Introducing SailPoint’s new framework: Governing AI agents before they run wild

AI agents are entering enterprise environments faster than teams can track.

They aren’t onboarded. They don’t appear in directories. They don’t file access requests.

They just run.

One day they’re a proof of concept. The next, they’re executing workflows, writing code, querying data, and acting on behalf of the business—often with broad access and no clear ownership.

This is modern AI adoption: speed first, controls later.

AI agents use existing credentials like API keys or tokens, enabling quick deployment and easy scaling.

That speed is both the value and the risk.

Enterprise AI agents aren’t limited to low-risk tasks. They access financial data, customer records, SaaS apps, repositories, and automation. Each operates as a non-human identity (NHI) with real permissions and a real blast radius.

And that’s what most organizations miss.

AI risk isn’t just about models or prompts. It’s about access.

Every agent has an identity. Every identity has privileges. Every privilege expands your attack surface.

Here’s the truth: an AI agent’s capabilities are defined by its access. Without governing the identity behind the agent, you don’t control the AI.

That’s the gap SailPoint’s new AI framework addresses... governing AI agents through identity, ownership, and access control.

AI introduces a new class of identities

AI adoption doesn’t just add new tools. It adds new identities.

Every AI agent operates through credentials. API keys. OAuth tokens. Service accounts. Cloud roles. These identities authenticate, authorize, and act inside production systems, sometimes with more access than the humans who initiated the work.

The difference is scale and speed.

Agents are created programmatically. They execute continuously. Some persist. Others exist just long enough to complete a task and disappear. Traditional identity models were never designed for this lifecycle.

Most security programs assume identities are relatively stable. Created intentionally. Reviewed periodically. Owned by someone who can explain why they exist. AI breaks all of those assumptions.

As organizations push AI deeper into core workflows like analytics, software delivery, and customer operations, the number of non-human identities grows rapidly. Visibility degrades. Ownership becomes unclear. Access accumulates faster than it can be reviewed.

This isn’t theoretical.

AI agents already access sensitive data. They trigger downstream automation. They modify systems of record. When something goes wrong, teams end up asking familiar questions, only faster and under more pressure.

What is this identity? What does it have access to? Why does it exist? Who is responsible for it?

Security failures rarely start with malicious intent. They start with unmanaged access. AI simply accelerates the problem.

If you can’t discover an agent, you can’t govern it. If you can’t govern its access, you can’t trust its output.

That’s why securing AI starts with securing the identities it operates through.

Defining agent behavior through access

Identity isn’t just where AI security starts. It’s where it’s enforced.

Every action an AI agent takes is dictated by its access. If you don’t define those boundaries, you don’t control the outcome.

That’s why bolt-on solutions don’t scale. Governing AI requires a unified, identity-centric framework built for non-human identities.

Introducing SailPoint’s real-time AI governance and security framework

• Visibility and risk analysis: You can’t secure what you can’t see. Effective agent governance starts with full visibility into all agents across your environment. Automated discovery uncovers agents in on-prem and cloud environments, including unsanctioned shadow deployments. Once identified, their metadata must be collected for risk analysis. A comprehensive bill of materials (BOM) is essential, detailing each agent’s interactions, authority levels, accessible data, tools, and underlying model. A structured, exportable inventory allows organizations to assess criticality, sensitivity, approvals, and monitoring needs. Without a BOM, risk is immeasurable; with one, you can manage each agent’s potential impact.
• Ownership and user access management: Ownership and user access management are key to effective agent governance. Ownership assigns accountability for an agent’s behavior, access, and lifecycle, while user access management ensures access aligns with least privilege. Each AI agent should have an assigned owner at onboarding, with fallback owners or groups to prevent single points of failure. Owners approve access, review usage, and ensure agents are updated or decommissioned as needed. Ownership must transfer when roles change or employees leave to prevent orphaned agents.
  
  User access management complements ownership by tracking who can access agents and enforcing access through provisioning. This ensures agent access is as consistent and secure as other enterprise systems. Regular reviews and updates maintain proper access levels. With clear ownership and access controls, organizations can drive effective governance and lifecycle management.
• Governance and lifecycle management: Agents need governed identities to prevent identity sprawl, privilege drift, and unmanageable access. Organizations must consistently create and delete agent identities, promptly de-provisioning them when no longer needed. Certification ensures accountability by validating agent existence and regularly reviewing access. Risk-based certification policies, informed by visibility and risk signals, focus attention on high-impact agents. Standards like SPIFFE and SPIRE enable cryptographic verification, ensuring the requesting entity is the intended agent, not an imposter. Identity governance should follow the same joiner, mover, leaver principles as for people. When an agent’s scope, ownership, model, or tools change, its accounts, credentials, and policies must adapt. Audit records should capture key decisions, and reasoning traces should document the context behind agent actions, supporting compliance, investigation, and improvement.
• Real-time authorization: Static access controls can’t keep up with autonomous agents operating at machine speed across systems. Authorization must be dynamic, assessing context and policy in real-time to determine what actions an agent can take. Just-in-time (JIT) access minimizes risk by granting permissions only when needed and revoking them immediately after. Adding policy guardrails ensures elevation happens only under strict conditions, like required attestations, approvals, or acceptable risk levels. Conditional access further enhances security by continuously evaluating context, tightening or denying access if anomalies arise or sensitive data is at risk. Advanced real-time authorization should be intent-based, focusing on what the agent aims to achieve rather than just endpoint access. This reduces excess privilege and aligns policy with outcomes. However, even the best authorization requires trust and protection controls to prevent manipulation, misuse, and downstream risks.
• Trust and protection: Agents differ from other machine identities because they interpret inputs, generate outputs, and act through tools. At scale, security must address both agent behavior and their underlying components. Protection focuses on the interaction layer. Prompt security prevents injection attacks and tampering, while response security mitigates data leaks and policy violations. Guardrails limit agent actions, tool usage, and data access, while behavioral monitoring detects anomalies and policy breaches, providing critical telemetry for response.
  
  Trust ensures supply chain integrity for models and components. Foundation model security, provenance, and change tracking help organizations identify and respond to evolving risks. Together, trust and protection reduce the likelihood and impact of agent misuse. Trust safeguards against compromised components influencing behavior, while protection prevents manipulation and limits harm during system interactions.

This framework shifts teams from reactive break-glass troubleshooting to proactive, by-design governance. It reduces the odds of having to ask, “Where did this come from, and what has it broken?”

How to apply the framework
If you’re deploying AI agents today, this framework is designed to help you scale without requiring a big-bang transformation. You don’t need to do everything at once. Start with the fundamentals, then layer advanced controls as your agent footprint grows.

Start with visibility and risk analysis.
First, find your agents and understand what they touch. Identify where they operate, what systems and data they can access, and how they’re being used. From there, build a basic bill of materials and assign risk tiers based on access breadth, data sensitivity, and potential impact.

Establish ownership and user access management.
Next, assign an owner to each agent early and make it easy to see who can use it. Clear ownership creates accountability, while transparent user access helps ensure permissions stay aligned to least privilege as agents evolve.

Govern agent identities with discipline.
Treat agent identities like any other identity in your environment. Create and delete accounts deliberately. Certify access on a regular cadence. Capture audit records and decision context, especially for sensitive actions. This turns AI from an exception into something you can govern.

Move beyond static permissions.
As maturity increases, reduce standing access. Introduce just-in-time access and policy-based controls so privileges are minimal, time-bound, and tied to context rather than permanently assigned.

Focus on trust and protection.
Finally, protect the interaction layer. Reduce the risk of manipulation and data leakage, and maintain trust in the models, tools, and dependencies your agents rely on as they change over time.

If you want to put this framework into practice, start by identifying your highest-impact agents and mapping them across these five pillars. That gives you a clear, practical starting point for which controls to implement as your AI footprint scales.

Final thought: control determines outcomes

AI agents aren’t experimental anymore. They’re operational. And their numbers are growing faster than any human workforce ever could.

Organizations succeeding with AI aren’t just deploying agents. They’re governing them. They recognize that autonomy without control doesn’t create innovation. It creates risk, cost overruns, compliance exposure, and security incidents that are hard to trace and harder to explain.

The difference comes down to identity.

SailPoint’s framework is designed to put control back where it belongs: with IT, security, and identity teams. By defining agent behavior through governed access, including what an agent can touch, for how long, and under whose authority, you establish clear boundaries without slowing execution.

This isn’t about locking AI down.

It’s about making it safe to scale.

Continuous, autonomous digital workers make identity governance the difference between advantage and risk.

The agents are already here.

The only question is whether they’re operating under control… or assumption.