AI Governance & Responsible AI
Most AI governance material is written as though the goal were to slow things down. In practice, the organisations that deploy fastest are the ones with a clear approval path, because the argument gets had once at the framework level instead of repeatedly at the project level.
We build governance that is operational: intake and triage by risk tier, controls implemented in the platform rather than in a policy document, and evidence produced automatically as systems run.
Platforms we typically deliver this on
Scope of the service
AI governance framework
Risk tiering, intake and approval workflow, roles and accountabilities, and the standards each tier must meet before deployment.
Model risk management
Inventory of AI systems in use, documented purpose and limitations, validation evidence and periodic review schedule.
Bias and fairness testing
Test design and execution across relevant population segments, with results documented in a form review bodies accept.
Privacy assessment support
Technical input to privacy impact assessments — data flows, retention, residency, de-identification and vendor sub-processing.
Security review and red teaming
Prompt injection, data exfiltration, jailbreak and tool-abuse testing against your deployed systems, with remediation.
Audit evidence and monitoring
Logging, retention and reporting designed so an auditor's questions can be answered from the system rather than reconstructed.
Where this is used
Representative engagements, described at the level our clients permit. Sector and shape are accurate; identifying detail is withheld.
Standing up an AI review board that functions
An agency's review process had become a bottleneck because every project was assessed from scratch. We introduced risk tiering with pre-approved controls, so low-risk internal tools follow a light path and high-risk public-facing systems get full scrutiny.
Outcome — Review effort concentrated where the risk actually is.
Evidence for a privacy impact assessment
A proposed system needed a PIA before deployment. We produced the data flow documentation, residency evidence, retention design and sub-processor inventory the privacy office required.
Outcome — PIA completed without the project stalling for months in clarification cycles.
Red-teaming a customer-facing assistant
Before launch we tested for prompt injection, attempts to extract other customers' data, and manipulation into giving advice outside its remit, then hardened the system against what we found.
Outcome — Failure modes found by us rather than by a customer or a journalist.
Before you get in touch
Which frameworks do you align to?
We work with NIST AI RMF, ISO/IEC 42001 and Canadian federal and provincial guidance, and map controls to whichever your organisation is accountable to rather than imposing a preferred one.
Can you govern AI we did not build?
Yes, and this is common. Much of the risk sits in tools adopted by departments directly. We inventory what is actually in use and bring it into the framework.
Does governance slow delivery?
It slows the first project and speeds up every one after, because the controls and the approval path already exist.
Often engaged alongside this
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