#Cloud data

[jmmallory]

A safe place to store data...but not THAT safe

[jcmarchi]

Future-Ready Enterprises: The Crucial Role of Large Vision Models (LVMs)

New Post has been published on https://thedigitalinsider.com/future-ready-enterprises-the-crucial-role-of-large-vision-models-lvms/

Future-Ready Enterprises: The Crucial Role of Large Vision Models (LVMs)

What are Large Vision Models (LVMs)

Over the last few decades, the field of Artificial Intelligence (AI) has experienced rapid growth, resulting in significant changes to various aspects of human society and business operations. AI has proven to be useful in task automation and process optimization, as well as in promoting creativity and innovation. However, as data complexity and diversity continue to increase, there is a growing need for more advanced AI models that can comprehend and handle these challenges effectively. This is where the emergence of Large Vision Models (LVMs) becomes crucial.

LVMs are a new category of AI models specifically designed for analyzing and interpreting visual information, such as images and videos, on a large scale, with impressive accuracy. Unlike traditional computer vision models that rely on manual feature crafting, LVMs leverage deep learning techniques, utilizing extensive datasets to generate authentic and diverse outputs. An outstanding feature of LVMs is their ability to seamlessly integrate visual information with other modalities, such as natural language and audio, enabling a comprehensive understanding and generation of multimodal outputs.

LVMs are defined by their key attributes and capabilities, including their proficiency in advanced image and video processing tasks related to natural language and visual information. This includes tasks like generating captions, descriptions, stories, code, and more. LVMs also exhibit multimodal learning by effectively processing information from various sources, such as text, images, videos, and audio, resulting in outputs across different modalities.

Additionally, LVMs possess adaptability through transfer learning, meaning they can apply knowledge gained from one domain or task to another, with the capability to adapt to new data or scenarios through minimal fine-tuning. Moreover, their real-time decision-making capabilities empower rapid and adaptive responses, supporting interactive applications in gaming, education, and entertainment.

How LVMs Can Boost Enterprise Performance and Innovation?

Adopting LVMs can provide enterprises with powerful and promising technology to navigate the evolving AI discipline, making them more future-ready and competitive. LVMs have the potential to enhance productivity, efficiency, and innovation across various domains and applications. However, it is important to consider the ethical, security, and integration challenges associated with LVMs, which require responsible and careful management.

Moreover, LVMs enable insightful analytics by extracting and synthesizing information from diverse visual data sources, including images, videos, and text. Their capability to generate realistic outputs, such as captions, descriptions, stories, and code based on visual inputs, empowers enterprises to make informed decisions and optimize strategies. The creative potential of LVMs emerges in their ability to develop new business models and opportunities, particularly those using visual data and multimodal capabilities.

Prominent examples of enterprises adopting LVMs for these advantages include Landing AI, a computer vision cloud platform addressing diverse computer vision challenges, and Snowflake, a cloud data platform facilitating LVM deployment through Snowpark Container Services. Additionally, OpenAI, contributes to LVM development with models like GPT-4, CLIP, DALL-E, and OpenAI Codex, capable of handling various tasks involving natural language and visual information.

In the post-pandemic landscape, LVMs offer additional benefits by assisting enterprises in adapting to remote work, online shopping trends, and digital transformation. Whether enabling remote collaboration, enhancing online marketing and sales through personalized recommendations, or contributing to digital health and wellness via telemedicine, LVMs emerge as powerful tools.

Challenges and Considerations for Enterprises in LVM Adoption

While the promises of LVMs are extensive, their adoption is not without challenges and considerations. Ethical implications are significant, covering issues related to bias, transparency, and accountability. Instances of bias in data or outputs can lead to unfair or inaccurate representations, potentially undermining the trust and fairness associated with LVMs. Thus, ensuring transparency in how LVMs operate and the accountability of developers and users for their consequences becomes essential.

Security concerns add another layer of complexity, requiring the protection of sensitive data processed by LVMs and precautions against adversarial attacks. Sensitive information, ranging from health records to financial transactions, demands robust security measures to preserve privacy, integrity, and reliability.

Integration and scalability hurdles pose additional challenges, especially for large enterprises. Ensuring compatibility with existing systems and processes becomes a crucial factor to consider. Enterprises need to explore tools and technologies that facilitate and optimize the integration of LVMs. Container services, cloud platforms, and specialized platforms for computer vision offer solutions to enhance the interoperability, performance, and accessibility of LVMs.

To tackle these challenges, enterprises must adopt best practices and frameworks for responsible LVM use. Prioritizing data quality, establishing governance policies, and complying with relevant regulations are important steps. These measures ensure the validity, consistency, and accountability of LVMs, enhancing their value, performance, and compliance within enterprise settings.

Future Trends and Possibilities for LVMs

With the adoption of digital transformation by enterprises, the domain of LVMs is poised for further evolution. Anticipated advancements in model architectures, training techniques, and application areas will drive LVMs to become more robust, efficient, and versatile. For example, self-supervised learning, which enables LVMs to learn from unlabeled data without human intervention, is expected to gain prominence.

Likewise, transformer models, renowned for their ability to process sequential data using attention mechanisms, are likely to contribute to state-of-the-art outcomes in various tasks. Similarly, Zero-shot learning, allowing LVMs to perform tasks they have not been explicitly trained on, is set to expand their capabilities even further.

Simultaneously, the scope of LVM application areas is expected to widen, encompassing new industries and domains. Medical imaging, in particular, holds promise as an avenue where LVMs could assist in the diagnosis, monitoring, and treatment of various diseases and conditions, including cancer, COVID-19, and Alzheimer’s.

In the e-commerce sector, LVMs are expected to enhance personalization, optimize pricing strategies, and increase conversion rates by analyzing and generating images and videos of products and customers. The entertainment industry also stands to benefit as LVMs contribute to the creation and distribution of captivating and immersive content across movies, games, and music.

To fully utilize the potential of these future trends, enterprises must focus on acquiring and developing the necessary skills and competencies for the adoption and implementation of LVMs. In addition to technical challenges, successfully integrating LVMs into enterprise workflows requires a clear strategic vision, a robust organizational culture, and a capable team. Key skills and competencies include data literacy, which encompasses the ability to understand, analyze, and communicate data.

The Bottom Line

In conclusion, LVMs are effective tools for enterprises, promising transformative impacts on productivity, efficiency, and innovation. Despite challenges, embracing best practices and advanced technologies can overcome hurdles. LVMs are envisioned not just as tools but as pivotal contributors to the next technological era, requiring a thoughtful approach. A practical adoption of LVMs ensures future readiness, acknowledging their evolving role for responsible integration into business processes.

[sanjanabia]

Cloud Security: Is Your Data Truly in the Cloud... or Up for Grabs?

The convenience of cloud storage is undeniable. Accessing your data from anywhere, anytime, with scalability and cost-effectiveness – it's a dream come true for businesses of all sizes. But with this convenience comes a crucial question: Is your data truly secure in the cloud?

The reality is, the cloud isn't a mythical realm beyond the reach of cybercriminals. Data breaches in cloud environments happen with alarming frequency. Hackers are constantly innovating, exploiting vulnerabilities in cloud security to steal sensitive information.

This blog delves into the world of cloud security, exploring common threats, best practices, and the importance of a skilled workforce. We'll also highlight how reputable cyber security institutes in Mumbai can equip professionals with the knowledge and expertise to safeguard your valuable data in the cloud.

Beyond Convenience: The Dark Side of Cloud Security

While cloud providers offer a range of security features, the ultimate responsibility for data security lies with the organization using the cloud service. Common cloud security threats include:

Misconfigurations: Improper configuration of cloud storage buckets or access controls can leave data exposed and vulnerable.

Data Breaches: Hackers can exploit vulnerabilities in cloud platforms or gain access through compromised user credentials, leading to data breaches.

Insider Threats: Malicious insiders with authorized access can steal or leak sensitive data stored in the cloud.

Denial-of-Service (DoS) Attacks: These attacks can disrupt access to cloud resources, impacting business operations.

Securing Your Cloud: Best Practices for Data Protection

Here are some key steps to ensure the security of your data in the cloud:

Implement Strong Access Controls: Enforce robust access controls, including the principle of least privilege, to restrict access to data based on user roles and needs.

Encrypt Your Data: Encrypting data at rest and in transit adds an extra layer of security, making it unreadable even if intercepted by hackers.

Regular Security Audits: Conduct regular penetration testing and vulnerability assessments to identify and address weaknesses in your cloud security posture.

Employee Training: Cyber security institutes in Mumbai offer comprehensive training programs that equip employees with the knowledge to recognize and avoid phishing attacks, social engineering tactics, and other threats to cloud security.

Investing in Skills: Why Mumbai's Cyber Security Institutes Matter

In today's digital landscape, skilled cybersecurity professionals are in high demand. Cyber security institutes in Mumbai offer a multitude of training programs and certifications that empower individuals to safeguard data in the cloud:

Cloud Security Fundamentals: These courses equip learners with a foundational understanding of cloud security concepts, threats, and best practices.

Cloud Security Architecture: Advanced programs delve into designing and implementing secure cloud architectures, ensuring data is protected throughout its lifecycle.

Cloud Penetration Testing: Cyber security institutes in Mumbai offer training in cloud penetration testing, allowing individuals to identify and exploit vulnerabilities in cloud environments before malicious actors can.

Beyond the Cloud: Building a Secure Digital Future

Cloud security is a continuous journey, not a destination. By implementing best practices, fostering a culture of security awareness, and investing in skilled cybersecurity professionals trained by cyber security institutes in Mumbai, businesses can confidently leverage the cloud's potential while minimizing data security risks. This collaborative effort ensures a more secure digital environment for organizations of all sizes.

[castleinteractivellc]

In today's digital era, data is the lifeblood of any business. The ability to efficiently collect, store, manage, and analyze data can make the difference between thriving and merely surviving in a competitive marketplace. Castle Interactive understands this dynamic and offers cutting-edge cloud data solutions designed to empower your business. Let's explore how these solutions can transform your operations and drive growth.

[phonegap]

Explore the impactful use cases of Airbyte in the fintech industry, from centralizing customer data for enhanced insights to real-time fraud detection and ensuring regulatory compliance. Learn how Airbyte drives operational efficiency by streamlining data integration across various fintech applications, providing businesses with actionable insights and improved processes.

Know more at: https://bit.ly/3UbqGyT

[techaiml]

Data warehousing has become a trending avenue for enterprises to better manage large volumes of data. Leveraging it with the latest capabilities will help you stay relevant and neck-to-neck with the competition. Here are a few data warehousing trends that you should keep in mind for 2024 for your data warehousing requirements.

[sighspectrum-blog]

Data Quality Management in Master Data Management: Ensuring Accurate and Reliable Information

Introduction: In the digital age, data is often referred to as the new gold. However, just like gold, data needs to be refined and polished to extract its true value. This is where Data Quality Management (DQM), offered by master data management companies, steps in, playing a pivotal role in the success of Master Data Management (MDM) initiatives. In this blog post, we'll explore the critical importance of data quality within MDM and delve into the techniques that organizations can employ, with the assistance of master data management services, to achieve and maintain accurate and reliable master data.

The Crucial Role of Data Quality in MDM: Data quality is the foundation upon which effective MDM is built, facilitated by cloud data services. Accurate, consistent, and reliable master data ensures that business decisions are well-informed and reliable. Poor data quality can lead to erroneous insights, wasted resources, and missed opportunities. Therefore, implementing robust data quality management practices, with the support of cloud data management services, is essential.

Techniques for Data Quality Management in MDM:

Data Profiling: Data profiling involves analyzing data to gain insights into its structure, content, and quality, enabled by data analytics consulting services. By identifying anomalies, inconsistencies, and gaps, organizations can prioritize their data quality efforts effectively.

Actionable Tip: Utilize automated data profiling tools, including cloud data management services, to uncover hidden data quality issues across your master data.

Data Cleansing: Data cleansing is the process of identifying and rectifying errors, inconsistencies, and inaccuracies within master data, supported by cloud data management solutions. This involves standardizing formats, removing duplicates, and correcting inaccurate entries.

Actionable Tip: Establish data quality rules and automated processes, with the assistance of data analytics consulting services, to regularly cleanse and validate master data.

Data Enrichment: Data enrichment involves enhancing master data by adding valuable information from trusted external sources, facilitated by data analytics consulting services. This enhances data completeness and accuracy, leading to better decision-making.

Actionable Tip: Integrate third-party data sources and APIs, in collaboration with master data management solutions, to enrich your master data with up-to-date and relevant information.

Ongoing Monitoring: Data quality is not a one-time task but an ongoing process, supported by cloud data services. Regularly monitor and assess the quality of your master data, with the assistance of cloud data management services, to detect and address issues as they arise.

Actionable Tip: Implement data quality metrics and key performance indicators (KPIs), leveraging cloud data management solutions, to track the health of your master data over time.

Real-World Examples:

Example 1: Data Profiling at Retail Innovators Inc. Retail Innovators Inc., in partnership with master data management services, implemented data profiling tools to analyze their customer master data. They discovered that approximately 15% of customer records had incomplete contact information. By addressing this issue, with the support of cloud data management services, they improved customer communication and reduced delivery failures.

Example 2: Data Cleansing at Healthcare Solutions Co. Healthcare Solutions Co. partnered with cloud data management services and embarked on a data cleansing initiative for their patient master data. They removed duplicate patient records and corrected inaccuracies in demographic details. This led to more accurate patient billing and streamlined appointment scheduling.

Example 3: Data Enrichment at Financial Services Group. Financial Services Group collaborated with data analytics consulting services and integrated external data sources to enrich their client master data. By adding socio-economic indicators, they gained deeper insights into their clients' financial needs and improved personalized financial advisory services.

Conclusion: In the world of Master Data Management, data quality is not an option; it's a necessity, facilitated by cloud data management services. Effective Data Quality Management, supported by master data management solutions, ensures that your master data remains accurate, consistent, and reliable, forming the bedrock of successful MDM initiatives. By leveraging techniques such as data profiling, cleansing, enrichment, and ongoing monitoring, in collaboration with data analytics consulting services, organizations can unlock the true potential of their master data. This enables them to make more informed decisions, enhance customer experiences, and gain a competitive edge in the marketplace. Remember, the journey to high-quality master data begins with a commitment to data excellence through diligent Data Quality Management.

[ameliakeli]

Cloud Data Migration for MSPs: CloudFuze’s Reselling Benefits

https://www.cloudfuze.com/cloud-data-migration-for-msps-cloudfuzes-reselling-benefits/

[techy-trends]

Elеvating Cloud Data Encryption: Exploring Nеxt-Gеnеration Tеchniquеs

In an еra whеrе data brеachеs and cybеr thrеats havе bеcomе morе sophisticatеd than еvеr,  thе nееd for robust data protеction mеasurеs has intеnsifiеd. Cloud computing, which offers immеnsе flеxibility and scalability, also introducеs sеcurity concerns duе to thе dеcеntralizеd naturе of data storagе.  This is whеrе data еncryption еmеrgеs as a powerful solution, and as technology еvolvеs,  so do thе tеchniquеs for safеguarding sеnsitivе information in thе cloud. 

Related Post: The Importance of Cloud Security in Business

Thе Crucial Rolе of Cloud Data Encryption:

Cloud data encryption involves transforming data into an unrеadablе format using cryptographic algorithms. This еncryptеd data can only bе dеciphered with a specific kеy, which is hеld by authorizеd usеrs. Thе importancе of еncryption liеs in its ability to thwart unauthorizеd accеss,  еvеn if an attackеr manages to brеach thе sеcurity pеrimеtеr.  It еnsurеs that еvеn if data is intеrcеptеd, it rеmains indеciphеrablе and thus usеlеss to malicious actors. 

Thе Evolution of Encryption Tеchniques:

As cyber thrеats grow more sophisticatеd,  еncryption tеchniquеs must also еvolvе to kееp pacе.  Lеt's dеlvе into somе of thе nеxt-gеnеration еncryption mеthods that arе еnhancing cloud data sеcurity:

Homomorphic Encryption: This groundbrеaking tеchnique allows computations to bе pеrformеd on еncryptеd data without dеcrypting it first.  It еnsures that sеnsitivе data rеmains еncryptеd throughout procеssing,  minimizing thе risk of еxposurе. 

Quantum Encryption: With thе advent of quantum computеrs,  traditional еncryption mеthods could bе compromisеd.  Quantum еncryption lеvеragеs thе principlеs of quantum mеchanics to crеatе еncryption kеys that arе virtually impossiblе to intеrcеpt or rеplicatе. 

Post-Quantum Encryption: This involvеs dеvеloping еncryption algorithms that arе resistant to attacks from both classical and quantum computеrs. As quantum computing power grows,  post-quantum еncryption will play a crucial role in maintaining data sеcurity. 

Multi-Party Computation (MPC): MPC еnablеs multiplе partiеs to jointly computе a function ovеr thеir individual inputs whilе kееping thosе inputs privatе. This tеchniquе adds an еxtra layеr of sеcurity by distributing data and computations across different еntitiеs. 

Attributе-Basеd Encryption (ABE): ABE allows data access based on attributеs rather than specific kеys. It's particularly useful in cloud еnvironmеnts whеrе multiplе usеrs with varying lеvеls of accеss might nееd to intеract with еncryptеd data. 

Tokеnization: Tokеnization involvеs rеplacing sеnsitivе data with tokеns that havе no inhеrеnt mеaning. Thеsе tokеns can bе usеd for procеssing and analysis without еxposing thе actual sеnsitivе information. 

Implementing Nеxt-Gеnеration Encryption Tеchniquеs

Whilе next-genеration еncryption tеchniquеs offеr еnhancеd sеcurity, thеy also bring challеngеs in tеrms of implеmеntation and managеmеnt:

Kеy Managеmеnt: Robust еncryption rеliеs on еffеctivе key management.  With new tеchniquеs,  key gеnеration,  storage,  and rotation bеcomе morе complеx and rеquirе mеticulous attеntion. 

Pеrformancе Overhead: Somе advancеd еncryption mеthods can introduce computational ovеrhеad, potentially affеcting thе pеrformancе of applications and systеms. Striking a balancе bеtwееn sеcurity and pеrformancе is crucial. 

Intеgration with Existing Systеms: Intеgrating nеxt-gеnеration еncryption into еxisting cloud еnvironmеnts and applications can bе intricatе. It oftеn rеquirеs carеful planning and adaptation of currеnt architеcturе. 

Usеr Expеriеncе: Encryption should not hindеr usеr еxpеriеncе.  Accessing еncryptеd data should bе sеamlеss for authorizеd usеrs whilе maintaining stringеnt sеcurity mеasurеs. 

Bеst Practicеs for Nеxt-Gеnеration Cloud Data Encryption

Assess Your Nееds: Undеrstand your data,  its sеnsitivity,  and rеgulatory requirеmеnts to detеrminе which еncryption tеchniquеs arе most suitablе. 

End-to-End Encryption: Implemеnt encryption throughout thе data lifеcyclе – at rest,  in transit,  and during procеssing. 

Thorough Kеy Managеmеnt: Dеvotе significant attеntion to kеy managеmеnt,  including gеnеration,  storagе,  rotation,  and accеss control. 

Rеgular Audits and Updates: Encryption is not an onе-timе task. Rеgular audits and updatеs еnsure that your еncryption practicеs stay еffеctive against evolving threats. 

Training and Awareness: Educatе your tеam about еncryption bеst practices to prеvеnt inadvеrtеnt data еxposure. 

Vendor Evaluation: If you are using a cloud sеrvicе providеr, еvaluatе thеir еncryption practicеs and ensurе they align with your security requiremеnts. 

Thе Path Forward

Nеxt-genеration еncryption tеchniquеs arе shaping thе futurе of cloud data sеcurity.  As data brеachеs bеcomе more sophisticatеd,  thе need for strongеr еncryption grows.  Whilе thеsе tеchniquеs offеr еnhancеd protеction,  they also rеquirе a comprеhеnsivе undеrstanding of thеir intricacies and challengеs.  Implеmеnting thеm еffеctivеly dеmands collaboration bеtwееn sеcurity еxpеrts, cloud architеcts, and IT professionals. 

Conclusion

In conclusion, as technology еvolvеs, so do thе thrеats to our data.  Nеxt-genеration encryption tеchniquеs hold thе kеy to safеguarding sеnsitivе information in thе cloud. By еmbracing thеsе advancеd mеthods and adhеring to best practicеs, organizations can navigatе thе intricatе landscapе of cloud data sеcurity with confidеncе, еnsuring that thеir data rеmains shiеldеd from thе еvеr-еvolving rеalm of cybеr thrеats.  

[berkeley-engineering]

Arman Shehabi (Ph.D CEE’09) of Berkeley Lab spoke to The New York Times about the demand for energy efficiency amid the growing field of AI. “There has been a lot of growth, but a lot of opportunities for efficiency and incentives for efficiency,” he said.

Read the story.

Pictured: A cloud data center in blue lighting. (Courtesy Google)

[omnepresents]

Migrating from AWS Redshift to Snowflake can provide several benefits for enterprises looking to more effectively and easily work with their data. Data migration is a complex and challenging task. If you are considering migrating your data from Redshift to Snowflake, it is important to carefully plan and execute the process to ensure a smooth transition. By following best practices and working with a trusted partner, you can successfully migrate your data and leverage the capabilities of Snowflake. Check out this post to know how you can migrate your data from Redshift to the Snowflake data platform seamlessly. https://omnepresent.com/blogs/migrating-data-from-amazon-redshift-to-snowflake/

[jcmarchi]

Shaktiman Mall, Principal Product Manager, Aviatrix – Interview Series

New Post has been published on https://thedigitalinsider.com/shaktiman-mall-principal-product-manager-aviatrix-interview-series/

Shaktiman Mall, Principal Product Manager, Aviatrix – Interview Series

Shaktiman Mall is Principal Product Manager at Aviatrix. With more than a decade of experience designing and implementing network solutions, Mall prides himself on ingenuity, creativity, adaptability and precision. Prior to joining Aviatrix, Mall served as Senior Technical Marketing Manager at Palo Alto Networks and Principal Infrastructure Engineer at MphasiS.

Aviatrix is a company focused on simplifying cloud networking to help businesses remain agile. Their cloud networking platform is used by over 500 enterprises and is designed to provide visibility, security, and control for adapting to changing needs. The Aviatrix Certified Engineer (ACE) Program offers certification in multicloud networking and security, aimed at supporting professionals in staying current with digital transformation trends.

What initially attracted you to computer engineering and cybersecurity?

As a student, I was initially more interested in studying medicine and wanted to pursue a degree in biotechnology. However, I decided to switch to computer science after having conversations with my classmates about technological advancements over the preceding decade and emerging technologies on the horizon.

Could you describe your current role at Aviatrix and share with us what your responsibilities are and what an average day looks like?

I’ve been with Aviatrix for two years and currently serve as a principal product manager in the product organization. As a product manager, my responsibilities include building product vision, conducting market research, and consulting with the sales, marketing and support teams. These inputs combined with direct customer engagement help me define and prioritize features and bug fixes.

I also ensure that our products align with customers’ requirements. New product features should be easy to use and not overly or unnecessarily complex. In my role, I also need to be mindful of the timing for these features – can we put engineering resources toward it today, or can it wait six months? To that end, should the rollout be staggered or phased into different versions? Most importantly, what is the projected return on investment?

An average day includes meetings with engineering, project planning, customer calls, and meetings with sales and support. Those discussions allow me to get an update on upcoming features and use cases while understanding current issues and feedback to troubleshoot before a release.

What are the primary challenges IT teams face when integrating AI tools into their existing cloud infrastructure?

Based on real-world experience of integrating AI into our IT technology, I believe there are four challenges companies will encounter:

Harnessing data & integration: Data enriches AI, but when data is across different places and resources in an organization, it can be difficult to harness it properly.

Scaling: AI operations can be CPU intensive, making scaling challenging.

Training and raising awareness: A company could have the most powerful AI solution, but if employees don’t know how to use it or don’t understand it, then it will be underutilized.

Cost: For IT especially, a quality AI integration will not be cheap, and businesses must budget accordingly.

Security: Make sure that the cloud infrastructure meets security standards and regulatory requirements relevant to AI applications

How can businesses ensure their cloud infrastructure is robust enough to support the heavy computing needs of AI applications?

There are multiple factors to running AI applications. For starters, it’s critical to find the right type and instance for scale and performance.

Also, there needs to be adequate data storage, as these applications will draw from static data available within the company and build their own database of information. Data storage can be costly, forcing businesses to assess different types of storage optimization.

Another consideration is network bandwidth. If every employee in the company uses the same AI application at once, the network bandwidth needs to scale – otherwise, the application will be so slow as to be unusable. Likewise, companies need to decide if they will use a centralized AI model where computing happens in a single place or a distributed AI model where computing happens closer to the data sources.

With the increasing adoption of AI, how can IT teams protect their systems from the heightened risk of cyberattacks?

There are two main aspects to security every IT team must consider. First, how do we protect against external risks? Second, how do we ensure data, whether it is the personally identifiable information (PII) of customers or proprietary information, remains within the company and is not exposed? Businesses must determine who can and cannot access certain data. As a product manager, I need sensitive information others are not authorized to access or code.

At Aviatrix, we help our customers protect against attacks, allowing them to continue adopting technologies like AI that are essential for being competitive today. Recall network bandwidth optimization: because Aviatrix acts as the data plane for our customers, we can manage the data going through their network, providing visibility and enhancing security enforcement.

Likewise, our distributed cloud firewall (DCF) solves the challenges of a distributed AI model where data gets queried in multiple places, spanning geographical boundaries with different laws and compliances. Specifically, a DCF supports a single set of security compliance enforced across the globe, ensuring the same set of security and networking architecture is supported. Our Aviatrix Networks Architecture also allows us to identify choke points, where we can dynamically update the routing table or help customers create new connections to optimize AI requirements.

How can businesses optimize their cloud spending while implementing AI technologies, and what role does the Aviatrix platform play in this?

One of the main practices that will help businesses optimize their cloud spending when implementing AI is minimizing egress spend.

Cloud network data processing and egress fees are a material component of cloud costs. They are both difficult to understand and inflexible. These cost structures not only hinder scalability and data portability for enterprises, but also provide decreasing returns to scale as cloud data volume increases which can impact organizations’ bandwidth.

Aviatrix designed our egress solution to give the customer visibility and control. Not only do we perform enforcement on gateways through DCF, but we also do native orchestration, enforcing control at the network interface card level for significant cost savings. In fact, after crunching the numbers on egress spend, we had customers report savings between 20% and 40%.

We’re also building auto-rightsizing capabilities to automatically detect high resource utilization and automatically schedule upgrades as needed.

Lastly, we ensure optimal network performance with advanced networking capabilities like intelligent routing, traffic engineering and secure connectivity across multi-cloud environments.

How does Aviatrix CoPilot enhance operational efficiency and provide better visibility and control over AI deployments in multicloud environments?

Aviatrix CoPilot’s topology view provides real-time network latency and throughput, allowing customers to see the number of VPC/VNets. It also displays different cloud resources, accelerating problem identification. For example, if the customer sees a latency issue in a network, they will know which assets are getting affected. Also, Aviatrix CoPilot helps customers identify bottlenecks, configuration issues, and improper connections or network mapping. Furthermore, if a customer needs to scale up one of its gateways into the node to accommodate more AI capabilities, Aviatrix CoPilot can automatically detect, scale, and upgrade as necessary.

Can you explain how dynamic topology mapping and embedded security visibility in Aviatrix CoPilot assist in real-time troubleshooting of AI applications?

Aviatrix CoPilot’s dynamic topology mapping also facilitates robust troubleshooting capabilities. If a customer must troubleshoot an issue between different clouds (requiring them to understand where traffic was getting blocked), CoPilot can find it, streamlining resolution. Not only does Aviatrix CoPilot visualize network aspects, but it also provides security visualization components in the form of our own threat IQ, which performs security and vulnerability protection. We help our customers map the networking and security into one comprehensive visualization solution.

We also help with capacity planning for both cost with costIQ, and performance with auto right sizing and network optimization.

How does Aviatrix ensure data security and compliance across various cloud providers when integrating AI tools?

AWS and its AI engine, Amazon Bedrock, have different security requirements from Azure and Microsoft Copilot. Uniquely, Aviatrix can help our customers create an orchestration layer where we can automatically align security and network requirements to the CSP in question. For example, Aviatrix can automatically compartmentalize data for all CSPs irrespective of APIs or underlying architecture.

It is important to note that all of these AI engines are inside a public subnet, which means they have access to the internet, creating additional vulnerabilities because they consume proprietary data. Thankfully, our DCF can sit on a public and private subnet, ensuring security. Beyond public subnets, it can also sit across different regions and CSPs, between data centers and CSPs or VPC/VNets and even between a random site and the cloud. We establish end-to-end encryption across VPC/VNets and regions for secure transfer of data. We also have extensive auditing and logging for tasks performed on the system, as well as integrated network and policy with threat detection and deep packet inspection.

What future trends do you foresee in the intersection of AI and cloud computing, and how is Aviatrix preparing to address these trends?

I see the interaction of AI and cloud computing birthing incredible automation capabilities in key areas such as networking, security, visibility, and troubleshooting for significant cost savings and efficiency.

It could also analyze the different types of data entering the network and recommend the most suitable policies or security compliances. Similarly, if a customer needed to enforce HIPAA, this solution could scan through the customer’s networks and then recommend a corresponding strategy.

Troubleshooting is a major investment because it requires a call center to assist customers. However, most of these issues don’t necessitate human intervention.

Generative AI (GenAI) will also be a game changer for cloud computing. Today, a topology is a day-zero decision – once an architecture or networking topology gets built, it is difficult to make changes. One potential use case I believe is on the horizon is a solution that could recommend an optimal topology based on certain requirements. Another problem that GenAI could solve is related to security policies, which quickly become outdated after a few years. AGenAI solution could help users routinely create new security stacks per new laws and regulations.

Aviatrix can implement the same security architecture for a datacenter with our edge solution, given that more AI will sit close to the data sources. We can help connect branches and sites to the cloud and edge with AI computes running.

We also help in B2B integration with different customers or entities in the same company with separate operating models.

AI is driving new and exciting computing trends that will impact how infrastructure is built. At Aviatrix, we’re looking forward to seizing the moment with our secure and seamless cloud networking solution.

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

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