#AI Control and Management

[quaranmine]

On Wednesday before I gave my presentation I confessed to a new employee that I was worried it would be too long and she brightly told me her life hack was to just let AI rewrite things for her. She said I should put in all my talking points and ask ChatGPT to give me a five minute exactly presentation. I was like....how is the most polite possible way (since this is a new colleague I shouldn't get off on the wrong foot with) that I can express that I will Not be taking this advice. Ever. I told her that I didn't think we were allowed to use ChatGPT at this job (we most certainly are not, it is a nightmare for any type of protected information) and also that I prefer to write all of my own work. Despite my best efforts the last part of that was still passive aggressive, lol.

Something about being a writer makes it so that it's almost offensive to me for someone to suggest I use AI to do my work instead? Like, the day I reach the point where I let AI write something for me is the day y'all need to be checking me for brain damage because clearly I'm losing it

[teaboot]

I’m starting to sound like a nutcase at work because upper management keeps trying to implement AI programs and AI assistants and Chat GPT and my middle-of-the-road, don’t-infodump, don’t-engage response has been “I don’t like AI”, “I prefer to remain in control of my own tasks”, “I’d rather make my own mistakes”, and “I don’t trust any machine smarter than a toaster”

[mappingtomorrow]

I'm excited to share my latest in-depth analysis on how Geographic Information Systems are revolutionizing defense operations worldwide. As the author of "Mapping Tomorrow: Navigating to the World of Geographic Information System," I've explored how cutting-edge geospatial technologies are transforming military capabilities across multiple domains. In this comprehensive blog post, I examine six critical case studies that demonstrate the strategic impact of advanced GIS implementation: Indo-Pacific Maritime Domain Awareness systems integrating satellite surveillance with oceanographic modeling Humanitarian demining operations enhanced through AI-driven terrain analysis and probability mapping Urban warfare planning revolutionized by high-fidelity 3D modeling and subsurface infrastructure mapping South Asian border security monitoring leveraging multi-sensor integration and cross-border incident mapping Military humanitarian assistance powered by damage assessment automation and resource optimization Electromagnetic spectrum operations treating digital signals as mappable terrain The article also explores emerging trends that will shape the future of defense GIS, including quantum computing applications, edge computing for disconnected operations, and advanced human-machine teaming in spatial analysis. As conflicts become increasingly complex and multi-domain, superior geospatial intelligence has emerged as a decisive factor in both conventional military operations and asymmetric warfare. The organizations that most effectively leverage these capabilities will maintain significant advantages in our increasingly contested world.

[jcmarchi]

AI’s Real Value Is Built on Data and People – Not Just Technology

New Post has been published on https://thedigitalinsider.com/ais-real-value-is-built-on-data-and-people-not-just-technology/

AI’s Real Value Is Built on Data and People – Not Just Technology

The promise of AI expands daily – from driving individual productivity gains to enabling organizations to uncover powerful new business insights through data. While the potential of AI appears limitless and its impact easy to imagine, the journey to a truly AI-powered ecosystem is both complex and challenging. This journey doesn’t begin and end with implementing, adopting or even consistently using AI – it ends there. Realizing the full value of an AI solution ultimately depends on the quality of the data and the people who implement, manage and apply it to drive meaningful results.

Data: The Cornerstone of AI Success

Data, the organizational constant. Whether it’s a Mom-and-Pop convenience store or an enterprise organization, every business runs on data (financial records, inventory, security footage  etc.) The   management, accessibility and governance of this data is the cornerstone to realizing AI’s full  potential  within an organization. Gartner recently noted that 63% of organizations either lack confidence or are unsure about if their existing data practice or management structure is sufficient for successful adoption of AI. Enabling an organization to unlock  the full potential of AI requires a well thought out Data Practice. From collection, storage, synthesis, analysis, security, privacy, governance, and access control – a framework and methodology must be in place to leverage AI properly.  Additionally, it is essential to mitigate the risks and unintended consequences. Bottom line, data is the cornerstone of analytics and the fuel for your AI.

The access your AI solution has to your data determines its potential to deliver – so much so, we’re seeing the emergence of new functions tailored specifically to it, the Chief Data Officer (CDO). Simply put, if an AI solution is introduced to an environment with “free-floating” data accessible to anyone – it will be error-prone, biased, non-compliant, and very likely to expose sensitive and private information. Conversely, when  the data environment is rich, structured, accurate, within a framework and methodology for how the organization uses its data – AI can return immediate benefits and save numerous hours on modeling, forecasting, and propensity development. Built around the data cornerstone are access rights and governance policies for data, which present its own concern – the human element.

People: The Underrated Factor in AI Adoption

IDC recently shared that 45% of CEOs and over 66% of CIOs surveyed conveyed a hesitancy around technology vendors not completely understanding the downside risk potential of AI. These leaders are justified in their caution. Arguably, the consequences of age-old IT risks remain similar with governed AI (i.e., downtime, operational seizures, costly cyber-insurance premiums, compliance fines, customer experience, data-breaches, ransomware, and more.) and are amplified by the integration of AI into IT. The concern comes from the lack of understanding around the root-causes for those consequences or for those that are not aware, the angst that comes with associate AI enablement serving as the catalyst for those consequences.

The pressing question is, “Should I invest in this costly IT tool that can vastly improve my business’s performance at every functional level at the risk of IT implosion due to lack of employee readiness and enablement?” Dramatic? Absolutely – business risk always is, and we already know the answer to that question. With more complex technologies and elevated operational potential, so too must the effort to enable teams to use these tools legally, properly, efficiently, and effectively.

The Vendor Challenge

The lack of confidence in technology vendors’ understanding goes beyond subject matter expertise and reflects a deeper issue: the inability to clearly articulate the specific risks that an organization can and will face with improper implementations and unrealistic expectations.

The relationship between an organization and technology vendors is much like that of a patient and a healthcare practitioner. The patient consults a healthcare practitioner with symptoms seeking a diagnosis and hoping for a simple and cost-effective remedy. In preventative situations, the healthcare practitioner will work with the patient on dietary recommendations, lifestyle choices, and specialized treatment to achieve specified health goals. Similarly, there’s an expectation that organizations will receive prescriptive solutions from technology vendors to solve or plan for technology implementations. However, when organizations are unable to provide prescriptive risks specific to given IT environments, it exacerbates the uncertainty of AI implementation.

Even when IT vendors effectively communicate the risks and potential impacts of AI, many organizations are deterred by the true total cost of ownership (TCO) involved in laying the necessary foundation. There’s a growing awareness that successful AI implementation must begin within the existing environment – and only when that environment is modernized can organizations truly unlock the value of AI integration. It’s similar to assuming that anyone can jump into the cockpit of an F1 supercar and instantly win races. Any reasonable person knows that success in racing is the result of both a skilled driver and a high-performance machine. Likewise, the benefits of AI can only be realized when an organization is properly prepared, trained, and equipped to adopt and implement it.

Case in Point: Microsoft 365 Copilot

Microsoft 365 Copilot is a great example of an existing AI solution whose potential impact and value have often been misunderstood or diluted due to customers’ misaligned expectations – in how AI should be implemented and what they believe it should do, rather than understanding what it can do. Today, more than 70% of Fortune 500 companies are already leveraging Microsoft 365 Copilot. However, the widespread fear that AI will replace jobs is largely a misconception when it comes to most real-world AI applications. While job displacement has occurred in some areas – such as fully automated “dark warehouses” – it’s important to distinguish between AI as a whole and its use in robotics. The latter has had a more direct impact on job replacement.

In the context of Modern Work, AI’s primary value lies in enhancing performance and amplifying expertise – not replacing it. By saving time and increasing functional output, AI enables more agile go-to-market strategies and faster value delivery. However, these benefits rely on critical enablers:

A mature Data Practice

Strong Access Management and Governance

Robust Security measures to mitigate risks

People enablement around responsible AI use and best practices

Here are a few examples of AI-driven functional improvements across business areas:

Sales Leaders can generate propensity models using customer lifecycle data to drive cross-sell and upsell strategies, improving customer retention and value.

Corporate Strategy & FP&A Teams gain deeper insights thanks to time saved analyzing business units, enabling better alignment with corporate goals.

Accounts Receivable Teams can manage payment cycles more efficiently with faster access to actionable data, improving outreach and customer engagement.

Marketing Leaders can build more effective, sales-aligned go-to-market strategies by leveraging AI insights on sales performance and opportunities.

Operations Teams can reduce time spent reconciling Finance and Sales data, minimizing chaos during end-of-quarter or end-of-year processes.

Customer Success & Support Teams can cut down response and resolution times by automating workflows and simplifying key steps.

These examples only scratch the surface of AI’s potential to drive functional transformation and productivity gains. Yet, realizing these benefits requires the right foundation – systems that allow AI to integrate, synthesize, analyze, and ultimately deliver on its promise.

Final Thought: No Plug-and-Play for AI

Implementing AI to unlock its full potential isn’t as simple as installing a program or application. It’s the integration of an interconnected web of autonomous functions that permeate your entire IT stack – delivering insights and operational efficiencies that would otherwise require significant manual effort, time and resources.

Realizing the value of an AI solution is grounded in building a data practice, maintaining a robust access and governance framework, and securing the ecosystem – a topic that requires its own deep dive.

The ability for technology vendors to a valued partner will be dependent on both marketing and enablement, focused on debunking myths and calibrating expectations on what harnessing the potential of AI truly means.

[manmishra]

🚀 Exciting news for publishers! Google has updated the Robots Meta Tag documentation to include AI Mode adjustments. Learn how to protect your content and amplify visibility in search results. Check out our latest article for all the details! #SEO #GoogleUpdates #AIMode #DigitalMarketing

[farmerstrend]

Safaricom & NGO Launch FarmerAI Solutions to Revolutionize Kenyan Agriculture

Safaricom PLC and Opportunity International, a global non-governmental organization, have developed FarmerAI in Kenya, an innovative AI chatbot that will provide smallholder farmers in underserved communities with real-time, relevant farming best practices. As per a 2022 report from the Kenya National Bureau of Statistics (KNBS), the agricultural sector contributes roughly 22.4% to the country’s…

[ai-factory]

AI Factory: Pioneering Innovation with Advanced AI Solutions

In today’s rapidly evolving digital landscape, businesses face unprecedented challenges and opportunities. Artificial Intelligence (AI) has emerged as a transformative force, enabling organizations to optimize operations, enhance decision-making, and deliver exceptional customer experiences. Enter the AI Factory—a revolutionary platform designed to empower businesses with scalable AI solutions tailored to their unique needs.

What is AI Factory?

The AI Factory is a cutting-edge platform that brings together advanced AI capabilities to streamline the development, deployment, and management of AI solutions. It serves as a comprehensive hub for:

AI Use Case Development

Proof of Concept (POC) Implementation

AI Solution Deployment

Lifecycle Management of AI Models

Explore more about the transformative potential of AI Factory on UnifyCloud’s AI Factory platform.

Why Businesses Need an AI Factory

The AI Factory addresses several critical pain points for organizations:

Scalability: Develop and deploy AI solutions that grow with your business.

Customization: Tailor AI models to address industry-specific challenges.

Efficiency: Automate workflows and reduce operational inefficiencies.

Cost Optimization: Manage resources effectively with tools like CloudAtlas AI Cost Optimize.

Industry-Specific Applications

Healthcare

The healthcare sector is witnessing a paradigm shift with AI-driven innovations:

Medical Imaging: Deploy AI POCs to analyze radiology images and identify anomalies with precision.

Patient Care: Leverage AI for personalized treatment plans and efficient hospital management systems.

Predictive Analytics: Harness AI to predict disease outbreaks and optimize resource allocation.

Learn more about how AI is revolutionizing healthcare on UnifyCloud’s AI solutions page.

Retail

Retail businesses can enhance customer experiences and streamline operations through AI:

Personalized Shopping: Use AI to analyze customer behavior and provide tailored recommendations.

Demand Forecasting: Implement AI POCs to predict market trends and adjust inventory levels accordingly.

Sentiment Analysis: Employ AI-driven tools to gauge customer feedback and improve service quality.

Explore how AI empowers retail on CloudAtlas AI Factory.

Finance

AI is transforming the financial services industry with:

Fraud Detection: Develop AI POCs to identify and prevent fraudulent activities in real-time.

Credit Risk Management: Utilize AI to assess creditworthiness and minimize risks.

Banking Automation: Enhance operational efficiency with generative AI for routine tasks.

Discover UnifyCloud’s innovative AI Guardian tool for compliance and security at CloudAtlas AI Guardian.

Manufacturing

The manufacturing industry benefits from AI in numerous ways:

Predictive Maintenance: Avoid equipment downtime with AI-driven insights.

Supply Chain Optimization: Streamline logistics and reduce costs with AI-powered analytics.

Product Design: Utilize generative AI to create innovative product designs.

For more insights, visit UnifyCloud’s CloudAtlas AI platform.

Construction

AI is making significant inroads in the construction industry:

Project Management: Implement AI POCs to manage timelines and resources effectively.

Safety Monitoring: Use AI to ensure worker safety and compliance with regulations.

Smart Infrastructure: Plan and execute intelligent infrastructure projects with AI insights.

Energy

The energy sector can achieve sustainability goals with AI:

Renewable Energy Forecasting: Predict energy generation patterns to optimize usage.

Smart Grid Management: Enhance energy distribution with AI-driven analytics.

Sustainable Planning: Leverage generative AI for eco-friendly energy solutions.

Visit UnifyCloud’s CloudAtlas AI Factory to explore sustainable AI innovations.

Solution-Specific Capabilities

AI Development and Deployment

Model Training: Build and train robust AI models tailored to specific business needs.

Lifecycle Management: Manage AI models from development to deployment.

Generative AI Solutions: Create innovative content and workflows with advanced generative AI tools.

Learn how CloudAtlas AI simplifies AI development and deployment.

Data Analytics

Big Data Insights: Analyze vast datasets for actionable insights.

Predictive Analytics: Forecast trends and make data-driven decisions.

Visualization: Use generative AI for intuitive and impactful data visualizations.

Automation

Business Process Automation: Streamline operations with AI-powered automation tools.

Robotic Process Automation (RPA): Implement AI POCs for efficient task automation.

Workflow Optimization: Enhance productivity with intelligent automation solutions.

Sustainability and Customer Experience

Environmental Impact Assessments: Use AI to evaluate and minimize ecological footprints.

Personalized User Experiences: Leverage generative AI for tailored customer interactions.

Sentiment Analysis: Gauge customer feedback to refine services.

Why Choose UnifyCloud’s AI Factory

UnifyCloud’s AI Factory offers:

Comprehensive Solutions: From AI development to deployment, all under one roof.

Proven Expertise: Decades of experience in delivering AI-driven business innovations.

Customizable Tools: Tailored solutions to meet unique industry demands.

Cost Efficiency: Optimize your investments with AI Cost Optimize tools.

Discover the future of AI with UnifyCloud’s CloudAtlas AI Factory.

Conclusion

The AI Factory is more than a platform; it’s a gateway to innovation and growth. By integrating AI into your business, you can unlock new opportunities, drive efficiency, and stay ahead in a competitive market. With UnifyCloud’s comprehensive suite of AI solutions, the journey from concept to execution becomes seamless. Explore the limitless possibilities of AI with UnifyCloud’s AI Factory today.

Learn More About AI Factory from Azure Marketplace – AI Factory | AI Cost Optimize | AI Guardian

[tagbintech]

AI in Manufacturing Operational Efficiency 2025

Artificial intelligence (AI) is revolutionizing the manufacturing sector by driving operational efficiency to new heights. From automating repetitive tasks to enabling real-time decision-making, AI is reshaping how manufacturing processes are executed. With the demand for smarter factories and leaner operations, companies are turning to AI to stay competitive. In this article, we explore how AI in manufacturing operational efficiency in 2025, key use cases, and the transformative benefits it offers.

The Role of AI in Manufacturing Operational Efficiency 2025

AI-powered solutions are pivotal in addressing the complexities of modern manufacturing. By integrating AI into operations, manufacturers can achieve predictive maintenance, improve quality control, and streamline supply chain processes. This leads to cost reduction, enhanced productivity, and minimized downtime.

Benefits of AI in Manufacturing 2025

Predictive Maintenance: AI algorithms analyze machine data to predict failures before they occur, reducing unplanned downtime.

Quality Control: Machine learning models detect defects in real-time, ensuring consistent product quality.

Supply Chain Optimization: AI enhances inventory management, demand forecasting, and supplier coordination.

Energy Efficiency: AI optimizes energy consumption by adjusting processes to real-time demands.

Human-Machine Collaboration: AI-powered tools empower workers with actionable insights, enhancing productivity and safety.

Key AI Applications in Manufacturing 2025

1. Predictive Maintenance

Predictive maintenance uses AI to monitor equipment performance and anticipate potential failures. Sensors embedded in machines collect real-time data, which AI models analyze to identify anomalies and predict when maintenance is needed. This reduces unexpected breakdowns and maintenance costs.

2. Quality Assurance

AI-powered computer vision systems inspect products for defects with greater accuracy than human inspectors. These systems can process thousands of images per second, identifying flaws invisible to the naked eye and ensuring consistent quality standards.

3. Supply Chain Management

AI streamlines supply chain operations by predicting demand, managing inventory, and optimizing logistics. This reduces lead times and ensures the availability of materials, enhancing operational efficiency.

4. Robotics and Automation

AI-driven robots handle complex tasks such as assembly, welding, and material handling. These robots adapt to changing conditions and learn from their environment, boosting flexibility and precision in manufacturing.

5. Energy Optimization

AI analyzes energy consumption patterns to identify inefficiencies. By adjusting machine operations and production schedules, manufacturers can significantly reduce energy usage and costs.

The Impact of AI on Operational Efficiency 2025

Enhanced Decision-Making

AI provides real-time insights that enable manufacturers to make informed decisions quickly. By analyzing vast amounts of data, AI identifies trends and patterns that humans might overlook.

Reduced Waste

AI optimizes resource utilization, reducing waste and lowering production costs. Smart systems adjust processes dynamically to minimize material wastage.

Improved Customer Satisfaction

By ensuring consistent product quality and timely deliveries, AI helps manufacturers meet customer expectations. Predictive analytics also enable personalized customer solutions.

Scalability

AI enables manufacturers to scale operations efficiently by automating repetitive tasks and optimizing workflows. This allows companies to meet growing demand without compromising quality or increasing costs.

Challenges and Solutions in Implementing AI

Data Management

AI systems require vast amounts of high-quality data for training. Manufacturers must invest in data collection and management systems to ensure reliable outcomes.

Integration with Legacy Systems

Integrating AI with existing infrastructure can be challenging. Partnering with experienced AI solution providers helps ensure seamless integration.

Workforce Training

Adopting AI requires upskilling employees to work alongside AI tools. Offering training programs ensures a smooth transition to AI-driven workflows.

The Future of AI in Manufacturing 2025

The adoption of AI in manufacturing is expected to grow exponentially. Emerging technologies such as generative AI, digital twins, and edge computing will further enhance operational efficiency. Companies that embrace AI will gain a competitive edge, driving innovation and sustainability in the manufacturing sector.

Challenges include data management, integration with legacy systems, and workforce training. Addressing these issues requires strategic planning and partnering with AI solution providers.

AI is transforming manufacturing by unlocking new levels of operational efficiency in 2025. From predictive maintenance to energy optimization, the possibilities are vast. By embracing AI, manufacturers can future-proof their operations and drive sustainable growth.

Content Source - https://tagbinnews.blogspot.com/2025/01/ai-in-manufacturing-operational.html

[ambrosiaventures]

How Pharmaceutical Consulting Can Help Launch Your New Product Successfully

Ambrosia Ventures, we ensure your product launch achieves maximum impact by utilizing our expertise in biopharma consulting, which makes us a trusted pharmaceutical consulting service provider in the US. Here's the way to transform your product launch strategy into a blueprint for success through pharmaceutical consulting services

[gagande]

PureCode software reviews | Avoid Redirects for Authentication Checks

Instead of using redirects to check if a user is authenticated, employ route guards or higher-order components (HOCs) to protect sensitive routes. This method also provides a more secure and centralized way of managing access control.

[leonbasinwriter]

AI-Enhanced Zero Trust for Third-Party Risk Management: Strategic Insights for 2025

Research projects that by 2025, 45% of organizations worldwide will experience attacks on their software supply chains, marking a significant rise from recent years (Cybersecurity Magazine, 2023).

Leon Basin | Strategic Business Development & Account Management | B2B Cybersecurity | AI-Privileged Access Management | Driving revenue growth and building strong customer relationships. Connect with me to discuss how we can enhance your organization’s PAM strategy. The Evolving Threat Landscape in Third-Party Security Research projects that by 2025, 45% of organizations worldwide will…

[jcmarchi]

Saryu Nayyar, CEO and Founder of Gurucul – Interview Series

New Post has been published on https://thedigitalinsider.com/saryu-nayyar-ceo-and-founder-of-gurucul-interview-series/

Saryu Nayyar, CEO and Founder of Gurucul – Interview Series

Saryu Nayyar is an internationally recognized cybersecurity expert, author, speaker and member of the Forbes Technology Council. She has more than 15 years of experience in the information security, identity and access management, IT risk and compliance, and security risk management sectors.

She was named EY Entrepreneurial Winning Women in 2017. She has held leadership roles in security products and services strategy at Oracle, Simeio, Sun Microsystems, Vaau (acquired by Sun) and Disney. Saryu also spent several years in senior positions at the technology security and risk management practice of Ernst & Young.

Gurucul is a cybersecurity company that specializes in behavior-based security and risk analytics. Its platform leverages machine learning, AI, and big data to detect insider threats, account compromise, and advanced attacks across hybrid environments. Gurucul is known for its Unified Security and Risk Analytics Platform, which integrates SIEM, UEBA (User and Entity Behavior Analytics), XDR, and identity analytics to provide real-time threat detection and response. The company serves enterprises, governments, and MSSPs, aiming to reduce false positives and accelerate threat remediation through intelligent automation.

What inspired you to start Gurucul in 2010, and what problem were you aiming to solve in the cybersecurity landscape?

Gurucul was founded to help Security Operations and Insider Risk Management teams obtain clarity into the most critical cyber risks impacting their business. Since 2010 we’ve taken a behavioral and predictive analytics approach, rather than rules-based, which has generated over 4,000+ machine learning models that put user and entity anomalies into context across a variety of different attack and risk scenarios. We’ve built upon this as our foundation, moving from helping large Fortune 50 companies solve Insider Risk challenges, to helping companies gain radical clarity into ALL cyber risk. This is the promise of REVEAL, our unified and AI-Driven Data and Security Analytics platform. Now we’re building on our AI mission with a vision to deliver a Self-Driving Security Analytics platform, using Machine Learning as our foundation but now layering on Generative and Agentic AI capabilities across the entire threat lifecycle. The goal is for analysts and engineers to spend less time in the myriad in complexity and more time focused on meaningful work. Allowing machines to amplify the definition of their day-to-day activities.

Having worked in leadership roles at Oracle, Sun Microsystems, and Ernst & Young, what key lessons did you bring from those experiences into founding Gurucul?

My leadership experience at Oracle, Sun Microsystems, and Ernst & Young strengthened my ability to solve complex security challenges and provided me with an understanding of the challenges that Fortune 100 CEOs and CISOs face. Collectively, it allowed me to gain a front-row seat the technological and business challenges most security leaders face and inspired me to build solutions to bridge those gaps.

How does Gurucul’s REVEAL platform differentiate itself from traditional SIEM (Security Information and Event Management) solutions?

Legacy SIEM solutions depend on static, rule-based approaches that lead to excessive false positives, increased costs, and delayed detection and response. Our REVEAL platform is fully cloud-native and AI-driven, utilizing advanced machine learning, behavioral analytics, and dynamic risk scoring to detect and respond to threats in real time. Unlike traditional platforms, REVEAL continuously adapts to evolving threats and integrates across on-premises, cloud, and hybrid environments for comprehensive security coverage. Recognized as the ‘Most Visionary’ SIEM solution in Gartner’s Magic Quadrant for three consecutive years, REVEAL redefines AI-driven SIEM with unmatched precision, speed, and visibility. Furthermore, SIEMs struggle with a data overload problem. They are too expensive to ingest everything needed for complete visibility and even if they do it just adds to the false positive problem. Gurucul understands this problem and it’s why we have a native and AI-driven Data Pipeline Management solution that filters non-critical data to low-cost storage, saving money, while retaining the ability to run federated search across all data. Analytics systems are a “garbage in, garbage out” situation. If the data coming in is bloated, unnecessary or incomplete then the output will not be accurate, actionable or ultimately trusted.

Can you explain how machine learning and behavioral analytics are used to detect threats in real time?

Our platform leverages over 4,000 machine learning models to continuously analyze all relevant datasets and identify anomalies and suspicious behaviors in real time. Unlike legacy security systems that rely on static rules, REVEAL uncovers threats as they emerge. The platform also utilizes User and Entity Behavior Analytics (UEBA) to establish baselines of normal user and entity behavior, detecting deviations that could indicate insider threats, compromised accounts, or malicious activity. This behavior is further contextualized by a big data engine that correlates, enriches and links security, network, IT, IoT, cloud, identity, business application data and both internal and external sourced threat intelligence. This informs a dynamic risk scoring engine that assigns real-time risk scores that help prioritize responses to critical threats. Together, these capabilities provide a comprehensive, AI-driven approach to real-time threat detection and response that set REVEAL apart from conventional security solutions.

How does Gurucul’s AI-driven approach help reduce false positives compared to conventional cybersecurity systems?

The REVEAL platform reduces false positives by leveraging AI-driven contextual analysis, behavioral insights, and machine learning to distinguish legitimate user activity from actual threats. Unlike conventional solutions, REVEAL refines its detection capabilities over time, improving accuracy while minimizing noise. Its UEBA detects deviations from baseline activity with high accuracy, allowing security teams to focus on legitimate security risks rather than being overwhelmed by false alarms. While Machine Learning is a foundational aspect, generative and agentic AI play a significant role in further appending context in natural language to help analysts understand exactly what is happening around an alert and even automate the response to said alerts.

What role does adversarial AI play in modern cybersecurity threats, and how does Gurucul combat these evolving risks?

First all we’re already seeing adversarial AI being applied to the lowest hanging fruit, the human vector and identity-based threats. This is why behavioral, and identity analytics are critical to being able to identify anomalous behaviors, put them into context and predict malicious behavior before it proliferates further. Furthermore, adversarial AI is the nail in the coffin for signature-based detection methods. Adversaries are using AI to evade these TTP defined detection rules, but again they can’t evade the behavioral based detections in the same way. SOC teams are not resourced adequately to continue to write rules to keep pace and will require a modern approach to threat detection, investigation and response. Behavior and context are the key ingredients.  Finally, platforms like REVEAL depend on a continuous feedback loop and we’re constantly applying AI to help us refine our detection models, recommend new models and inform new threat intelligence our entire ecosystem of customers can benefit from.

How does Gurucul’s risk-based scoring system improve security teams’ ability to prioritize threats?

Our platform’s dynamic risk scoring system assigns real-time risk scores to users, entities, and actions based on observed behaviors and contextual insights. This enables security teams to prioritize critical threats, reducing response times and optimizing resources. By quantifying risk on a 0–100 scale, REVEAL ensures that organizations focus on the most pressing incidents rather than being overwhelmed by low-priority alerts. With a unified risk score spanning all enterprise data sources, security teams gain greater visibility and control, leading to faster, more informed decision-making.

In an age of increasing data breaches, how can AI-driven security solutions help organizations prevent insider threats?

Insider threats are an especially challenging security risk due to their subtle nature and the access that employees possess. REVEAL’s UEBA detects deviations from established behavioral baselines, identifying risky activities such as unauthorized data access, unusual login times, and privilege misuse. Dynamic risk scoring also continuously assesses behaviors in real time, assigning risk levels to prioritize the most pressing insider risks. These AI-driven capabilities enable security teams to proactively detect and mitigate insider threats before they escalate into breaches. Given the predictive nature of behavioral analytics Insider Risk Management is race against the clock. Insider Risk Management teams need to be able to respond and collaborate quickly, with privacy top-of-mind. Context again is critical here and appending behavioral deviations with context from identity systems, HR applications and all other relevant data sources gives these teams the ammunition to quickly build and defend a case of evidence so the business can respond and remediate before data exfiltration occurs.

How does Gurucul’s identity analytics solution enhance security compared to traditional IAM (identity and access management) tools?

Traditional IAM solutions focus on access control and authentication but lack the intelligence and visibility to detect compromised accounts or privilege abuse in real time. REVEAL goes beyond these limitations by leveraging AI-powered behavioral analytics to continuously assess user risk, dynamically adjust risk scores, and enforce adaptive access entitlements, minimizing misuse and illegitimate privileges. By integrating with existing IAM frameworks and enforcing least-privilege access, our solution enhances identity security and reduces the attack surface. The problem with IAM governance is identity system sprawl and the lack of interconnectedness between different identity systems. Gurucul gives teams a 360° view of their identity risks across all identity infrastructure. Now they can stop rubber stamping access but rather take risk-oriented approach to access policies. Furthermore, they can expedite the compliance aspect of IAM and demonstrate a continuous monitoring and fully holistic approach to access controls across the organization.

What are the key cybersecurity threats you foresee in the next five years, and how can AI help mitigate them?

Identity-based threats will continue to proliferate, because they have worked. Adversaries are going to double-down on gaining access by logging in either via compromising insiders or attacking identity infrastructure. Naturally insider threats will continue to be a key risk vector for many businesses, especially as shadow IT continues. Whether malicious or negligent, companies will increasingly need visibility into insider risk. Furthermore, AI will accelerate the variations of conventional TTPs, because adversaries know that is how they will be able to evade detections by doing so and it will be low cost for them to creative adaptive tactics, technics and protocols. Hence again why focusing on behavior in context and having detection systems capable of adapting just as fast will be crucial for the foreseeable future.

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

[nnctales]

AI Consulting Business in Construction: Transforming the Industry

The construction industry is experiencing a profound transformation due to the integration of artificial intelligence (AI). The AI consulting business is at the forefront of this change, guiding construction firms in optimizing operations, enhancing safety, and improving project outcomes. This article explores various applications of AI in construction, supported by examples and statistics that…

[futurefatum]

Are We On The Brink Of A Revolution? (Tone: 400)

Eric Weinstein discusses the potential for a political and social revolution driven by misinformation and AI governance. #Politics #AI #Revolution

September 2nd, 2024 by @ChrisWillx Eric Weinstein – Are We On The Brink Of A Revolution? ABOUT THIS VIDEO: In the video “Are We On The Brink Of A Revolution?” by Eric Weinstein, he discusses the current state of political, economic, and scientific systems in the United States and globally. Weinstein explores the concept of a managed reality, where societal narratives are carefully controlled…

[ambrosiaventures]

How Pharmaceutical Consulting Can Help Launch Your New Product Successfully

At Ambrosia Ventures, we ensure your product launch achieves maximum impact by utilizing our expertise in biopharma consulting, which makes us a trusted pharmaceutical consulting service provider in the US. Here's the way to transform your product launch strategy into a blueprint for success through pharmaceutical consulting services: