Topic starter
24/10/2026 8:50 pm
TL;DR: Financial crime vendors are collapsing machine learning, classical automation, GenAI and agentic AI into one label, which obscures very different operating models, regulatory risks and buyer expectations, according to Unit21. The real test is whether the system only summarizes work or actually executes and audits investigation steps end to end.
Editorial analysis by NHI Mgmt Group, based on content published by Unit21: “ML, AI, GenAI, Agentic AI: A field guide for buyers who are done with buzzwords”.
Key questions
Q: How should security teams evaluate whether an AI automation platform is truly agentic?
A: Test whether the system can plan, chain, and complete multi-step tasks using tools, not just generate text.
Q: Why do agentic AI systems require tighter authorisation than copilots?
A: Because they do more than generate text.
Q: What signs show that a vendor's AI is really just automation with a language layer?
A: If the system still depends on the human to gather evidence, make the decision and complete the case, then the AI is not driving the workflow.
Practitioner guidance
- Define AI capability tiers before procurement Separate scoring models, copilots, workflow automation and agentic systems in your evaluation criteria so procurement, risk and operations are assessing the same thing.
- Require identity and privilege boundaries for agents Assign each agent a discrete operating identity, limit tool access to the minimum workflow scope and log every action in an auditable trail.
- Test regulated workflows against historical outcomes Benchmark any proposed agentic system on known cases before production use, including evidence gathering, disposition logic and narrative quality.
Bottom line: The central risk is category confusion: very different systems are being marketed under the same AI label, which distorts procurement and governance decisions.
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