Building Trust Before Deployment

AI governance has a credibility problem. It sounds like compliance theatre, box ticking exercise, right up until your first model hallucination reaches a customer, an autonomous agent makes an irreversible business decision, or regulators ask questions you can’t answer.

The challenge isn’t that AI is ungovernable. The challenge I see is most organisations are trying to govern AI as an afterthought—a policy layer applied after systems are already in production. Like PCI compliance, checking what needs to be done when your already taking payments, that’s fundamentally backwards.

Why traditional governance fails for AI

Enterprise governance frameworks were built for relatively static systems with predictable behaviour. AI systems are different, they can drift over time, exhibit emergent behaviours, and create outcomes that weren’t explicitly programmed.

In 2026, the core issue for security architects isn’t “Do we have an AI policy?” It’s “Did we design for trustworthiness under real operating conditions?”

The agentic security problem

Agentic AI is moving from experimentation to mass production. We’re seeing AI systems that don’t just recommend—they act: triaging alerts, quarantining hosts, rotating credentials, negotiating access, initiating workflows, and closing tickets.

That changes the risk profile:

• Agents tend to need broader access to data and systems to be useful.
• They can make high-impact, non-reversible decisions quickly.
• They follow open-ended decision pathways that are hard to pre-model.

When an autonomous security agent isolates critical infrastructure based on a false positive, the failure is rarely “lack of policy.” It’s missing architectural guardrails: boundaries, circuit breakers, and observability designed into the system.

Architecture-first governance (governance by design)

The answer isn’t more policies. It’s designing governance into the system architecture before deployment.

What that looks like in practice:

• Data access controls and Zero Trust foundations. Every model/agent should have explicit, minimal access to the data and actions it needs—no “it’s easier if we just give it everything.”
• Explainability by design. If you can’t explain why a model produced an output (or why an agent took an action), you don’t have an operationally safe system—you have a liability.
• Continuous monitoring for drift and anomalous behaviour. Traditional security monitoring won’t reliably detect model drift, prompt injection patterns, or “slow failures” where behaviour degrades over weeks.
• Circuit breakers and human-in-the-loop triggers. Define thresholds for when the system must stop, degrade safely, or request human approval—especially for high-impact actions (network isolation, privilege escalation, data exfil prevention, financial commitments).
• Continuous compliance and auditability. Treat audit evidence as a product output: logs, decision traces, approvals, model versions, and data lineage should be available by design, not stitched together during an incident.

Governance becomes a system property, not a document.

The AI inventory you actually need

Before you can govern AI, you need to know what AI you have—and most organisations are running more than they think: embedded AI in SaaS platforms, “shadow AI” in business units, and legacy ML models nobody remembers training.

A useful AI inventory captures:

• Purpose, owner, and deployment status (pilot, prod, retired).
• Data sources and access patterns (including third parties).
• Risk classification (business criticality + regulatory exposure).
• Dependency map (what breaks if this fails or is switched off).
• Operational controls (monitoring, circuit breakers, rollback plan).
• Incident response playbooks tailored to the system’s failure modes.

This isn’t a spreadsheet exercise. It’s an architectural dependency map that reveals your real exposure.

Defence-offence asymmetry (and how to respond)

Adversaries are moving faster because they’re not burdened by governance. But the response isn’t to abandon governance—it’s to build governance that enables speed safely.

If your agents can respond in seconds because you’ve pre-defined decision boundaries, monitoring guardrails, and safe failure modes, you’re not slowing down defence. You’re accelerating it within controlled parameters.

Getting started (this quarter)

If you want a pragmatic starting point:

1. Pick one AI system already in production (or about to be).
2. Threat model it like you would any high-value service—but include AI-specific failure modes: drift, prompt injection, data leakage via outputs, over-privileged tools/actions, supply chain dependencies.
3. Implement two guardrails immediately: a circuit breaker for high-impact actions, and decision logging that makes post-incident reconstruction possible.
4. Build the AI inventory as you go; don’t wait for “complete discovery.”

The organisations that will thrive with AI in 2026 and beyond won’t necessarily have the most sophisticated models. They’ll be the ones that designed trustworthiness into their architecture from the start.