Take Okta's AI Readiness Assessment to score your agents' security, identify where trust breaks down, and get actionable steps to address risks.

Your AI agent environment is currently unsafe for production. Agents operate without enforceable boundaries, authentication is weak or shared, and governance is fragmented. While pilots may run, deploying agents at scale without foundational identity and security controls exposes you to data leaks, compliance violations, and operational risk. Immediate attention is needed to map agents, enforce boundaries, and implement delegation and auditability.

High Risk / Undefined

Your AI agents have some structure, but inconsistencies remain. Some boundaries are defined, identity controls exist but are coarse-grained, and lifecycle governance is partially manual. While your agents can operate in controlled scenarios, gaps leave room for unauthorized access or uncontrolled actions. Standardization and consistent monitoring are needed to move from pilot to production with confidence.

Emerging / Partial Coverage

Your AI agent environment demonstrates strong readiness. Agents operate within defined boundaries, authenticate as unique identities with least-privilege access, and follow standardized, auditable security and governance practices. You have centralized visibility and control over the lifecycle of all agents. Your focus can shift from remediation to scaling innovation safely across your organization.

Mature / Fully Governed

AI Security Risk Assessment: How Secure Are Your AI Agents? | Okta

Where AI operates and how boundaries limit risk.

Each agent operates within explicit, enforced boundaries per system

Boundaries are defined at the application or environment level

Agents operate with broadly trusted access

Partially—ownership or access is unclear

No—agents aren't consistently identified

How agents authenticate and act under enforceable rules.

As unique identities with limited, time-bound access

As dedicated service accounts per workload

Using shared service accounts or long-lived keys

Using embedded secrets or ad-hoc credentials

Checked each time against policies and context

Based on delegated user or system permissions

Whether secure practices scale as AI development accelerates.

Yes—standardized architecture and tooling are available

No—secure agents require bespoke design

Yes—delegations and approvals are standardized

How AI risk is centrally managed and contained across systems.

Impact is limited to the agent's specific permissions

Impact is limited to a single application

Your agents operate in unclear or informal boundaries. Without clear mapping and enforceable constraints, you cannot audit or govern agent actions reliably, creating exposure to unauthorized access and operational errors.

Undefined Agent Boundaries

Agents are not centrally managed, monitored, or audited. Shadow AI risk is high, and unexpected agent behavior could affect multiple systems without containment. You lack reliable processes for creation, modification, retirement, or incident response.

Fragmented Governance

Agents are using shared credentials or long-lived keys, or permissions are not properly scoped. You cannot fully trace actions to a verified identity, increasing the risk of data leakage, compliance violations, and unintentional privilege escalation.

Uncontrolled Access

Teams are building agents without consistent security patterns or delegation workflows. High-stakes actions may execute without proper checks, leaving operational, regulatory, and reputational risks unmitigated.

Ad-Hoc Security Practices

<ul><li>See the complete breakdown of your AI risk across all four pillars</li><li>Understand the exact gaps limiting your score, and why they matter</li><li>Get targeted actions based on your architecture and stage</li><li>Have a clear path from your current score to fully governed AI</li></ul>