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For CIOs and Heads of Unified Communications, the mandate has shifted dramatically: this time, saying “no” to AI isn’t an option. Dan Nadir, Chief Product Officer, Theta Lake told us:
“In the past, compliance teams had the luxury of being able to not allow certain technologies to be enabled. But in 2026 – that horse has left the barn. The business is already applying extreme pressure for these tools to be widely adopted”
With 99% of firms expanding AI adoption and 88% reporting governance and security challenges, the question is no longer whether to enable AI – it’s whether organizations can see and govern what happens after they do.
Beyond Guardrails: Why Access Controls Aren’t Enough
Traditional security controls – authentication, access policies, data loss prevention – were designed for a world where humans created content. But AI introduces an entirely new participant that generates summaries, drafts communications, and surfaces information across everyday workflows at unprecedented scale.
Esteban Lopez, Senior Manager of Product & Technical Marketing, Theta Lake followed up to say:
“Organizations are betting big on AI, and its success depends on the quality of data it has access to and its ability to learn through meaningful human interactions. But there’s no precedent for how humans will interact with AI, how AI will respond, or how AI-to-AI interactions will unfold. Traditional controls won’t work – they won’t scale.”
The visibility gap is stark: guardrails are preventative, but verification is still required. Once AI is enabled, policies alone cannot prove what actually happened inside AI interactions. And when firms lock down AI tools too tightly, employees simply move to personal devices and unsanctioned platforms – creating Shadow AI that compliance teams can’t see at all.
The New Risk Landscape: Behavior Over Content
With AI, governance has moved from monitoring what employees share to understanding how they behave. Real-world examples from Theta Lake’s AI inspection platform reveal the scale of the challenge:
Fabricated testimonials: Users requesting fictional customer quotes claiming 50%+ returns – constituting fraud and violating FINRA rules
Compliance testing patterns: Employees repeatedly testing AI guardrails with progressively modified requests, demonstrating knowledge that requests are improper but seeking workarounds
AI system manipulation: Attempts to manipulate AI through hypothetical scenarios, false justifications, and social engineering tactics
Promissory language: Deliberately crafted prompts requesting “ensure” and “guarantee” language in investment contexts to imply guaranteed returns
MNPI exposure: Users asking AI for extensive sensitive data including stock grants, customer SSNs, regulatory actions, and confidential project details
Nadir explained:
“You can’t look at those behaviors and not think that somebody should intercede. Even if the AI continues to say no, you still want to know that the user is trying to circumvent the rules. They have a pattern of repeated bad behavior. That’s important to know.”
This represents a fundamental shift: in traditional compliance, you either sent the problematic email or you didn’t. With AI, organizations can now see what employees are trying to do – and whether they’re successful.
A Multi-Layered Governance Model
Effective AI governance requires a structured approach that balances enablement with oversight:
Foundation layer: Understand where users are going (Copilot, ChatGPT, Grammarly, Anthropic), conduct risk assessments, invest in secure enterprise licenses, and block access to high-risk tools.
Data governance: Define permissions – do AI tools inherit the same data access as individual users, or do they require separate controls?
Baseline guardrails: Deploy structured controls for PII, PCI, and sensitive data based on user roles and context.
Continuous inspection: Capture full-fidelity records of prompts, responses, behaviors, and downstream sharing. Analyze patterns over time to surface risks that single interactions wouldn’t reveal.
Lopez goes on to say:
“Without totally locking the system down – which just forces people off-channel – true governance gives you full visibility into what your users are doing. You can see intent, reconstruct activity over time, and surface behaviors that might not trigger rules in isolation but become clear risks when viewed holistically.”
Shared Evidence, Unified Response
One of the biggest operational challenges is that AI governance spans multiple teams: UC owns deployment, Compliance owns supervision and retention, and Security owns data exposure and misuse detection. Without a shared control layer, AI risk is discovered late – during audits or incidents.
Modern AI inspection platforms integrate with existing SIEM and observability workflows, ensuring AI-related events appear alongside other security signals without creating parallel systems. This allows UC, Compliance, and Security to operate from the same evidence.
The ROI Case: Enable First, Govern What Happens Next
Organizations that deploy AI inspection report measurable outcomes within 90 days:
Faster adoption: Confidence to enable Copilot, Zoom AI Companion, and other productivity tools without “wait and see” delays
Shadow AI reduction: Sanctioned tools with governance beat unsanctioned tools with zero oversight
Regulatory defensibility: When regulators ask “how do you govern AI?”, firms have evidence – not promises
“You can’t manage what you can’t measure. The differentiator isn’t whether to enable AI – it’s whether you can see and govern AI interactions once you do. With the right inspection and governance layer, AI can be deployed confidently at scale.”
— Dan Nadir.
For CIOs navigating this landscape, the mandate is clear: enable AI, but ensure someone is watching, understanding, and governing what happens next. Because the compliance violations you can’t see are the risks that will find you first.
Ready to move from guardrails to real governance?
While you’re reading this, your competitors are figuring out how to enable AI safely – and pull ahead. The good news? You don’t have to solve this alone. Theta Lake’s team has seen thousands of real-world AI interactions across regulated industries, and they’re genuinely helpful humans who want to share what’s working (and what’s not).
Whether you’re just starting to think about AI governance or you’re knee-deep in deployment challenges, a 20-minute conversation could save you months of trial and error. Reach out to Theta Lake and let’s talk through what governance looks like in your environment – no pitch deck required.
Explore more on AI governance and compliance:
Video: AI Governance Crisis – 88% of Firms Face Challenges They Can’t Control – Deep dive with Stacey English on the data behind the crisis
Big UC Update: Inside Theta Lake’s AI Compliance Innovation with Dan Nadir – Hear Dan’s insights on what’s coming next
All Theta Lake coverage on UC Today – Stay ahead of the curve with the latest thinking
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