CISO Reality Check: How to Trust AI You Didn’t Build
Published:
CISOs have a new problem that doesn’t fit neatly into any traditional control framework:
How do you trust AI you didn’t build – running in clouds you don’t control – moving data through vendors you can’t fully see?
The security conversation usually starts with “model risk.” That’s real. But the bigger operational risk is often everything around the model: the API calls, the cloud hops, the third-party tooling, and the unanswered question of who moved what data, where, and under whose protection. That’s not just provenance — it’s data Chain of Custody (CoC) and accountability across the entire path.
Meanwhile, AI is already being used offensively — phishing generation, stealth malware concepts, credential stuffing optimization — and cloud-hosted AI services can be stolen, manipulated, or extracted through adversarial and model inversion techniques.
Then there’s the trust failure CISOs feel immediately in production: fabrication (“hallucination”) and deception / misalignment, including reports of in-context scheming behaviors across multiple frontier model families — an obvious problem if your analysts or workflows treat outputs as ground truth.
The uncomfortable truth: AI is now as complex as safety-critical engineered systems — yet we don’t have a DO-178B/C equivalent for AI. So trust has to be earned through verification & validation under real operating conditions, continuous observation, and clearly defined expectations.
A practical “AI Trust” posture for CISOs
Map the chain of custody: every vendor, API, cloud, and storage location your prompts and data touch.
Assume adversarial pressure: plan for prompt-level manipulation, model extraction, and AML-style evasion against AI-enabled defenses.
Treat outputs as untrusted by default: require verification steps for high-impact decisions because fabrication and deception are real operational risks.
Test like an engineer: validate tools across scenarios, track failure modes, and build trust the same way we do for other complex systems—by evidence, not vibes.
Keep it reasonable: define what “safe enough” means for your mission and environment (hospital networks don’t get the same tolerance for failure as a marketing chatbot).
Read it here: AI_Trust_for_CISOs.pdf
Final question:
If an AI-driven decision fails inside your environment tomorrow, do you have a documented chain of custody, verification method, and accountability path — or do you only have a vendor contract and a hope?