Examples of where exposure arises
When you use AI, the exposure is yours.
These are common examples, not an exhaustive list. The relevant exposure depends on your system, how it is used, your sector and the law that applies.
Talent acquisition
RiskAn AI-powered talent acquisition pipeline screens or ranks candidates in ways that disadvantage people by gender or ethnicity.
Why it mattersYour organisation remains accountable for how it uses the model and for the hiring decisions that follow.
Regulated decisions
RiskAn AI tool recommends a supplier or outcome, but its inputs, scoring, model steps and approvals cannot be reconstructed.
Why it mattersIf the decision is regulated or challenged, your organisation must explain and defend it and satisfy the applicable record-keeping duties.
Buying an AI product
RiskThe demonstration and contract promise a capability, but the code, data dependencies, controls and live behaviour tell a more limited story.
Why it mattersWithout system-specific diligence and contract terms, you may pay for value that is not reproducible while carrying the liability, control costs and evidence gaps yourself.
AI-derived evidence
RiskYou receive an AI-generated document, prompt history, output, model artefact or code said to have produced evidence.
Why it mattersIf its provenance, completeness or result cannot be tested, you may rely on material that proves less than it appears to.
Counterparty AI use
RiskThe other side appears to have used AI, but its prompts, outputs, logs, configuration and code remain behind its systems.
Why it mattersA demand for all prompts may be refused. Technical mapping helps counsel seek the relevant material through proportionate requests under the applicable rules.