An effective AI governance program should begin with a usable inventory, accountable ownership, risk tiering, and a minimum control baseline that can be enforced at runtime. In the first 90 days, the AI Risk Officer should establish the governance charter, map AI systems and agents, define control requirements by risk tier, prioritize runtime controls for high-impact applications and autonomous agents, then collect evidence through control testing, reviews, exceptions, and audit-ready reporting.
Start with governance that can operate, not policy that sits unused
The 90-day plan should focus on creating a governance system that can be maintained, tested, and used by the teams responsible for AI systems and agents. The goal is not only to document policy, but to connect ownership, risk classification, control decisions, runtime enforcement, and audit evidence.
90-day AI governance priorities
Charter the program, name owners, create intake criteria, and build the initial AI inventory.
Define risk tiers, minimum controls, escalation paths, and runtime enforcement priorities.
Test controls, remediate gaps, publish dashboards, and prepare audit evidence.
Days 1-30: build the baseline and assign accountability
- Define intake criteria: Require new or changed AI use cases to disclose business purpose, owner, data access, model or agent type, autonomy level, vendor involvement, and deployment environment.
- Map AI context: Record intended purpose, usage context, affected stakeholders, benefits, potential impacts, and known risks for each priority system.
- Classify initial risk: Tier use cases by data sensitivity, business criticality, external exposure, autonomy, decision impact, regulatory exposure, and ability to take action through tools or APIs.
- Assign owners: Name accountable business and technical owners for each system, including owners for data access, model behavior, agent permissions, and operational review.
- Create a RACI: Clarify the roles of AI Risk, Security, Legal, Privacy, Compliance, Data Governance, Engineering, Procurement, Internal Audit, and business stakeholders.
Days 31-60: define the control baseline and prioritize runtime enforcement
- Identity binding: Tie AI agents and service actions to authenticated identities so requests, tool calls, and approvals can be traced.
- Least privilege: Restrict tools, APIs, credentials, environments, and data access to the minimum scope required for the task.
- Policy checks: Apply prompt, retrieval, output, and tool-call policies for sensitive information, unsafe actions, and prohibited use.
- Human approval: Require approval for high-impact, irreversible, externally visible, financial, legal, security, or data-moving actions.
- Audit logging: Capture request metadata, policy decisions, model or agent version, tool call, output or action, exception, and reviewer decision.
Enterprise AI governance architecture: connect the registry to the runtime
The target architecture for enterprise AI governance should connect management-system discipline with technical enforcement. A central registry provides the governance system of record. Runtime enforcement points apply policy where AI systems actually interact with data, models, users, tools, and downstream systems. Evidence collection links both layers so security, compliance, legal, privacy, and internal audit teams can review what happened.
The registry should not be a static spreadsheet if the organization expects AI adoption to grow. It should be maintained as a living record of systems, agents, models, data access paths, permissions, owners, risk tiers, approval status, review cadence, and exceptions. Governance reviews should update the registry whenever ownership changes, permissions expand, models or agents change, new tools are added, or risk conditions shift.
At runtime, the program should define where policy decisions are made and what happens when a request is allowed, blocked, escalated, or approved with conditions. This is particularly important for AI agent governance. Agents may receive user requests, retrieve enterprise data, reason over tasks, select tools, call APIs, interact with other agents, and generate outputs. Each of those steps can create security, privacy, integrity, or accountability risk.
Trussed AI focuses on runtime governance and security for enterprise AI agents, including areas such as runtime policy enforcement, agent identity, agent permissions, least privilege, tool approval workflows, audit logging, MCP security, and AI tool governance. Those capabilities are relevant where an enterprise needs governance controls to operate at the point of agent execution rather than only during design review.