The way organizations manage AI is changing. The shift is from simple models to agents that act on their own — sometimes for better, sometimes for worse. To handle this, good AI governance means an organization must continuously know, control, and prove what its AI systems are doing once they are running.
Many groups still use what is called “paper governance.” This means they have policies and frameworks, but these rules are not enforced all the time. This creates a false sense of security. To create real, effective control, cybersecurity company Snyk has created an “Executive Guide to Operationalizing and Enforcing AI Governance,” an operational roadmap for its AI governance maturity model that turns the five steps of AI security—Discover, Assess, Defend, Govern, and Measure—into one continuous system. This system is built on three main abilities: visibility, control, and accountability.
Phase 1: Foundation – Visibility (Discover)
Good governance starts with visibility. This means understanding all AI systems and their parts. This goes beyond just the models to include agents, tools, orchestration layers, and how they interact in the code and pipelines. Organizations need to stop using old, static lists and start using continuous AI discovery. Old lists fail because AI is often hidden within dependencies and orchestration layers, changing without anyone knowing.
Continuous discovery builds a reliable system of record. This makes sure that governance rules are based on what is truly being used. In practice, this means regularly scanning codebases and developer environments to find AI parts as soon as they are added. It is critical to proactively identify “shadow AI”—the models and frameworks developers embed on their own. If organizations do not find this shadow AI, they leave unknown systems outside the governance process, which creates unmanaged risk. Visibility is the first, most important step to establish a base for control.
Phase 2: Risk Assessment – Measurement (Assess)
After gaining full visibility, organizations must measure risk in a consistent way. Organizations should use a unified AI risk index (0–1000) to ensure all models and applications are judged by the same criteria. This single score helps teams compare risks across different systems and set clear thresholds for what is acceptable use.
Measurement must be based on observable signals, not just on assumptions. These visible risks include leaking of sensitive data, agents with too many permissions to interact with tools, and the integrity of outputs. A testing method such as AI red teaming., which exposes the gap between what the system was approved for and what is truly safe in production, can be effective. Consistent measurement helps guide future policy decisions.
Phase 3: Operational Enforcement (Defend)
Governance becomes effective only when policies are enforced in real time, by embedding policy enforcement across development and build-time workflows. In fast-moving environments, manual reviews cannot keep pace. Enforcement must be risk-aware; when thresholds are exceeded, violations should be automatically flagged or blocked. This translates static policies into active, running controls.
This phase, Snyk said in its report, also secures the AI supply chain. Modern systems depend on MCP servers, plugins, and third-party integrations. These outside sources represent a large area that can be attacked. Treating AI components like critical dependencies ensures they are verified and re-evaluated as they evolve. If this step is missed, organizations rely on outdated assumptions about the system’s safety. This operational enforcement is key to establishing control.
Phase 4: Core Risk Controls (Govern)
The “Govern” phase focuses on enforcing least-privilege access. Agents should only have access to the tools, data, and permissions strictly necessary for their function. This includes scoping tool usage and defining clear execution boundaries. Controls must be applied during development—when agent capabilities are configured—and maintained during runtime. Runtime layers must be capable of governing the agent’s behavior live. Without this combined approach, a single compromised agent can act far beyond its intended scope. Governing access ensures tight control over powerful AI capabilities.
Phase 5: Continuous Validation (Measure)
The final phase ensures that governance is an always-on system. AI systems are dynamic: models are updated, and new threat patterns evolve. Effective governance requires constantly checking risk signals and guardrails. This means systems must be reassessed whenever important changes occur, such as new dependencies or model updates. Continuous governance also focuses on preventing sensitive data exposure.
This continuous measurement ensures the system is accountable. By aligning with this five-phase roadmap, governance moves from a static exercise to an enabling layer. It allows decision-making to speed up through standardized criteria. It enables the safe adoption of higher-value applications that involve sensitive data. And, it ensures that regulatory readiness is a natural, built-in capability.
Snyk’s governance maturity model
Most organizations are not starting from zero, but they are far from enforceable governance. Snyk’s maturity model helps CISOs quickly assess their current state and define a path toward operational, provable AI governance.
From the executive guide:
“Evo by Snyk operationalizes this governance model as a continuous system. By integrating directly into developer workflows, pipelines, and runtime environments, Evo provides a real-time AI system of record that automatically discovers models, agents, tools, and dependencies as they are introduced. It enables organizations to govern risk while embedding policy enforcement directly into build pipelines.
Rather than stitching together point solutions for discovery, testing, enforcement, and monitoring, Evo provides a unified approach that aligns directly to the governance life cycle outlined in this guide. The result is not just better visibility or stronger controls, but a system that enables organizations to continuously see, measure, and govern AI in motion.”
