Playbook for LLM Security‘, won a top award. What’s the most important takeaway or principle from the book that every AI developer should understand when building secure applications?”

The most important takeaway from my book, “The Developer’s Playbook for LLM Security,” is simple: “with great power comes great responsibility.” While understanding traditional security concepts remains essential, developers now face an entirely new set of challenges unique to LLMs. This powerful technology isn’t a free pass, it demands proactive, thoughtful security practices. Developers must expand their perspective, recognizing and addressing these new vulnerabilities from the outset, embedding security into every step of their AI application’s lifecycle.

How do you see the cybersecurity workforce evolving in the next 5 years as agentic AI becomes more mainstream?

We’re currently in an AI arms race. Adversaries are aggressively deploying AI to further their malicious goals, making cybersecurity professionals more crucial than ever. The next five years won’t diminish the cybersecurity workforce, they’ll elevate it. Professionals must embrace AI, integrating it into their teams and workflows. Security roles will shift toward strategic command—less about individual effort and more about orchestrating an effective response with a team of AI-driven agents. This transformation empowers cybersecurity professionals to lead decisively and confidently in the battle against ever-evolving threats.

Thank you for the great interview, readers who wish to learn more should visit Exabeam.

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virtualizationhowto · 2 years ago

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Suricata vs Snort: Which is the best IDS?

Suricata vs Snort: Which is the best IDS? #homelab #selfhosted #SuricataVsSnortComparison #IntrusionDetectionSystems #NetworkSecurityTools #AnomalyBasedDetection #SnortNetworkAnalysis #SuricataMultiThreadedApproach #IDSandIPSDifferences #CommunitySupport

Security and network professionals have long trusted a couple of tools in network security. Suricata and Snort are popular choices among security professionals for intrusion detection systems IDS and intrusion prevention system IPS solutions. This post will delve into a detailed comparison between Suricata vs Snort security solutions, looking at their architectures, capabilities, community…

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#Anomaly-based detection #Community support in IDS #IDS and IPS differences #Intrusion Detection Systems #Network security tools #Packet capturing techniques #Rule-based threat detection #Snort&039;s network analysis #Suricata vs Snort comparison #Suricata&039;s multi-threaded approach

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gftimelord · 10 months ago

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Greetings!

I am Dr. Stanford Pines, you may call me 'Ford' or simply 'Doctor'. I suppose it is about time I explore the World Wide Web, or the 'Internet'. There is much I still have to learn and figure out since returning to this dimension.

It seems I have fallen quite out of touch for the most part during the thirty years I was gone. It's frankly very impressive and jarring to see how so much has changed so I might take some time for me to properly adjust but I digress.

I travel through plenty of dimensions with my brother Stanley quite often to further my research. Nothing we can't handle now that Cipher is out of the picture so I will continue adventuring and learning.

I am glad to be of acquaintance to you all!

Ad astra per aspera

- Dr. Stanford Pines

OOC: HAHA- Here's a Ford blog because I genuinely couldn't help myself. My main blog is @matrixbearer2024! This Stanford Pines is very similar to the timelords from the Doctor Who franchise but other than that he's still pretty much the same. I'll mostly have him set post-weirdmageddon but I'm open to shifting whichever point in time for questions or RPs, have fun everyone!

P.S. Down below are insights to his character and inventory for anyone interested or planning to interact with him!

Doc's inventory:

Modified Sonic Screwdriver

Rift Manipulation: Creates and stabilizes interdimensional rifts using doors as conduits.

Wood Manipulation: Can interact with wooden objects, allowing for unlocking, modifying, or opening them.

Lock Picking: Can unlock doors and secure mechanisms electronically.

Repair Capabilities: Repairs mechanical devices, machinery, and certain types of technology.

Environmental Scanning: Gathers environmental data, hazards, such as toxins, radiation, or other dangers, detecting anomalies, and analyzing energy signatures.

Data Analysis: Capable of analyzing data from various sources and providing real-time feedback, which can be especially useful in scientific or technical situations.

Communication Device: Interfaces with various technologies for sending and receiving signals.

Universal Translator: Translates languages in real time, enabling communication across the multiverse.

Communications Device: Functions as a communicator to contact other beings or devices across dimensions.

Energy Emission and Manipulation: Emits energy pulses to create barriers, distract enemies, or manipulate technology as well as manipulating energy sources, allowing it to overload systems or temporarily disable them.

Holographic Projection: Can create holographic displays for visualization of data, theories, or environments.

Lock Picking: Bypasses and unlocks physical and digital security systems.

Thermal Regulation: Measures and adjusts temperature in different environments.

Frequency Manipulation: Disrupts or enhances certain technologies by emitting sounds at specific frequencies.

Medical Functions: Provides advanced diagnostics and medical support, which is due to Doc’s preparedness(paranoia) for unforeseen events. (e.g. scanning for vital signs, diseases, and medical conditions; performing rudimentary medical diagnostics and suggest treatments; minor surgical procedures, such as suturing wounds; administering certain medications or injections in emergencies)

Forcefield Manipulation: Can activate and control protective barriers or shields, adding a layer of defense in dangerous situations.

Data Storage & Retrieval: Stores information and interacts with databases, making it a powerful tool for research.

Manipulation of Atoms: Using the power of the infinity die, Doc’s sonic screwdriver can manipulate the energy within atoms of entities that have a tangible, alterable form. This ability allows him to rearrange or shift the physical properties of objects or beings.

Weaponized Function: Can shoot energy blasts, though this function was originally designed as a laser for electronic tinkering.

Time Manipulation: Has limited abilities to manipulate time within a localized area, such as slowing down or speeding up the perception of time for specific objects or people.

Emergency Beacon: Can emit distress signals to call for help or alert allies in emergencies.

Multi-Purpose Tool: Serves as a general tool for tinkering, adjusting mechanisms, and solving puzzles, which aligns with Doc's analytical nature and creativity.

Repair Box

Immortality: A set of nanobots that constantly repairs and heals injuries, rendering Doc functionally immortal. While he cannot die from age or illness, fatal wounds can still kill him.

Healing Factor: Non-fatal wounds heal rapidly, which allow the Doctor to recover quickly from injuries that would otherwise incapacitate others.

Phantom Pain: He experiences phantom pain from time to time, a side effect of the repair box, which sometimes immobilizes him during particularly bad days.

Journal & Pen

Eidetic Memory: While Doc has a photographic memory, he carries a journal where he records his travels, discoveries, and reflections. This is partly an emotional release and partly a way to process the things he can never forget.

Personal Reflections: His journal also contains musings, sketches, and insights into his more philosophical thoughts, which he tends to keep private.

Zygon Force Field Device

Personal Shield: A portable device that creates a personal force field around the doctor which protects him from the worst injuries in moments of danger.

Camouflage: For a temporary time, the advanced zygon technology refracts and manipulates light to turn Doc invisible to the naked eye. This can be activated and disabled manually.

Limited Durability: Though powerful, the forcefield can only take so much damage at a time before it needs to recharge.

Advanced Medical Kit

Comprehensive: This kit contains emergency medical supplies, medications, and advanced tools for situations where the repair box might not immediately be enough. The doctor’s paranoia drives him to always be prepared.

Multiverse Map

Hand-drawn: A rough sketch and collection of dimensions he’s visited or studied, filled with notes about potential dangers and anomalies.

OOC: Does Doc carry a phone with him? Nope! Thinks it's something redundant because of his sonic being able to act as a communicator at a rudimentary level. Granted, you're not going to get stellar audio or video quality from something like that, but it works throughout the multiverse so Doc doesn't bother. If you plan on having an OC or other character meet him through this route, it could be as a transmission to his sonic!

Doc's Appearance:

Since the time he received the repair box, Doc has been biologically frozen in a state where his physical body remains in its 40s.

Doc is standing tall at 6'2", no different from most iterations of his canon counterparts.

His coat the the same as the one he already uses in post-canon gravity falls, that hasn't changed aesthetically.

The doctor sports a suit because if he's going to kick butt might as well do it in style, he also switches between neck-ties and bow-ties because bow-ties are cool.

He wears sneakers instead of his boots since those had worn down and broken sometime during his travels and sneakers are just generally easier to replace.

Underneath all his dress-up though he's covered in scars from past battles and his old tattoos that he'd never had the time to get rid of.

His glasses still have a crack in them, mostly because he couldn't be bothered to get a new pair of bifocals anyway.

Always clean shaven, yes he still shaves his face with fire that has never changed.

Key Quotes About Doc:

"You don't need to look like a monster to be one."

"HAHAHAHA- I just SNOGGED Madame de Pompadour!"

"ALLONS-Y!"

"This apple sucks I hate apples-"

"Laptop. Gimme!"

"Who da man?! ..... Oh, well I'm never saying that again."

"Immortality isn't living forever that's not what it feels like. Immortality is everybody else dying because you can't."

"Goodness is not goodness that seeks advantage. Good is good in the deepest pit without hope, without witness, without reward. Virtue is only virtue in extremis."

"Sometimes the only choices you have are bad ones, but you still have to choose."

"The day you lose someone isn't the worst. At least you've got something to do. It's all the days they stay dead."

"Pain is a gift. Without the capacity for pain we can't feel the hurt we inflict."

"There's a lot of things you need to get across this universe. Warp drive, wormhole refractors. You know the thing you need most of all? You need a hand to hold."

"Love is not an emotion. It's a promise."

"The universe is big. It’s vast and complicated and ridiculous. And sometimes, very rarely, impossible things just happen and we call them miracles."

"Some people live more in 20 years than others do in 80. It’s not the time that matters, it’s the person."

"I’m the doctor, and I save people."

"First thing’s first, but not necessarily in that order."

"You want weapons? We're in a library! Books! Best weapons in the world!"

"People assume that time is a strict progression of cause to effect, but actually from a non-linear, non-subjective viewpoint— it’s more like a big ball of wibbly wobbly… time-y wimey… stuff."

"I’m about to do something very clever and a tiny bit against the rules of the multiverse. It’s important that I’m properly dressed."

"Arrogance can trip you up.”

"Do what I do: Hold tight and pretend it’s a plan!"

"You’ll find that it’s a very small universe when I’m angry with you."

See the bowtie? I wear it and I don’t care. That’s why it’s cool."

"Big flashy things have my name written all over them. Well… not yet. Give me time and a crayon."

"Never cruel or cowardly. Never give up, never give in.”

"Rest is for the weary, sleep is for the dead.”

"You don’t want to take over the universe. You wouldn’t know what to do with it beyond shout at it."

"Never be certain of anything. It’s a sign of weakness."

"Courage isn’t just a matter of not being frightened, you know. It’s being afraid and doing what you have to do anyway."

“Why do humans never do as they’re told? Someone should replace you all with robots. No, on second thought, they shouldn’t, bad idea.”

"You know, the very powerful and the very stupid have one thing in common: they don’t alter their views to fit the facts; they alter the facts to fit their views.”

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cadejos · 11 months ago

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ON NANAMI'S POWER LEVEL, DOMAIN EXPANSIONS, DOMAIN COUNTERS, AND HOW JUJUTSU SOCIETY PLAYS A ROLE.

This analysis originally turned viral on twitter. I'm posting it here for archival.

Nanami treated sorcery like a job and Gojo treated sorcery like a lifestyle. I've thought long and hard about why Nanami does not have certain skills (DE, Simple Domain, etc) that'd easily bump him up in terms of power, as he's already very strong. The reason is two-fold:

He never set out to do more than what he absolutely had to do. ("Moderate effort where moderate effort suffices," etc)

Information about sorcery is very gatekept and compartmentalized, because Jujutsu society sucks.

For point number 1, we are to keep in mind that Nanami is a grade one sorcerer, very much the peak of what sorcery is supposed to be outside of Special Grade work. The purpose of sorcery, up until very recently, has been about killing curses, most of which are not special grade or intelligent. The disaster curses are anomalies, and battles with Domain users were very rare until they showed up. They vastly skewed the power system. Remember that not even Naobito Zenin, the head of one of the great clans, had a Domain expansion either, and it took the work of a Domain user (Megumi) and an experienced sorcerer killer (Toji) to properly counter Dagon in his domain.

If domain battles are truly so rare, I don't really blame Nanami for not going out of his way to work on developing one, especially since Domains require an element of self-assurance that Nanami, due to trauma and disposition, was never geared toward developing.

His soul was strong enough to protect against a novice Mahito subconsciously, which is a promising start, but once Mahito grew too strong he was way out of Nanami's scope (not to mention Gege deliberately tired him over the course of Shibuya) and Nanami was more inclined to take his loss gracefully than to force himself to craft an spontaneous Domain Expansion. It's not like he really had the energy to try, either.

Overall, developing a DE for the off chance that he stumbled upon a Domain user just doesn't sound like his style. And he wouldn't do it for fun, either, because jujutsu is not fun for him, and it never has been. It's just work.

Let's say he would want to at least develop a domain counter, though. That's where point number 2 steps in. The whole reason something as fundamental as a domain counter is so rare in jujutsu is purely because jujutsu society is inherently selfish and self-serving.

If I recall correctly, SD is not something you can teach due to a binding vow tied to the technique. It has to be something you learn on your own through observation and intuition, or by joining New Shadow Style. Up until UiUi's soul swapping, there wasn't a reliable work around for this conundrum. And the other domain counters? Old, not very well known, and gatekept by the clans.

Sometimes I'm inclined to believe jujutsu sorcerers learn sorcery not because of the school system but in spite of it. Unless you're already a genius, born gifted, or willing to go an extra -- ambiguously illicit -- mile (like Kusakabe), there's not much the average sorcerer can do, and not many tools for them to learn to begin with. Nanami is presented as the baseline of what modern day good sorcery looks like; what you can achieve if you're competent, and don't have the privilege of relying on very good mentors, obscure knowledge, or ancient techniques. Even then he had an expansion technique, not something every sorcerer has, and he was capable of achieving one of the pinnacles of Jujutsu, which is the black flash; precisely because of his attitude toward jujutsu and his ability to focus when things get serious.

Maybe if given enough time to heal from his psychological wounds, and given opportunities for more black flashes, as well as a strong enough incentive, he could have circumvented a lot of problems and enlightened his way toward a DE or other such jujutsu-relevelations.

But that's speculation and not really the point of his character.

Had he been a villain though? Gege probably would've made him stronger, if his Culling Games score in JJK's draft Jujutsu Sousen is anything to go by, which is amusing.

Supplementary reading:

In regards to black flashes: a post where I go over why I think Yuuji and Nanami are especially good at them, and why I think they require conditions that are in opposition to Domain expansions.

Measuring Nanami's critical hit power: where I use a statement to further analyze and evaluate the capabilities of the Ratio Technique.

#jujutsu kaisen #gojo satoru #nanami kento #kento nanami #kento x reader #meta.#jujutsu society

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lostsoulaltair · 1 year ago

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OnS Chapter 136 Analysis - Love vs The Past

Well, a new chapter dropped. I must say that I liked it. We really are heading towards the end of the story and two sides now are on stage.

One fighting for love; the other one fighting to bring back something that is long lost.

Love can often be misunderstood in the manga story; to the point it distorts a lot, but in reality, it takes so many shapes, so many forms along giving birth to ideals.

Each character within the story is moved to do something for "love". But in this role too; the "love" for the "past" exists.

The past is a momentum that will always live in our memories; it is something that will forever be frozen; something that will not change. It's something that is part of us without defining us.

As we've seen today; the chapter starts with Yuu trying to reach the First; but given that the Progenitors want to avoid the destruction of the world much further; they were preventing Yuu from making contact with the First.

Of course, we see how Mika couldn't withstand the raw power of Urd Geales; we saw Shinya and Kureto alive and kicking once again; and finally, the main spot; the reunion between Shikama and Yuu.

Shikama is aware that Yuu has all his memories back ever since he detected an anomaly along the interaction with the corpse of Mikaela. Of course, whenever Shikama knows the soul of his son is alive and kicking, it's up to interpretation. Though, it is clear he knows it exists.

Eventually we see how Shikama is going to bestow Yuu his knowledge and power

But before he could give away his power to Yuu; Shinoa interferred with the possibility.

Leading to our possible future battle between Shinoa and Yuu.

Two ideals being carried away within the story. And yet, at the same time they connect through the main theme. "Love."

And no, it's not "romantic love ". The "love" or 愛 which is not the same as other interpretations of love itself. Just "ai". A love that takes so many shapes.

Let's begin with the real analysis here.

Yuu isn't exactly fighting for humanity nor his friends, nor his family. Given that the "regression" process he made to recall his whole life; he saw how his life originated alongside being the son of the First.

Given that, he is actually aiming for the task of "saving" but given that "love" is playing its role as well. It is the love he has deep down for his father that he is doing everything to bring back what his father cherished in the past. Which is something reflected as well when he was the angel Mikaela. Taking his life so divine punishment wouldn't fall upon his father.

Shikama did love his angels, his followers and he did blame himself for the punishment that fell upon them. But given God's nature; it was normal that the punishment fell upon those angels given that they no longer praised for God.

Though; one thing Shikama and Yuu have in common, is the attachment to the past. It is the very fact that the past for them is something that should have prevailed instead of changing. What do I mean?

To Shikama, the Progenitors he chose, despite being reincarnations of those angels that followed him once; are not his angels.

And this is highly visible once Ashera asks Shikama what he should do back in chapter 117.

To his eyes, Shikama's sired progenitors were just tools given that to him; they are shadows of who they were once, but to those progenitors; even if they have acknowledged that they are reincarnations; they made a choice.

They chose to follow up their own path. And as they've stated back in chapter 114; their last and final task is to protect the world they live in. Meaning avoiding the resurrection of the angel Mikaela.

But now, why do I say Shikama and Yuu have one thing in common?

Yuu is a character that despite living endlessly; along having a constant memory reset until now that he gathered all his memories; he's done one thing to no end. And that is living in the past.

True, there are scars that take its time to heal from the past; some are very difficult nonetheless; he chose to live in such past. When he had a chance to make a choice; that choice was no longer centered in the present; it began to lead further astray to the past.

But then, what's the point of those promises?

Given how everything has turned. Those promises are something that can't be fulfilled. What do I mean?

In order to revive the angels from long ago; sacrifices must be made. But here's another thing that it is being dismissed. What could it be?

The absolute lack of appreciation to life.

Yuu might state he aims to save everyone; even to Ashera who at the end understood that there are limits to life. He loved and embraced he managed to get his sister back; but the cost for that was making her suffer for eternity; nevertheless, the lives both had, those kind of experiences they managed to live had their moments of bliss; bringing back entities that perished long ago, is discarting the moments experienced, the moments lived along the emotions that were carried through a lifetime.

But now, there is an opposite force here. And that force is no other than. "Love."

Many might consider that Shinoa's love is "obssession, madness like Mahiru's love" but no. This is actually wrong.

Back when she stated she would kill Mikaela; there was a massive misconception of this which chapter 133 explained a lot.

Shinoa thought Yuu was being possessed by Mikaela at that time hence her retoric; but given that she saw first hand that Yuu had fooled them; she understood that Yuu was doing everything on his own will. And let's not forget that Yuu might be dumb, but not clueless or ignorant.

Shinoa's form of "love" is not something that came out of nothing; it's something that began taking shape by her own; it began from curiosity, turning into uncertainty, then transforming into caring, along developing feelings for a special someone. Such feelings for her were seen as something hideous or bad; but at the end, she understood they weren't bad, nevertheless, she never admitted them until or rather never accepted them truthfully until chapter 133.

Chapter 133 was a very reflexive chapter given that it gave Shinoa resolution; a resolution as to why it is the reason she was standing still in the very end of the world.

Shinoa's love is not centered to one person, but rather; it is centered to the very core family she gained through her journey which lead to this.

Her battle and her resolution is directed to the very family that has stood with her and has followed her through the long journey she has taken so far. Hence why; she is standing along her squad just like how the vampires did; how they are going to live. Not by a decree of an entity, not by a decree of her sister and Guren nor the vampires; but rather, taking their own path until the very end.

Hence why Shikama states that her love is self centered, which is no different from him. He centered his love to his angels; Shinoa is centering her love to those who exist in the present. Along letting them return to their normality before the world ended; given that the end of the world, the events that took place before they were born, were all spiderwebs made by the First.

This is the first time they are fighting for their own.

What do you think it'll happen next?

Let me know!

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mariacallous · 8 months ago

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In recent years, commercial spyware has been deployed by more actors against a wider range of victims, but the prevailing narrative has still been that the malware is used in targeted attacks against an extremely small number of people. At the same time, though, it has been difficult to check devices for infection, leading individuals to navigate an ad hoc array of academic institutions and NGOs that have been on the front lines of developing forensic techniques to detect mobile spyware. On Tuesday, the mobile device security firm iVerify is publishing findings from a spyware detection feature it launched in May. Of 2,500 device scans that the company's customers elected to submit for inspection, seven revealed infections by the notorious NSO Group malware known as Pegasus.

The company’s Mobile Threat Hunting feature uses a combination of malware signature-based detection, heuristics, and machine learning to look for anomalies in iOS and Android device activity or telltale signs of spyware infection. For paying iVerify customers, the tool regularly checks devices for potential compromise. But the company also offers a free version of the feature for anyone who downloads the iVerify Basics app for $1. These users can walk through steps to generate and send a special diagnostic utility file to iVerify and receive analysis within hours. Free users can use the tool once a month. iVerify's infrastructure is built to be privacy-preserving, but to run the Mobile Threat Hunting feature, users must enter an email address so the company has a way to contact them if a scan turns up spyware—as it did in the seven recent Pegasus discoveries.

“The really fascinating thing is that the people who were targeted were not just journalists and activists, but business leaders, people running commercial enterprises, people in government positions,” says Rocky Cole, chief operating officer of iVerify and a former US National Security Agency analyst. “It looks a lot more like the targeting profile of your average piece of malware or your average APT group than it does the narrative that’s been out there that mercenary spyware is being abused to target activists. It is doing that, absolutely, but this cross section of society was surprising to find.”

Seven out of 2,500 scans may sound like a small group, especially in the somewhat self-selecting customer base of iVerify users, whether paying or free, who want to be monitoring their mobile device security at all, much less checking specifically for spyware. But the fact that the tool has already found a handful of infections at all speaks to how widely the use of spyware has proliferated around the world. Having an easy tool for diagnosing spyware compromises may well expand the picture of just how often such malware is being used.

“NSO Group sells its products exclusively to vetted US & Israel-allied intelligence and law enforcement agencies,” NSO Group spokesperson Gil Lainer told WIRED in a statement. "Our customers use these technologies daily.”

iVerify vice president of research Matthias Frielingsdorf will present the group's Pegasus findings at the Objective by the Sea security conference in Maui, Hawaii on Friday. He says that it took significant investment to develop the detection tool because mobile operating systems like Android, and particularly iOS, are more locked down than traditional desktop operating systems and don't allow monitoring software to have kernel access at the heart of the system. Cole says that the crucial insight was to use telemetry taken from as close to the kernel as possible to tune machine learning models for detection. Some spyware, like Pegasus, also has characteristic traits that make it easier to flag. In the seven detections, Mobile Threat Hunting caught Pegasus using diagnostic data, shutdown logs, and crash logs. But the challenge, Cole says, is in refining mobile monitoring tools to reduce false positives.

Developing the detection capability has already been invaluable, though. Cole says that it helped iVerify identify signs of compromise on the smartphone of Gurpatwant Singh Pannun, a lawyer and Sikh political activist who was the target of an alleged, foiled assassination attempt by an Indian government employee in New York City. The Mobile Threat Hunting feature also flagged suspected nation state activity on the mobile devices of two Harris-Walz campaign officials—a senior member of the campaign and an IT department member—during the presidential race.

“The age of assuming that iPhones and Android phones are safe out of the box is over,” Cole says. “The sorts of capabilities to know if your phone has spyware on it were not widespread. There were technical barriers and it was leaving a lot of people behind. Now you have the ability to know if your phone is infected with commercial spyware. And the rate is much higher than the prevailing narrative.”

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frank-olivier · 8 months ago

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The Future Unscripted: Professor Nolan’s Musings on AI, NHI, and Humanity

Professor Garry Nolan’s conversation offers a profound exploration of the intersections between unconventional phenomena, artificial intelligence (AI), and humanity’s future. His personal anecdotes of unexplained childhood experiences serve as the catalyst for his professional inclination towards the unorthodox, underscoring the importance of considering unconventional data in scientific inquiry. By embracing anomalies, Nolan exemplifies the transformative potential of curiosity-driven research in the pursuit of novel knowledge.

Nolan’s scientific approach is notably inclusive, as evidenced by his exploration of Non-Human Intelligence (NHI), a term that broadens the scope of inquiry beyond traditional notions of extraterrestrial life. The analysis of enigmatic materials, such as Bismuth Magnesium, and the theorized capabilities of NHI, including mastery over anti-gravity and energy, prompt intriguing questions about coexistence. His analogy between human-ant interactions and potential human-NHI relations offers a thought-provoking perspective on mutual existence, shifting the focus from competition to symbiosis.

The future, as envisioned by Nolan, is intimately tied to AI’s development and application. He posits AI as a dual-edged solution: a tool for expediting scientific breakthroughs and resolving global crises, as well as a potential precursor to artificial superintelligence (ASI) that could embody a form of consciousness. This speculative horizon highlights the imperative of careful AI management to ensure equitable global access to education and growth. Nolan’s emphasis on AI-driven objective decision-making processes also presents a compelling case for reforming the current political landscape, which is often mired in tribal histories and scarcity mindsets.

A notable aspect of Nolan’s discourse is his critique of contemporary leadership and power structures, juxtaposed with the conflict resolution methods of bonobo chimps as a paradigm for more harmonious human interactions. This dichotomy underscores the urgent need for innovative, peace-centric approaches to global relations. Ultimately, Nolan’s optimism about humanity’s future, rooted in our capacity for crisis-driven ingenuity and the judicious leveraging of AI, presents a compelling vision of a world where challenges are met with collective brilliance and equality in education and growth becomes a universal reality.

Gary Nolan: Alien & UFO - The Critical Question Nobody's Asking (Gita Wirjawan, Endgame Podcast, December 2024)

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Friday, December 6, 2024

#artificial intelligence #non-human intelligence #unidentified aerial phenomena #uap #future of humanity #scientific approach #unconventional thinking #ai and society #technology and humanity #futurism #speculative science #interdisciplinary studies #innovation and progress #human knowledge and understanding #interview #ai assisted writing #machine art #Youtube

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ausetkmt · 8 months ago

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Daily Newsletter

Our biggest stories, handpicked for you each day.

#A New Phone Scanner That Detects Spyware Has Already Found 7 Pegasus Infections #Phone Scanner #phone viruses #phones with installed viruses #IVerify

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innomaxit · 2 days ago

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The AI Shift: How IT Consulting Is Evolving in 2025

The landscape of IT consulting is undergoing a seismic transformation, and at the heart of this revolution is Artificial Intelligence (AI). As we move through 2025, AI is no longer a buzzword — it’s an essential business tool. Whether you're a consultant, business leader, or tech enthusiast, understanding how AI is reshaping IT consulting can give you a crucial edge.

🌍 The AI Revolution in IT Consulting

AI has embedded itself deeply in the consulting world. What used to take hours of manual analysis, documentation, and planning can now be streamlined using intelligent algorithms and automation tools.

Here’s how:

1. Smarter, Faster Decision-Making

AI-driven analytics tools can now sift through massive datasets in real-time, pulling actionable insights for IT consultants. Whether it's analyzing server logs, optimizing workflows, or helping clients with digital transformation, AI empowers consultants to make faster, smarter, and more confident decisions.

2. Predictive Maintenance

Gone are the days of reactive IT support. AI now enables predictive maintenance — identifying system failures before they occur. IT consultants can use these tools to schedule updates, forecast failures, and reduce downtime for clients. This shift not only saves money but significantly boosts trust and reliability.

3. Hyperautomation

AI tools can automate everything from routine ticket handling and patch management to full-scale cloud deployment. Hyperautomation is allowing consultants to focus more on innovation, strategy, and human-centric problems, rather than spending hours on repetitive backend tasks.

4. Cybersecurity Defense

With the growing complexity of cyber threats, AI is helping IT consultants build stronger, more adaptive defenses. Real-time anomaly detection, AI threat modeling, and automated incident response are becoming the gold standard. Clients now expect their consultants to provide intelligent, always-on security solutions.

5. 24/7 Support with AI Chatbots

AI-powered chatbots and virtual assistants can handle customer support queries 24/7, dramatically improving client satisfaction and reducing human workload. These bots are becoming more conversational, intelligent, and integrated with backend systems to solve problems instantly.

📈 Changing Client Expectations in 2025

With AI revolutionizing the consulting toolkit, clients too are evolving. Their expectations are higher, and their demands more specific.

Here’s what clients are looking for in 2025:

Faster turnaround times — Clients expect problems solved instantly with the help of AI.

Personalized insights — One-size-fits-all solutions are no longer acceptable. Clients want strategies tailored to their unique data and needs.

Transparency — With AI-generated dashboards, clients now expect clear metrics, reports, and ROI visuals.

Scalable solutions — Businesses want systems that grow with them. AI enables flexible, future-ready infrastructure.

Proactive support — Instead of waiting for something to go wrong, clients expect consultants to anticipate problems and solve them in advance.

🧠 The Human + AI Equation

Despite the rise of automation, AI isn’t replacing IT consultants — it’s augmenting them. The consultants of tomorrow are those who combine technical expertise with emotional intelligence, creative problem-solving, and the ability to guide businesses through change.

The winning formula is clear: Human strategy + AI efficiency = client success.

🔮 What’s Next?

The future of IT consulting is not just about technology. It’s about understanding how people, processes, and platforms can come together to drive results. As AI continues to evolve, consultants who adapt quickly will be the ones leading the charge into a more intelligent, efficient, and scalable future.

💡 Interested in learning how Innomax IT Solutions is leading this AI-driven change in consulting?

📩 Contact us: [email protected] 🌐 Visit: www.innomaxsol.com

#AIin2025 #ITConsultingTrends #ArtificialIntelligence #DigitalTransformation #BusinessTechnology #AIinBusiness #FutureOfWork #TechConsulting #InnovationInIT #InnomaxIT

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hollowbutcanlove · 3 days ago

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Emergency 18+

chapter 51

you x iso

a/n: y/n studied at the Faculty of Physics and Mathematics, an interesting fact

The cold of the Protocol hangar seeped into their bones, a stark contrast to the warmth of the Chinese village. The smells of machine oil, ozone, and sterilizing agents hit Echo like a physical slap as she stopped at the threshold of Brimstone's office. Inside, the Polar-X team – Iso, Reyna, Fade – were reporting their failure. Killjoy’s words about the Beta resonance, about Omega playing with fire that could burn reality itself, hung in the air heavier than arctic ice.

Echo didn’t enter. She absorbed this cold dread, this hum of the approaching storm. This wasn't fear. It was steely resolve. Her eyes met Iso’s across the room. Not a word. They both knew: the time for cherry blossoms and tea ceremonies was over. Her place was here. The warrior had returned.

An hour later. The Protocol Council

The air hummed with tension. Holograms projected a world map blazing with Beta anomaly hotspots, schematics of Omega portals, pulsing graphs of the stolen "Radianite-X," and… archival footage of Beta-Earth. Absolute void. A mirrored wall devouring matter.

Echo sat at the table not as a patient, not as a subject of study, but as a full participant. Sage took a place beside her, radiating calm strength. Iso sat opposite, his gaze darting between Echo and the holograms, filled with anxiety and readiness for battle. Brimstone at the head of the table was a granite monolith. Killjoy, pale but focused, managed the projections. Omen sat slightly apart, eyes closed, yet the sensation of a psi-shield hung in the air. Viper fidgeted, her fingers drumming nervously on the table – impatience and a thirst for action radiated from her in waves.

“Begin,” - Brimstone’s voice cut like an axe through ice. - “Killjoy. Polar-X analysis summary.”

The technogenius flinched, but her voice was crisp:

“Confirmed. Omega’s attack wasn’t just resource theft. The goal is resonance. They used the Radianite-X theft under extreme load and their portal activation to simulate Beta-Earth’s signature.” - She magnified a graph where two curves – Beta’s signature and data from the stolen container – nearly merged at their peak.

“Radianite-X in this state isn’t fuel. It’s a catalyst. A key to… something greater. To opening a door? To creating a weapon that harnesses the Void’s very essence? They’re seeking a way to tame the apocalypse.”

Silence. Even Viper stopped drumming.

“Their next step?” Brimstone asked.

“Extrapolation points to an attempt to stabilize the resonance,” -Killjoy continued. - “For that, they need two things: more highly purified Radianite-X… and an amplified ‘key’ signal.” - Her gaze flickered involuntarily towards Echo. - “The Rho-7 in her blood… it’s not just unique. It resonates with Beta energy. Omega’s archives… their torture… it wasn’t just sadism. They were studying this link under stress. Seeking a way to amplify it, control it.”

Echo didn’t flinch. Her eyes were fixed on the hologram of Beta-Earth. On that void that had once been her home. Not fear, but a hungry, chilling curiosity burned in her gaze. She saw not just a threat. She saw a mystery. The mystery of her origin, her fall. The mystery of the power that destroyed her world.

“So their target is Echo,” - Viper stated, her voice cutting the air. - “And that damned radianite. We sit here while they build Armageddon! I propose a preemptive strike. A full-scale op to destroy their primary research centers!” - She jabbed a finger at the archival torture footage flickering on one screen. - “And we use this! Their own data! Find vulnerabilities in their defenses, their psi-training! Echo’s pain – our weapon against them!”

“No!”

Iso’s word cracked like a gunshot. He surged to his feet, fists clenched, eyes blazing with fury directed at Viper.

“Her pain is not your tool! Her body is not a key for you to wield! We are not Omega! We won’t dig through those… those archives of hell!”

“Idiotic sentiment!” - Viper shot back. - “They don’t play by the rules! They use EVERYTHING! We must respond in kind, or we’ll all be consumed by that Void!”

The argument escalated. Brimstone watched, his face impassive. Sage quietly placed a hand on Echo’s shoulder, feeling the tension there – not from fear, but concentration. Echo looked at the Beta hologram, at the archival footage of her pain, at the arguing Iso and Viper. An internal calculation was happening in her eyes.

Assessment. Acceptance.

She raised her hand. Not high, but the movement was so sharp, so unexpectedly commanding, that the argument died instantly. All eyes snapped to her. She stood, her figure, so recently fragile, now radiating unshakeable strength.

“To break their key,” - her words fell like ice shards, - “we must understand it better than they do. We must understand her. Beta.” -She pointed at the hologram. - “Give me access. To everything. To Omega’s data on Beta-Earth. To their research on the Rho-7 link. To the archives…” - She spoke the last word without a tremor. - “Not to dig through pain. To find the flaw in their understanding. To find my way to interact with what’s left of me.” - Her gaze, full of cold, analytical fire, challenged Brimstone. - “I am the only one who can do this. Because I am from there. And part of it is in me.”

A thick, heavy silence fell. Even Viper was speechless. Iso looked at Echo with a mix of horror, pride, and profound pain. He saw her volunteering to step back into hell, no longer a victim, but a strategist.

Brimstone rose slowly. His measured gaze swept the faces: furious Viper, protective Iso, impassive Omen, focused Killjoy, and finally – Echo. Warrior. Scientist. Key.

“Granted,” - he pronounced, and finality rang in his voice. - “Operation Polar-X showed us: old methods are insufficient. The threat has transcended conventional warfare.” - He looked at Echo. - “Your proposal has merit. But it is dangerous. Extremely so.”

His gaze swept the table:

“Viper’s proposal for an immediate large-scale strike is denied. Too risky. Omega expects provocation. I authorize the formation of a special operations-research task force. Codename: IRON BLOSSOM. Echo is the core. Full access to all Omega-Beta level data, including the archives. Her task: analysis, vulnerability search, understanding the nature of the Rho-7-Beta link. Potential field participation only with her consent and absolute necessity. Killjoy – techno-analysis, data security, lab infrastructure, Radianite-X analysis. Omen – psi-support, shielding for Echo during hazardous data work, intuitive threat assessment. Sage – coordination, group security, comms, logistics. Iso – operational support and protection for the group outside the lab. Security on excursions, liaison with main forces.” - His gaze met Iso’s. - “Conflict of interest. Risk to Echo. You are a field soldier. Your strength is there.”

Iso nodded, jaw clenched. It hurt, but it was logical.

previous chapter - 50

next chapter - 52

to the beginning

#valorant #valorant x reader #valorant x you #iso valorant #iso x reader #valorant iso x reader #iso x you

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jcmarchi · 2 months ago

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Top 10 AI Tools for Embedded Analytics and Reporting (May 2025)

New Post has been published on https://thedigitalinsider.com/top-10-ai-tools-for-embedded-analytics-and-reporting-may-2025/

Top 10 AI Tools for Embedded Analytics and Reporting (May 2025)

Embedded analytics refers to integrating interactive dashboards, reports, and AI-driven data insights directly into applications or workflows. This approach lets users access analytics in context without switching to a separate BI tool. It’s a rapidly growing market – valued around $20 billion in 2024 and projected to reach $75 billion by 2032 (18% CAGR).

Organizations are embracing embedded analytics to empower end-users with real-time information. These trends are fueled by demand for self-service data access and AI features like natural language queries and automated insights, which make analytics more accessible.

Below we review top tools that provide AI-powered embedded analytics and reporting. Each tool includes an overview, key pros and cons, and a breakdown of pricing tiers.

AI Tools for Embedded Analytics and Reporting (Comparison Table)

AI Tool Best For Price Features Explo Turnkey, white-label SaaS dashboards Free internal · embed from $795/mo No-code builder, Explo AI NLQ, SOC 2/HIPAA ThoughtSpot Google-style NL search for data in apps Dev trial free · usage-based quote SpotIQ AI insights, search & Liveboards embed Tableau Embedded Pixel-perfect visuals & broad connectors $12–70/user/mo Pulse AI summaries, drag-drop viz, JS API Power BI Embedded Azure-centric, cost-efficient scaling A1 capacity from ~$735/mo NL Q&A, AutoML visuals, REST/JS SDK Looker Governed metrics & Google Cloud synergy Custom (≈$120k+/yr) LookML model, secure embed SDK, BigQuery native Sisense OEMs needing deep white-label control Starter ≈$10k/yr · Cloud ≈$21k/yr ElastiCube in-chip, NLQ, full REST/JS APIs Qlik Associative, real-time data exploration $200–2,750/mo (capacity-based) Associative engine, Insight Advisor AI, Nebula.js Domo Everywhere Cloud BI with built-in ETL & sharing From ~$3k/mo (quote) 500+ connectors, alerts, credit-based scaling Yellowfin BI Data storytelling & flexible OEM pricing Custom (≈$15k+/yr) Stories, Signals AI alerts, multi-tenant Mode Analytics SQL/Python notebooks to embedded reports Free · Pro ≈$6k/yr Notebooks, API embed, Visual Explorer

(Source: Explo)

Explo is an embedded analytics platform designed for product and engineering teams to quickly add customer-facing dashboards and reports to their apps. It offers a no-code interface for creating interactive charts and supports white-labeled embedding, so the analytics blend into your product’s UI.

Explo focuses on self-service: end-users can explore data and even build ad hoc reports without needing developer intervention. A standout feature is Explo AI, a generative AI capability that lets users ask free-form questions and get back relevant charts automatically.

This makes data exploration as easy as typing a query in natural language. Explo integrates with many databases and is built to scale from startup use cases to enterprise deployments (it’s SOC II, GDPR, and HIPAA compliant for security).

Pros and Cons

Drag-and-drop dashboards—embed in minutes

Generative AI (Explo AI) for NLQ insights

Full white-label + SOC 2 / HIPAA compliance

Young platform; smaller community

Costs rise with large end-user counts

Cloud-only; no on-prem deployment

Pricing: (Monthly subscriptions – USD)

Launch – Free: Internal BI use only; unlimited internal users/dashboards.

Growth – from $795/month: For embedding in apps; includes 3 embedded dashboards, 25 customer accounts.

Pro – from $2,195/month: Advanced embedding; unlimited dashboards, full white-label, scales with usage.

Enterprise – Custom: Custom pricing for large scale deployments; includes priority support, SSO, custom features.

Visit Explo →

ThoughtSpot is an AI-driven analytics platform renowned for its search-based interface. With ThoughtSpot’s embedded analytics, users can type natural language queries (or use voice) to explore data and instantly get visual answers.

This makes analytics accessible to non-technical users – essentially a Google-like experience for your business data. ThoughtSpot’s in-memory engine handles large data volumes, and its AI engine (SpotIQ) automatically finds insights and anomalies.

For embedding, ThoughtSpot provides low-code components and robust REST APIs/SDKs to integrate interactive Liveboards (dashboards) or even just the search bar into applications. It’s popular for customer-facing analytics in apps where end-users need ad-hoc querying ability.

Businesses in retail, finance, and healthcare use ThoughtSpot to let frontline employees and customers ask data questions on the fly. The platform emphasizes ease-of-use and fast deployment, though it also offers enterprise features like row-level security and scalability across cloud data warehouses.

Pros and Cons

Google-style NL search for data

SpotIQ AI auto-surfaces trends

Embeds dashboards, charts, or just the search bar

Enterprise-grade pricing for SMBs

Limited advanced data modeling

Setup needs schema indexing expertise

Pricing: (Tiered, with consumption-based licensing – USD)

Essentials – $1,250/month (billed annually): For larger deployments; increased data capacity and features.

ThoughtSpot Pro: Custom quote. Full embedding capabilities for customer-facing apps (up to ~500 million data rows).

ThoughtSpot Enterprise: Custom quote. Unlimited data scale and enterprise SLA. Includes multi-tenant support, advanced security, etc.

Visit ThoughtSpot →

Tableau (part of Salesforce) is a leading BI platform known for its powerful visualization and dashboarding capabilities. Tableau Embedded Analytics allows organizations to integrate Tableau’s interactive charts and reports into their own applications or websites.

Developers can embed Tableau dashboards via iFrames or using the JavaScript API, enabling rich data visuals and filtering in-app. Tableau’s strength lies in its breadth of out-of-the-box visuals, drag-and-drop ease for creating dashboards, and a large user community.

It also has introduced AI features – for example, in 2024 Salesforce announced Tableau Pulse, which uses generative AI to deliver automated insights and natural language summaries to users. This augments embedded dashboards with proactive explanations.

Tableau works with a wide range of data sources and offers live or in-memory data connectivity, ensuring that embedded content can display up-to-date info. It’s well-suited for both internal embedded use (e.g. within an enterprise portal) and external customer-facing analytics, though licensing cost and infrastructure must be planned accordingly.

Pros and Cons

Market-leading visual library

New “Pulse” AI summaries & NLQ

Broad data connectors + massive community

License cost balloons at scale

Requires Tableau Server/Cloud infrastructure

Styling customization via JS API only

Pricing: (Subscription per user, with role-based tiers – USD)

Creator – $70 per user/month: Full authoring license (data prep, dashboard creation). Needed for developers building embedded dashboards.

Explorer – $35 per user/month: For users who explore and edit limited content. Suitable for internal power users interacting with embedded reports.

Viewer – $12 per user/month: Read-only access to view dashboards. For end viewers of embedded analytics.

Visit Tableau →

Microsoft Power BI is a widely-used BI suite, and Power BI Embedded refers to the Azure service and APIs that let you embed Power BI visuals into custom applications. This is attractive for developers building customer-facing analytics, as it combines Power BI’s robust features (interactive reports, AI visuals, natural language Q&A, etc.) with flexible embedding options.

You can embed full reports or individual tiles, control them via REST API, and apply row-level security for multi-tenant scenarios. Power BI’s strengths include tight integration with the Microsoft ecosystem (Azure, Office 365), strong data modeling (via Power BI Desktop), and growing AI capabilities (e.g. the Q&A visual that allows users to ask questions in plain English).

Pros and Cons

Rich BI + AI visuals (NL Q&A, AutoML)

Azure capacity pricing scales to any user base

Deep Microsoft ecosystem integration

Initial setup can be complex (capacities, RLS)

Devs need Power BI Pro licenses

Some portal features absent in embeds

Pricing: (Azure capacity-based or per-user – USD)

Power BI Pro – $14/user/month: Enables creating and sharing reports. Required for developers and any internal users of embedded content.

Power BI Premium Per User – $24/user/month: Enhanced features (AI, larger datasets) on a per-user basis. Useful if a small number of users need premium capabilities instead of a full capacity.

Power BI Embedded (A SKUs) – From ~$735/month for A1 capacity (3 GB RAM, 1 v-core). Scales up to ~$23,500/month for A6 (100 GB, 32 cores) for high-end needs. Billed hourly via Azure, with scale-out options.

Visit Power BI →

Looker is a modern analytics platform now part of Google Cloud. It is known for its unique data modeling layer, LookML, which lets data teams define business metrics and logic centrally.

For embedded analytics, Looker provides a robust solution: you can embed interactive dashboards or exploratory data tables in applications, leveraging the same Looker backend. One of Looker’s core strengths is consistency – because of LookML, all users (and embedded views) use trusted data definitions, avoiding mismatched metrics.

Looker also excels at integrations: it connects natively to cloud databases (BigQuery, Snowflake, etc.), and because it’s in the Google ecosystem, it integrates with Google Cloud services (permissions, AI/ML via BigQuery, etc.).

Pros and Cons

LookML enforces single source of truth

Secure embed SDK + full theming

Tight BigQuery & Google AI integration

Premium six-figure pricing common

Steep LookML learning curve

Visuals less flashy than Tableau/Power BI

Pricing: (Custom quotes via sales; example figures)

Visit Looker →

Sisense is a full-stack BI and analytics platform with a strong focus on embedded analytics use cases. It enables companies to infuse analytics into their products via flexible APIs or web components, and even allows building custom analytic apps.

Sisense is known for its ElastiCube in-chip memory technology, which can mash up data from multiple sources and deliver fast performance for dashboards. In recent years, Sisense has incorporated AI features (e.g. NLQ, automated insights) to stay competitive.

A key advantage of Sisense is its ability to be fully white-labeled and its OEM-friendly licensing, which is why many SaaS providers choose it to power their in-app analytics. It offers both cloud and on-premises deployment options, catering to different security requirements.

Sisense also provides a range of customization options: you can embed entire dashboards or individual widgets, and use their JavaScript library to deeply customize look and feel. It’s suited for organizations that need an end-to-end solution – from data preparation to visualization – specifically tailored for embedding in external applications.

Pros and Cons

ElastiCube fuses data fast in-memory

White-label OEM-friendly APIs

AI alerts & NLQ for end-users

UI learning curve for new users

Quote-based pricing can be steep

Advanced setup often needs dev resources

Pricing: (Annual license, quote-based – USD)

Starter (Self-Hosted) – Starts around $10,000/year for a small deployment (few users, basic features). This would typically be an on-prem license for internal BI or limited OEM use.

Cloud (SaaS) Starter – ~$21,000/year for ~5 users on Sisense Cloud (cloud hosting carries ~2× premium over self-host).

Growth/Enterprise OEM – Costs scale significantly with usage; mid-range deployments often range $50K-$100K+ per year. Large enterprise deals can reach several hundred thousand or more if there are very high numbers of end-users.

Visit Sisense →

Qlik is a long-time leader in BI, offering Qlik Sense as its modern analytics platform. Qlik’s embedded analytics capabilities allow you to integrate its associative data engine and rich visuals into other applications.

Qlik’s differentiator is its Associative Engine: users can freely explore data associations (making selections across any fields) and the engine instantly updates all charts to reflect those selections, revealing hidden insights.

In an embedded scenario, this means end-users can get powerful interactive exploration, not just static filtered views. Qlik provides APIs (Capability API, Nebula.js library, etc.) to embed charts or even build fully custom analytics experiences on top of its engine. It also supports standard embed via iframes or mashups.

Qlik has incorporated AI as well – the Insight Advisor can generate insights or chart suggestions automatically. For developers, Qlik’s platform is quite robust: you can script data transformations in its load script, use its security rules for multi-tenant setups, and even embed Qlik into mobile apps.

Pros and Cons

Associative engine enables free exploration

Fast in-memory performance for big data

Robust APIs + Insight Advisor AI

Unique scripting → higher learning curve

Enterprise-level pricing

UI can feel dated without theming

Pricing: (USD)

Starter – $200 / month (billed annually): Includes 10 users + 25 GB “data for analysis.” No extra data add-ons available.

Standard – $825 / month: Starts with 25 GB; buy more capacity in 25 GB blocks. Unlimited user access.

Premium – $2,750 / month: Starts with 50 GB, adds AI/ML, public/anonymous access, larger app sizes (10 GB).

Enterprise – Custom quote: Begins at 250 GB; supports larger app sizes (up to 40 GB), multi-region tenants, expanded AI/automation quotas.

Visit Qlik →

Domo is a cloud-first business intelligence platform, and Domo Everywhere is its embedded analytics solution aimed at sharing Domo’s dashboards outside the core Domo environment. With Domo Everywhere, companies can distribute interactive dashboards to customers or partners via embed codes or public links, while still managing everything from the central Domo instance.

Domo is known for its end-to-end capabilities in the cloud – from data integration (500+ connectors, built-in ETL called Magic ETL) to data visualization and even a built-in data science layer.

For embedding, Domo emphasizes ease of use: non-technical users can create dashboards in Domo’s drag-and-drop interface, then simply embed them with minimal coding. It also offers robust governance so you can control what external viewers see.

Pros and Cons

End-to-end cloud BI with 500+ connectors

Simple drag-and-embed workflow

Real-time alerts & collaboration tools

Credit-based pricing tricky to budget

Cloud-only; no on-prem option

Deeper custom UI needs dev work

Pricing: (Subscription, contact Domo for quote – USD)

Basic Embedded Package – roughly $3,000 per month for a limited-user, limited-data scenario. This might include a handful of dashboards and a moderate number of external viewers.

Mid-size Deployment – approximately $20k–$50k per year for mid-sized businesses. This would cover more users and data; e.g., a few hundred external users with regular usage.

Enterprise – $100k+/year for large-scale deployments. Enterprises with thousands of external users or very high data volumes can expect costs in six figures. (Domo often structures enterprise deals as unlimited-user but metered by data/query credits.)

Visit Domo →

Yellowfin is a BI platform that has carved a niche in embedded analytics and data storytelling. It offers a cohesive solution with modules for dashboards, data discovery, automated signals (alerts on changes), and even a unique Story feature for narrative reporting.

For embedding, Yellowfin Embedded Analytics provides OEM partners a flexible licensing model and technical capabilities to integrate Yellowfin content into their applications. Yellowfin’s strength lies in its balanced focus: it’s powerful enough for enterprise BI but also streamlined for embedding, with features like multi-tenant support and white-labeling.

It also has NLP query (natural language querying) and AI-driven insights, aligning with modern trends. A notable feature is Yellowfin’s data storytelling – you can create slide-show style narratives with charts and text, which can be embedded to give end-users contextual analysis, not just raw dashboards.

Yellowfin is often praised for its collaborative features (annotations, discussion threads on charts) which can be beneficial in an embedded context where you want users to engage with the analytics.

Pros and Cons

Built-in Stories & Signals for narratives

OEM pricing adaptable (fixed or revenue-share)

Multi-tenant + full white-label support

Lower brand recognition vs. “big three”

Some UI elements feel legacy

Advanced features require training

Pricing: (Custom – Yellowfin offers flexible models)

Visit Yellowfin →

Mode is a platform geared towards advanced analysts and data scientists, combining BI with notebooks. It’s now part of ThoughtSpot (acquired in 2023) but still offered as a standalone solution.

Mode’s appeal in an embedded context is its flexibility: analysts can use SQL, Python, and R in one environment to craft analyses, then publish interactive visualizations or dashboards that can be embedded into web apps. This means if your application’s analytics require heavy custom analysis or statistical work, Mode is well-suited.

It has a modern HTML5 dashboarding system and recently introduced “Visual Explorer” for drag-and-drop charting, plus AI assist features for query suggestions. Companies often use Mode to build rich, bespoke analytics for their customers – for example, a software company might use Mode to develop a complex report, and then embed that report in their product for each customer with the data filtered appropriately.

Mode supports white-label embedding, and you can control it via their API (to provision users, run queries, etc.). It’s popular with data teams due to the seamless workflow from coding to sharing insights.

Pros and Cons

Unified SQL, Python, R notebooks → dashboards

Strong API for automated embedding

Generous free tier for prototyping

Analyst skills (SQL/Python) required

Fewer NLQ/AI features for end-users

Visualization options less extensive than Tableau

Pricing: (USD)

Studio (Free) – $0 forever for up to 3 users. This includes core SQL/Python/R analytics, private data connections, 10MB query limit, etc. Good for initial development and testing of embedded ideas.

Pro (Business) – Starts around ~$6,000/year (estimated). Mode doesn’t list fixed prices, but third-party sources indicate pro plans in the mid four-figure range annually for small teams.

Enterprise – Custom pricing, typically five-figure annually up to ~$50k for large orgs. Includes all Pro features plus enterprise security (SSO, advanced permissions), custom compute for heavy workloads, and premium support.

Visit Mode →

How to Choose the Right Embedded Analytics Tool

Selecting an embedded analytics solution requires balancing your company’s needs with each tool’s strengths. Start with your use case and audience: Consider who will be using the analytics and their technical level. If you’re embedding dashboards for non-technical business users or customers, a tool with an easy UI could be important. Conversely, if your application demands highly custom analyses or you have a strong data science team, a more flexible code-first tool might be better.

Also evaluate whether you need a fully managed solution (more plug-and-play, e.g. Explo or Domo) or are willing to manage more infrastructure for a potentially more powerful platform (e.g. self-hosting Qlik or Sisense for complete control). The size of your company (and engineering resources) will influence this trade-off – startups often lean towards turnkey cloud services, while larger enterprises might integrate a platform into their existing tech stack.

Integration and scalability are critical factors. Look at how well the tool will integrate with your current systems and future architecture. Finally, weigh pricing and total cost of ownership against your budget and revenue model. Embedded analytics tools vary from per-user pricing to usage-based and fixed OEM licenses. Map out a rough projection of costs for 1 year and 3 years as your user count grows.

FAQs (Embedded Analytics and Reporting)

1. What are the main differences between Tableau and Power BI?

Tableau focuses on advanced visual design, cross-platform deployment (on-prem or any cloud), and a large viz library, but it costs more per user. Power BI is cheaper, tightly integrated with Microsoft 365/Azure, and great for Excel users, though some features require an Azure capacity and Windows-centric stack.

2. How does Sisense handle large datasets compared to other tools?

Sisense’s proprietary ElastiCube “in-chip” engine compresses data in memory, letting a single node serve millions of rows while maintaining fast query response; benchmarks show 500 GB cubes on 128 GB RAM. Competing BI tools often rely on external warehouses or slower in-memory engines for similar workloads.

3. Which embedded analytics tool offers the best customization options?

Sisense and Qlik are stand-outs: both expose full REST/JavaScript APIs, support deep white-labeling, and let dev teams build bespoke visual components or mashups—ideal when you need analytics to look and feel 100 % native in your app.

4. Are there any free alternatives to Tableau and Sisense?

Yes—open-source BI platforms like Apache Superset, Metabase, Redash, and Google’s free Looker Studio deliver dashboarding and basic embedded options at zero cost (self-hosted or SaaS tiers), making them good entry-level substitutes for smaller teams or tight budgets.

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rise2research · 4 months ago

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The Automation Revolution: How Embedded Analytics is Leading the Way

Embedded analytics tools have emerged as game-changers, seamlessly integrating data-driven insights into business applications and enabling automation across various industries. By providing real-time analytics within existing workflows, these tools empower organizations to make informed decisions without switching between multiple platforms.

The Role of Embedded Analytics in Automation

Embedded analytics refers to the integration of analytical capabilities directly into business applications, eliminating the need for separate business intelligence (BI) tools. This integration enhances automation by:

Reducing Manual Data Analysis: Automated dashboards and real-time reporting eliminate the need for manual data extraction and processing.

Improving Decision-Making: AI-powered analytics provide predictive insights, helping businesses anticipate trends and make proactive decisions.

Enhancing Operational Efficiency: Automated alerts and anomaly detection streamline workflow management, reducing bottlenecks and inefficiencies.

Increasing User Accessibility: Non-technical users can easily access and interpret data within familiar applications, enabling data-driven culture across organizations.

Industry-Wide Impact of Embedded Analytics

1. Manufacturing: Predictive Maintenance & Process Optimization

By analyzing real-time sensor data, predictive maintenance reduces downtime, enhances production efficiency, and minimizes repair costs.

2. Healthcare: Enhancing Patient Outcomes & Resource Management

Healthcare providers use embedded analytics to track patient records, optimize treatment plans, and manage hospital resources effectively.

3. Retail: Personalized Customer Experiences & Inventory Optimization

Retailers integrate embedded analytics into e-commerce platforms to analyze customer preferences, optimize pricing, and manage inventory.

4. Finance: Fraud Detection & Risk Management

Financial institutions use embedded analytics to detect fraudulent activities, assess credit risks, and automate compliance monitoring.

5. Logistics: Supply Chain Optimization & Route Planning

Supply chain managers use embedded analytics to track shipments, optimize delivery routes, and manage inventory levels.

6. Education: Student Performance Analysis & Learning Personalization

Educational institutions utilize embedded analytics to track student performance, identify learning gaps, and personalize educational experiences.

The Future of Embedded Analytics in Automation

As AI and machine learning continue to evolve, embedded analytics will play an even greater role in automation. Future advancements may include:

Self-Service BI: Empowering users with more intuitive, AI-driven analytics tools that require minimal technical expertise.

Hyperautomation: Combining embedded analytics with robotic process automation (RPA) for end-to-end business process automation.

Advanced Predictive & Prescriptive Analytics: Leveraging AI for more accurate forecasting and decision-making support.

Greater Integration with IoT & Edge Computing: Enhancing real-time analytics capabilities for industries reliant on IoT sensors and connected devices.

Conclusion

By integrating analytics within existing workflows, businesses can improve efficiency, reduce operational costs, and enhance customer experiences. As technology continues to advance, the synergy between embedded analytics and automation will drive innovation and reshape the future of various industries.

To know more: data collection and insights

data analytics services

#data analytics #market research #data collection #surveyprogramming #datainsights

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dr-octavio-kalev · 10 months ago

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Subject Classification: M-08-035

Damage Type: Null ⚪️

Danger Level: HELFER 🔵

Anomaly Type: Mineral

Discovery Classification: Expedition (08)

Department: MOTHRA Storage Facilities

Lead Researcher: Dr. Ironclad

Identification: Titanic Metal

Containment: ANM-035 bars are stored in different warehouses of the MOTHRA Institution. The "containment" must have a temperature regulator where the ambient temperature must remain at 22°C, never exceeding 120°C. If this happens, the emergency alarm should be activated immediately.

Titanic metal bars can be used to build secure cells for complex anomalies, materials or weapons, protective gear, and other related uses advantageous to MOTHRA.

Description: As of the writing of this file, ANM-035 is a set of 800 bars of a type of metal nicknamed "Titanic Metal." While its original color was white, it was observed that the metal bars could change to blue over time, a process similar to copper, which also makes the ore even more resistant.

The ANM-035 bars are extremely durable, being 60 times stronger than common metal, 49 times more resistant than tungsten, and 20 times more resistant than graphene. Upon further analysis, a significant mix of graphene atoms and hexagonal boron nitride was detected in the metal.

Regarding its durability, some tests were conducted:

Over 100 strikes with a machete were made against the bar, resulting in no scratches.

Some shots were fired at the bar, but the test was quickly canceled when it was noticed that the bullets ricocheted back.

ANM-014 attempted to break the bar but, after several strikes, complained that his "hand hurt." (ANM-014 typically manages to break titanium bars in half.)

ANM-035 was dropped from an airplane at an altitude of 12,000 meters, and the bar suffered no damage, though the surrounding environment did sustain significant damage.

ANM-032 was set to try piercing the bar with its beak. The instance failed to do so.

Multiple RPG shots followed by explosions were tested, but the bar remained unaffected.

A tank rolled over the bar several times, but nothing happened to it.

Finally, it was discovered that if ANM-035 is exposed to a temperature above 70°C, the ore tends to melt into an extremely corrosive and radioactive liquid.

ANM-035 can be used to create more powerful tools, utensils, and weapons (only melee weapons). A shield made from ANM-035 was built and is completely bulletproof and resistant to various other blows or attacks. Additionally, armor was constructed, and together with the shield, they are capable of repelling numerous attacks, including pecks from ANM-032 or attacks from humanoid ANMs.

The bars typically measure 55cm in length and 25cm in height, weighing approximately 500kg. All were found in a military bunker dating back to World War II in the USA.

#originalscp #scp #scp foundation #writing #mothra institution #anomaly

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xaltius · 5 months ago

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How Artificial Intelligence is Reshaping Cybersecurity Jobs?

Artificial intelligence (AI) has permeated nearly every aspect of our lives, and cybersecurity is no exception. While the technology offers immense potential for bolstering defenses, it also raises questions about its impact on cybersecurity jobs. Will AI replace human analysts, or will it simply augment their capabilities? Let's delve into the complex relationship between AI and cybersecurity employment.

AI: Benefits and Challenges

AI's ability to analyze vast datasets, identify patterns, and automate tasks makes it a powerful tool for cybersecurity. It can:

Enhance Threat Detection: AI algorithms can detect anomalies and suspicious activities that human analysts might miss, leading to faster and more accurate threat detection.

Automate Incident Response: AI can automate responses to routine security incidents, freeing up human analysts to focus on complex threats.

Improve Vulnerability Management: AI can identify and prioritize vulnerabilities, enabling organizations to patch systems more efficiently.

Strengthen Behavioral Analytics: AI can establish baselines of normal user and system behavior, flagging deviations that may indicate malicious activity.

However, AI also presents challenges:

AI-Powered Attacks: Cybercriminals are increasingly using AI to develop sophisticated attacks, such as AI-driven phishing and malware.

Bias and Ethical Concerns: AI algorithms can inherit biases from their training data, potentially leading to discriminatory or unfair security outcomes.

The Black Box Problem: The complexity of some AI models can make it difficult to understand how they arrive at their decisions, hindering troubleshooting and auditing.

The Evolving Role of Cybersecurity Professionals:

Instead of replacing human analysts, AI is more likely to transform their roles. Cybersecurity professionals will need to:

Become AI-Savvy: They'll need to understand how AI algorithms work, how to use AI-powered security tools, and how to defend against AI-driven attacks.

Focus on Complex Threats: AI will handle routine tasks, allowing humans to focus on sophisticated and novel attacks that require critical thinking and problem-solving skills.

Manage AI Models: They'll be responsible for training, validating, and maintaining AI models used in security systems.

Address Ethical Considerations: They'll need to understand and address the ethical implications of AI in cybersecurity.

Improve Security Automation: They will need to know how to implement and manage the security automations that AI makes possible.

The Skills of the Future:

To thrive in the age of AI, cybersecurity professionals will need to develop new skills, including:

Machine Learning Fundamentals: Understanding how AI/ML algorithms work is essential.

Data Analysis and Visualization: Interpreting data from AI security tools is crucial.

Programming (Python): For working with AI/ML libraries and automation tools.

Critical Thinking and Problem-Solving: Essential for handling complex security incidents.

Adaptability: The cybersecurity landscape is constantly changing, requiring continuous learning.

Xaltius Academy's Cybersecurity Course: Preparing for the Future:

Xaltius Academy's Cybersecurity Course is designed to equip you with the fundamental knowledge and practical skills needed to navigate the evolving cybersecurity landscape. The course is designed to keep up with the changes in the cyber security field. It provides a strong foundation and prepares you for a successful cybersecurity career in the age of AI.

Conclusion:

AI is not going to eliminate cybersecurity jobs, but it will fundamentally change them. The key is to embrace AI as a powerful tool and adapt to the changing landscape by acquiring new skills and knowledge. Cybersecurity professionals who can leverage AI to enhance their capabilities will be highly valued in the years to come.

#cybersecurity #ai #technology

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tech4bizsolutions · 5 months ago

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MedAI by Tech4Biz Solutions: Pioneering Next-Gen Medical Technologies

The healthcare industry is undergoing a seismic shift as advanced technologies continue to transform the way care is delivered. MedAI by Tech4Biz Solutions is at the forefront of this revolution, leveraging artificial intelligence and cutting-edge tools to develop next-generation medical solutions. By enhancing diagnostics, personalizing patient care, and streamlining operations, MedAI is empowering healthcare providers to deliver better outcomes.

1. AI-Driven Medical Insights

MedAI harnesses the power of artificial intelligence to analyze complex medical data and generate actionable insights. Its advanced algorithms can detect anomalies, predict disease progression, and recommend treatment pathways with unprecedented accuracy.

Case Study: A large medical center integrated MedAI’s diagnostic platform, leading to:

Faster identification of rare conditions.

A 30% reduction in misdiagnoses.

Enhanced clinician confidence in treatment decisions.

These capabilities underscore MedAI’s role in advancing clinical decision-making.

2. Personalized Patient Care

Personalization is key to modern healthcare, and MedAI’s data-driven approach ensures treatment plans are tailored to individual needs. By analyzing patient histories, lifestyle factors, and genetic data, MedAI offers more targeted and effective interventions.

Example: A chronic disease management clinic used MedAI to create personalized care plans, resulting in:

Improved medication adherence.

Decreased hospital readmission rates.

Greater patient satisfaction and engagement.

MedAI’s solutions allow providers to offer more precise, patient-centered care.

3. Enhanced Operational Efficiency

MedAI goes beyond clinical improvements by optimizing healthcare operations. Its automation tools reduce administrative burdens, freeing healthcare professionals to focus on patient care.

Insight: A regional hospital implemented MedAI’s workflow automation system, achieving:

A 40% reduction in administrative errors.

Faster patient registration and billing processes.

Streamlined appointment scheduling.

These improvements enhance overall operational efficiency and patient experiences.

4. Advanced Predictive Analytics

Predictive analytics play a vital role in preventive care. MedAI’s algorithms identify patients at high risk of developing chronic conditions, enabling early interventions.

Case Study: A primary care network used MedAI’s predictive models to monitor high-risk patients, leading to:

Early lifestyle adjustments and medical interventions.

A 25% drop in emergency room visits.

Higher enrollment in wellness programs.

By shifting to proactive care, MedAI helps reduce healthcare costs and improve long-term outcomes.

5. Revolutionizing Telemedicine

The rise of telemedicine has been accelerated by MedAI’s AI-powered virtual care solutions. These tools enhance remote consultations by providing real-time patient insights and symptom analysis.

Example: A telehealth provider adopted MedAI’s platform and reported:

Improved diagnostic accuracy during virtual visits.

Reduced wait times for consultations.

Increased access to care for rural and underserved populations.

MedAI’s telemedicine tools ensure equitable, high-quality virtual care for all.

6. Streamlining Drug Development

MedAI accelerates the drug discovery process by analyzing clinical trial data and simulating drug interactions. Its AI models help identify promising compounds faster and improve trial success rates.

Case Study: A pharmaceutical company partnered with MedAI to enhance its drug development process, achieving:

Faster identification of viable drug candidates.

Shorter trial durations.

Reduced costs associated with trial phases.

These innovations are driving faster development of life-saving medications.

7. Natural Language Processing for Clinical Data

MedAI’s natural language processing (NLP) capabilities extract insights from unstructured medical data, such as physician notes and discharge summaries. This allows for faster retrieval of vital patient information.

Insight: A healthcare system implemented MedAI’s NLP engine and experienced:

Improved documentation accuracy.

Quicker clinical decision-making.

Enhanced risk assessment for high-priority cases.

By automating data extraction, MedAI reduces clinician workloads and improves care quality.

8. Robust Data Security and Compliance

Data security is paramount in healthcare. MedAI employs advanced encryption, threat monitoring, and regulatory compliance measures to safeguard patient information.

Example: A hospital using MedAI’s security solutions reported:

Early detection of potential data breaches.

Full compliance with healthcare privacy regulations.

Increased patient trust and confidence in data protection.

MedAI ensures that sensitive medical data remains secure in an evolving digital landscape.

Conclusion

MedAI by Tech4Biz Solutions is redefining healthcare through its pioneering medical technologies. By delivering AI-driven insights, personalized care, operational efficiency, and robust security, MedAI empowers healthcare providers to navigate the future of medicine with confidence.

As healthcare continues to evolve, MedAI remains a trailblazer, driving innovation that transforms patient care and outcomes. Explore MedAI’s comprehensive solutions today and discover the next frontier of medical excellence.

For More Reachout :https://medai.tech4bizsolutions.com/

#ai #tech4bizsolutions #medai #medicalai

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