The Discussion
AI is reshaping the landscape of enterprise security, and this webinar tackled one of the most pressing topics in the industry today: how enterprises can secure the cloud amidst AI’s exponential growth. Adrian Sanabria hosted the session, joined by Nimrod Vax, who shared expert insights on managing AI use, navigating regulatory challenges, and leveraging AI-powered tools to strengthen data governance and security.
Tailored for a targeted audience of CISOs, privacy leaders, and data professionals, the session explored solutions ranging from AI discovery and inventory management to proactive governance strategies—all in the context of AI’s rapid adoption.
Top 3 Takeaways
1. Data Security Practices Must Adapt to AI’s Pervasiveness
Enterprises can no longer treat AI as just another add-on. Nimrod Vax pointed out that AI adoption magnifies existing data hygiene challenges, making tools like DSPM (Data Security Posture Management) and DLP (Data Loss Prevention) essential for understanding and securing enterprise data systems. One striking insight? AI tools like Microsoft Copilot often expose sensitive data inadvertently during typical user queries, which is an urgent wake-up call for CISOs striving for better governance.
2. Inventory Management is Key to Mitigating Risks
Successful security programs start with deep data discovery and inventory management at scale. Nimrod introduced the concept of managing inventories not just for enterprise data, but also for AI models themselves. Knowing “who is using what AI tools, where they are located, and what data is flowing into them” lays the groundwork for efficiently mitigating AI-related risks. As Adrian emphasized, “You can’t protect what you don’t know.”
3. Align Risk Management with Trusted Frameworks
Organizations dealing with AI adoption should map risks to established frameworks like NIST or GDPR to drive proactive compliance and security programs. Nimrod underscored the importance of applying top-down and bottom-up approaches by leveraging automated tools for granularity and precision while integrating business context to support regulatory obligations. Custom guardrails and context-aware controls are becoming indispensable for enterprises deploying AI solutions.
Deep Dive: AI Discovery and Shadow AI Risks
One of the highlights of the webinar was Nimrod’s explanation of “shadow AI” and the challenge it poses for organizations—it is essentially the unauthorized or untracked use of AI tools by employees. Enterprises must prioritize AI discovery, which enables them to identify who is using what tools and ensures sensitive data isn’t exposed to generative AI models without proper security controls.
Nimrod also introduced the concept of AI TRiSM (Trust, Risk, and Security Management), which connects AI discovery to broader strategies for reinforcing compliance. He encouraged CISOs to start small by labeling sensitive data for exclusion from AI systems before scaling up to more robust solutions. “Effective data labeling is critical,” he emphasized, adding, “so are the tools that honor these labels to prevent mishandling.”
To address these risks, the pair stressed the importance of implementing modern DSPM approaches and integrating them seamlessly with AI inventory tools. This proactive risk management ensures that enterprises can stay ahead of vulnerabilities posed by generative AI technologies.
Memorable Quotes
- “Generative AI isn’t just a game-changer,it’s a productivity magnifier. But it also magnifies risks already present in your data security framework.” – Adrian Sanabria
- “Organizations face this hoarding mentality with data,keeping everything, fearing deletion. But to secure AI models, defensible disposition is critical.” – Nimrod Vax
- “The purpose of processing AI applications defines the risk profile. Custom enterprise guardrails are not negotiable if you want to mitigate those risks effectively.”– Nimrod Vax
Related Resources
- Solution Brief: Connect the Dots in Data and AI Through Governance, Context, and Control
- Whitepaper: Data Privacy in the Age of AI
- Whitepaper: Adopt Responsible AI with BigID Next
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