What happens after the policy is written?
An AI policy can assign a reviewer. The real test is whether that person can recognize a problem, challenge a recommendation, and act before harm occurs. InfoSecured examines this connection between governance and everyday decisions: how safeguards work, where responsibility sits, and what the evidence actually shows.
Our focus: helping you ask sharper questions, examine the answers, and make the next decision on a stronger foundation.
What to verify in operation
A reviewer can pause, override, or escalate when the evidence does not support the next action.
Records connect the AI output, human reasoning, evidence reviewed, and resulting decision.
Exceptions have an owner, a response path, and a way to confirm that corrective action was completed.
AI assurance in practice
See how assurance questions change with the system, the people using it, and the consequences of getting it wrong.
What would justify confidence in this AI system?
AI assurance evaluates evidence behind claims about an AI system’s capabilities, risks, and safeguards. A useful review makes clear what has been tested, what the findings support, and what still needs attention.
InfoSecured review method
From AI claim to review decision.
Use this sequence to turn a broad assurance question into a reviewable decision. Each stage should leave a record another reviewer can understand and challenge.
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01 / Claim
What are we relying on the AI system to do?
State the capability, safeguard, or operating claim and the conditions under which you expect it to hold.
Define the assurance question → -
02 / Risk
What failure matters in this use?
Identify the failure, affected people or process, consequences, and the owner responsible for the next decision.
Map the risk domains → -
03 / Control
What safeguard should change the outcome?
Name the control, the behavior it must achieve, who operates it, and where intervention or escalation is possible.
Examine human oversight → -
04 / Evidence
What demonstrates that the claim is supported?
Examine current, traceable records showing how the system or control performed under conditions relevant to the intended use.
Browse evidence resources → -
05 / Decision
What can be approved, restricted, or left unresolved?
Record the conclusion, remaining uncertainty, required actions, accountable decision-maker, and the evidence needed next.
Structure the review →
AI assurance tools and references.
Choose a practical starting point: a general AI review workbook, a specialized AML evidence pack, or a directory of frameworks, standards, and regulations.
AI Assurance Review Kit
The AI Assurance Evidence Review Kit connects risks, controls, evidence, human oversight, and follow-up across seven Excel worksheets. Includes a companion guide.
AML AI Assurance Pack
The AML AI Assurance Evidence Pack provides a specialized workbook and supporting guides for examining alert review, escalation, decision records, and evidence references.
Frameworks & Standards
Explore AI governance frameworks, standards, and regulations by purpose and status. Follow links to authoritative sources to identify what deserves closer examination.