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11/08/2026 3:11 pm
TL;DR: Generic SaaS security questionnaires do not test whether AI systems can reason over regulated data, call tools, and initiate actions on their own, which is why finance procurement needs a different lens, according to Akto. The decisive gap is assumption failure: review checkpoints and static permissions were built for non-autonomous software, not actors that can act on their conclusions mid-session.
NHIMG editorial — based on content published by Akto: How to Build an AI Security RFP for Finance in 2026
By the numbers:
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, inappropriately sharing sensitive data, and revealing access credentials.
- When AWS credentials are exposed publicly, attackers attempt access within an average of 17 minutes.
Questions worth separating out
Q: How should financial institutions evaluate AI vendors that can act autonomously?
A: Start by classifying the use case by authority, not by vendor category.
Q: Why do AI agents need special governance compared with normal applications?
A: AI agents make decisions about which tools to use and how to use them, so they can be manipulated by malicious context as well as code.
Q: What breaks when an AI vendor cannot reconstruct a single agent action?
A: Internal audit, incident review, and regulatory defence all become weaker.
Practitioner guidance
- Define autonomy tiers before issuing the RFP Separate chat, retrieval, and action-bearing use cases into distinct risk classes so identity, logging, and approval requirements scale with authority rather than with vendor category.
- Require a use-case-specific autonomy matrix Ask each vendor to show exactly which actions require human approval, which are monitored, and which are fully autonomous for your specific financial workflow.
- Test for reconstructable agent actions Make the proof-of-value scenario include a single agent action from the recent past and require the vendor to explain what triggered it, what data it used, and under what authority it acted.
What's in the full article
Akto's full article covers the operational detail this post intentionally leaves for the source:
- A finance-focused RFP question set you can adapt for regulated AI procurement.
- A practical scoring model for weighting autonomy, audit, and oversight requirements by use case.
- Examples of vendor red flags, including weak answers about model logic and agent authentication.
- Detailed mapping from AI vendor claims to financial regulatory expectations such as DORA, PCI-DSS, and GDPR.
👉 Read Akto's AI security RFP framework for financial services →
AI security RFPs for finance: are your controls autonomy-aware?
Explore further
11/08/2026 3:26 pm
Autonomy breaks the assumption that access is only risky when a person is present to act on it. Generic SaaS security models were designed for software that waits for a user to click or approve. That assumption fails when an AI system can read regulated data and then decide to use it immediately. The implication is not merely that controls need to be added. It is that procurement, IAM, and compliance are evaluating a different kind of actor altogether.
A few things that frame the scale:
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, inappropriately sharing sensitive data, and revealing access credentials, according to AI Agents: The New Attack Surface report.
- Only 52% of companies can track and audit the data their AI agents access, leaving 48% with a complete blind spot for compliance and breach investigation.
A question worth separating out:
Q: Who is accountable when an AI system in finance makes a policy-relevant decision?
A: Accountability stays with the institution, but operational ownership must be assigned to the team that can prove identity linkage, policy enforcement, and record retention. In practice, that means IAM, security, and compliance need a shared evidence model for AI use. Without it, responsibility is clear on paper but weak in execution.
👉 Read our full editorial: AI security RFPs for finance need autonomy-aware controls
