Enterprise AI Solutions

AI Risk & Compliance Planning

In regulated sectors, or wherever an AI system's output could affect a customer, an employee, or a regulatory obligation.

This work identifies where AI systems create regulatory, contractual, or reputational exposure, and defines the controls and evidence needed to satisfy a reviewer. Evidence is the operative word: a control nobody can demonstrate is not much use during an audit.

It also covers what to do when something goes wrong, since incident response designed during an incident is uniformly worse than incident response designed beforehand.

What the work includes

  • Mapping of AI use against applicable regulatory and contractual obligations
  • Control definitions with the evidence each one produces
  • Documentation suitable for review under NDA by customers and auditors
  • Incident response paths integrated with existing security operations
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Common questions about AI Risk & Compliance Planning.

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Anywhere a system's output could affect a customer, an employee, or a regulatory position. That means recording what the system decides, what data informed it, who can override it, and what evidence exists that the controls operate as described.

By deciding in advance what evidence would satisfy the question and ensuring the system produces it as a matter of course. Evidence assembled after a request tends to be incomplete, and gaps discovered at that point cannot be filled retrospectively.

That depends on your sector, the data involved, and where you operate — which is exactly what this work establishes. The common thread is that existing obligations on data handling and automated decisions usually apply to AI systems already, without new rules being needed.

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