How should financial institutions evaluate AI vendors that can act autonomously?

Start by classifying the use case by authority, not by vendor category. A chatbot, a retrieval system, and an action-bearing agent carry different levels of identity risk, so the RFP should require separate controls for approval, logging, identity, and revocation at each tier.

Why This Matters for Security Teams

Financial institutions are not just buying software when they evaluate autonomous AI vendors. They are potentially onboarding a decisioning entity that can request data, call tools, trigger workflows, and expose regulated information. That changes the vendor review from a normal application assessment into an identity, authorisation, and revocation problem. Current guidance suggests treating the AI agent as an operational actor, not a passive interface, and mapping its authority before procurement closes.

The practical risk is that a vendor may look safe in a demo while hiding broad runtime behaviour in production. The AI Agents: The New Attack Surface report notes that 80% of organisations report agents have already performed actions beyond intended scope, including accessing unauthorised systems and revealing access credentials. That aligns with the control focus in the OWASP Agentic AI Top 10 and the NIST AI Risk Management Framework, both of which push organisations toward runtime governance rather than trust in vendor assurances. In practice, many security teams encounter excessive agent authority only after a workflow has already touched sensitive accounts or moved laterally into production systems.

How It Works in Practice

Evaluation should begin with the agent’s authority model. A vendor that offers a “copilot” with no tool execution has a very different risk profile from one that can open tickets, send funds, rotate secrets, or query customer records. For autonomous workloads, static RBAC alone is usually insufficient because the agent’s actions are not fixed in advance. Instead, ask whether the vendor supports intent-based or context-aware authorisation at runtime, plus explicit approval gates for high-risk actions.

Financial institutions should require evidence for four control layers:

• Workload identity: the agent must have cryptographic identity, not shared API keys.
• JIT credentials: secrets should be issued per task, short-lived, and revoked automatically when the action completes.
• Policy evaluation at request time: rules should be enforced with context, not only at onboarding.
• Complete auditability: logs must show what the agent attempted, what it accessed, and why it was allowed or denied.

The Ultimate Guide to NHIs — 2025 Outlook and Predictions is useful for framing the lifecycle question, while the CSA MAESTRO agentic AI threat modeling framework and NIST SP 800-63 Digital Identity Guidelines help teams separate identity proofing from session-level authorisation. Vendors should also be pressed on revocation: if the model, tool chain, or orchestration layer is compromised, can the institution suspend the agent immediately without breaking unrelated services? These controls tend to break down when vendors rely on long-lived service accounts, shared secrets, or opaque orchestration layers because the institution cannot prove which identity performed which action.

Common Variations and Edge Cases

Tighter runtime control often increases integration effort, latency, and procurement friction, requiring institutions to balance automation benefits against operational oversight. That tradeoff becomes especially visible when a vendor supports multiple agent tiers, such as read-only research, workflow execution, and external transaction initiation.

Best practice is evolving, but there is no universal standard for how much autonomy should be allowed in each tier. For low-risk retrieval agents, limited access and strong logging may be enough. For agents that can execute financial actions, current guidance suggests separating approval authority from execution authority, and requiring human sign-off for material changes, fund movement, or customer-impacting events. This is where the CoPhish OAuth Token Theft via Copilot Studio case is instructive, because it shows how an apparently normal workflow can be abused through token handling and overbroad permissions.

The strongest vendor responses will describe how they prevent credential reuse, lateral movement, and prompt-driven tool chaining, and they will map those safeguards to the NIST AI Risk Management Framework and the OWASP Agentic AI Top 10. In regulated environments, the edge case that matters most is not whether the vendor has AI features, but whether those features can be switched off, scoped tightly, and audited without waiting for a release cycle.

Related resources from NHI Mgmt Group

• When is it crucial to implement least-privilege access for AI agents?
• What is the difference between managed identities and hardcoded secrets for AI agents?
• Why do AI agents make non-human identity governance harder?
• Why do AI agents create new risk in non-human identity management?