Future of Data Security

What if the file itself decided who gets access, carried its own encryption, and refused unauthorized requests? Georgeo X. Pulikkathara, Global CIO and CISO at iMerit Technology, sees that future arriving fast.
With 34 years in IT security and active service as a Colonel in the US Army Reserve, Georgeo breaks down 40-minute AI-enabled breaches, the flood of non-human accounts, and why human oversight remains the one control that will never go away.

Topics discussed:
34 years in IT security and Army Reserve service
iMerit Technology's AI data solutions
AI-enabled attacks and the 40-minute breach
Non-human accounts flooding enterprise networks
Human-in-the-loop oversight for AI systems
Self-protecting data with built-in encryption
Quantum-resistant encryption and post-quantum security
Competency, character, and commitment in security teams

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Gartner predicts 40% of enterprise applications will incorporate agentic AI by year's end, but Jon Rohrich is asking a more basic question: why? Leading Avanade's Canadian cybersecurity practice with nearly 30 consultants, Jon keeps watching proof of concepts succeed, only to collapse when unmodeled risks surface in production.

Jon tells Jean why zero trust is the right framework for governing AI agents, how to challenge stakeholders stuck in outdated security programs, and what a candidate's home lab reveals about their real passion for security.

Topics discussed:
Applying zero trust principles to AI agent access control
Why proof of concepts collapse at production scale
Managing AI agent identities like human user identities
Board pressure to deploy AI without proper threat modeling
Using lab environments to evaluate technical passion in hiring
Challenging long-tenured stakeholders to modernize security programs
Recognizing and overcoming imposter syndrome in cybersecurity careers

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Giving an AI agent your access does not mean giving it your judgment. The fix is data that knows the difference, so the same document a person can export, an agent can only summarize, never share externally, never train on.
 
Taylor Lobb, Chief Security Officer at Podium, tells Jean why identity now covers agents and service accounts, how visibility beats blocking shadow AI, and why every agentic workflow needs its own scoped security agent.
 
Topics discussed:
- Extending identity models to agents, APIs, and service accounts
- Making data identity-aware so access matches the requester
- Beating shadow AI with visibility instead of blocking tools
- Pairing every agentic workflow with a scoped security agent
- Why build-versus-buy has inverted for AI-native engineering teams
- Vendor value shifting from interfaces to proprietary data enrichment
- Running security as an enablement and partnership function
- Exposing tool data through MCP for internally built systems

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AI is not creating a new data problem. Leon Bian, VP and Head of Product, AI & Data Security (Databolt) at Capital One, argues it is exposing the ones enterprises already had: fragmented data, unclear ownership, weak classification, and broad access controls never designed for agents running at machine speed. At most enterprises, 80 to 90 percent of that data is unstructured, and until recently it was almost entirely outside the scope of protection.
Leon tells Jean why three controls most large organizations treat as solved fail the moment agentic AI enters the picture: strong IAM, role-based and attribute-based access, and on-behalf-of-user permissions. IAM tells you who is asking, not whether they should access specific data for a specific purpose. RBAC and ABAC tell you who and what and when, but agentic AI requires a fourth dimension: why. And when agents inherit user permissions accumulated over years, you get permission creep running at machine speed. He also lays out why frontier AI models collapsing the time from vulnerability discovery to exploitation to near zero demands a hardened data layer as the last line of defense, and why using AI to close vulnerabilities is no longer optional.
Topics discussed:
AI revealing data problems enterprises already had

Why strong IAM is a false signal of data security

RBAC and ABAC insufficient when agent context determines risk

Intent as the missing governance dimension in agentic AI

On-behalf-of-user access enabling permission creep at machine speed

Frontier models collapsing vulnerability-to-exploitation timelines to near-zero

Using AI to close vulnerabilities at machine speed

Tokenization preserving data utility and referential integrity where encryption cannot

Vivek Menon's board stopped asking about patching schedules and vulnerability counts. Their questions now center on AI risk posture, and the governance tools meant to answer them lag one to two months behind at best. Vivek, CISO and Head of Enterprise Data at Digital Turbine, tells Jean how he runs AI SOC agents that compressed a 10-person workload to 4 while holding headcount flat from this point forward.

Vivek also breaks down the agentic AI risks he tracks in active pilots: executives with the most privileged access and the most sensitive data on their laptops are the ones pushing hardest for adoption, sub-agents spawn and drift from original tasks with decreasing oversight, and an employee at his company recently downloaded a malicious tax prep skill from an AI marketplace. He frames the current cost picture as opex capping rather than opex saving, and predicts the CISO role is already converging with data trust into something closer to a Chief Data Trust Officer.
Topics discussed:
Shadow AI lapping shadow IT as top ungoverned risk

Privileged executives as high-risk AI adopters

Sub-agent spawning and diminishing task control

Malicious AI marketplace skills targeting employees

AI SOC agents compressing 10-person teams to 4

AI governance tools lagging months behind board questions

Opex capping through older models for 98% of use cases

CISO role converging into Chief Data Trust Officer

Get in touch with your host, Jean Le Bouthillier: 
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