Snyk creates a practical guide to the growth model of AI management

The way organizations deal with AI is changing. The shift from simple models to automated agents – sometimes for the better, sometimes for the worse. To address this, good AI governance means that an organization must continuously know, control, and justify what its AI systems are doing once they are operational.

Many groups still use what is called “paper management.” This means that they have policies and structures, but these laws are not always enforced. This creates a false sense of security. To create real, effective governance, cybersecurity firm Snyk has created the “Executive Guide to Implementing and Enforcing AI Governance,” a practical roadmap for its AI governance maturity model that transforms the five steps of AI security—Discover, Assess, Defend, Govern, and Comply—into one continuous process. This program is built on three major skills: visibility, control, and accountability.

Section 1: Foundation – Visibility (Find Yourself)

Good governance begins with visibility. This means understanding all AI systems and their components. This goes beyond just models to include agents, tools, orchestration layers, and how they interact in code and pipelines. Organizations need to stop using old, static lists and start using continuous AI discovery. Old systems fail because AI is often hidden within layers of dependencies and orchestration, changing without anyone knowing.

Continuous acquisition creates a reliable system of record. This ensures that governance rules are based on what is actually being used. In practice, this means constantly scanning codebases and developer environments for AI components as soon as they are added. It is important to continuously identify “shadow AI”—models and frameworks that developers embed themselves. If organizations don’t get this AI reputation, they leave unknown systems outside the governance process, creating unmanageable risks. Visibility is the first, most important step in establishing a control base.

Phase 2: Risk Assessment – ​​Measurement (Assessment)

After gaining full visibility, organizations must measure risk consistently. Organizations should use a composite AI risk index (0–1000) to ensure that all models and applications are judged on the same criteria. This single result helps teams compare risks across different systems and set clear thresholds for what usage is acceptable.

Measurements should be based on visible signals, not just guesswork. These obvious risks include the leakage of sensitive data, agents with too many permissions to interact with tools, and the integrity of results. A test method like AI red teaming., which exposes the gap between what the system has approved and what is actually safe in production, can work. Consistent measurement helps guide future policy decisions.

Phase 3: Strengthening Operations (Protect)

Governance is only effective when policies are implemented in real-time, by embedding policy implementation across development and build-time workflows. In fast-moving environments, manual updates cannot keep up with the speed. Enforcement must be aware of the risks; when limits are exceeded, the violation should be automatically flagged or blocked. This translates static policies into effective, efficient controls.

This category, Snyk said in his report, also protects the AI ​​supply chain. Modern systems rely on MCP servers, plugins, and third-party integrations. These external sources represent a large area that can be attacked. Treating AI components as critical dependencies ensures they are validated and retested as they evolve. If this step is missed, organizations rely on outdated assumptions about system security. This enforcement is key to establishing control.

Section 4: Key Risk Controls (Governance)

The “governance” section focuses on enforcing access to less rights. Agents should have access only to the tools, data, and permissions necessary for their operations. This includes the use of scoping tools and defining clear signaling parameters. Controls must be implemented during development—when the agent’s capabilities are configured—and maintained at runtime. Runtime layers must be able to control the behavior of the agent live. Without this integrated approach, a single vulnerable agent can do more than is intended. Dominant access ensures tight control over powerful AI capabilities.

Section 5: Continuous Verification (Measurement)

The last section confirms that governance is a permanent system. AI systems are dynamic: models are updated, and new threat patterns emerge. Effective governance requires constant attention to risk signals and observations. This means that systems must be retested whenever significant changes occur, such as new dependencies or model updates. Continuous governance also focuses on preventing the disclosure of sensitive data.

This continuous evaluation ensures that the system is accountable. Along this five-stage road map, governance moves from static operations to an enabling layer. It allows decision-making to be expedited with established criteria. It enables secure adoption of high-value applications that involve sensitive data. Also, it confirms that readiness to control is a natural, built-in skill.

Snyk’s management growth model

Most organizations do not start from zero, but they are far from mandatory management. Snyk’s maturity model helps CISOs quickly assess their current state and define a path toward effective, transparent AI governance.

From the master guide:

“Evo by Snyk we use this governance model as an ongoing process. By integrating directly into developer workflows, pipelines, and runtime environments, Come on provides a real-time AI system of record that automatically discovers models, agents, tools, and dependencies as they are introduced. It allows organizations to manage risk while embedding policy enforcement directly into build pipelines.

Rather than bundling together point solutions for detection, testing, enforcement, and monitoring, Evo provides a unified approach that aligns directly with the governance lifecycle described in this guide. The result is not just better visibility or tighter control, but a system that allows organizations to continuously see, measure, and manage AI on the go.”