Artificial Intelligence
Artificial Intelligence
We govern AI systems so they meet emerging regulatory expectations — and because our sister practice, AI Solution Development, also builds AI solutions and agents for clients, our governance advice is grounded in how these systems actually get built, not just how they're described in a vendor's compliance questionnaire. Most firms in this space only advise; we also build, which is a different kind of fluency.
AI regulation is moving fast and unevenly across jurisdictions — the EU AI Act, an expanding set of US state and federal AI laws, and a growing patchwork of country-specific rules don't yet form one coherent regulatory picture. Voluntary frameworks like the NIST AI Risk Management Framework help fill the gap in the meantime, but they're guidance, not law. We help organizations build AI governance that's durable across both — the regulations that bind you and the frameworks that inform good practice — not compliance built to satisfy whichever rule is loudest this quarter.
01
AI Governance Framework Design
Governance frameworks that define how AI systems get approved, monitored, and retired within your organization — ownership, risk tiers, and review gates that scale as you adopt more AI, rather than ad hoc case-by-case decisions.
02
AI Impact Assessments (NIST AI RMF-aligned)
Structured impact assessments for AI systems, aligned to the NIST AI Risk Management Framework, evaluating risk to individuals and the organization before deployment — the AI-specific counterpart to a DPIA.
03
AI Regulatory Compliance (EU AI Act, US State & Federal AI Law, Cross-Market)
Compliance advisory across the AI-specific regulatory landscape — EU AI Act risk-tiering and obligations, the expanding set of US state and federal AI laws, and emerging country-specific rules elsewhere — reconciled into one compliance posture for organizations deploying AI globally, informed by (but never confined to) voluntary frameworks like the NIST AI RMF.
04
Responsible / Ethical AI Advisory
Advisory on the responsible-AI questions that sit alongside strict legal compliance — transparency to users, appropriate human oversight, and use-case decisions that hold up to scrutiny beyond the letter of any single regulation.
05
Privacy-by-Design for AI/ML Development
Privacy embedded directly into AI/ML development — training data provenance, data minimization in model design, and privacy-preserving techniques — so AI systems don't become the weakest link in an otherwise sound privacy program.
06
Automated Compliance Workflow Development
We build automated workflows that operationalize compliance obligations — DSAR routing, consent enforcement, policy checks embedded in deployment pipelines — turning manual compliance processes into systems that run reliably at scale.
07
Algorithmic Bias & Fairness Assessments
Assessment of AI systems for bias and disparate impact across relevant populations, with concrete remediation guidance — a technical and regulatory risk that's increasingly a specific, named obligation rather than a general good-practice suggestion.
08
AI Vendor / Third-Party Tool Risk Assessments
Risk assessment of third-party AI tools and vendors before adoption — how they handle your data, what they claim about model behavior, and whether their compliance posture holds up — since most organizations' AI risk today comes from tools they didn't build.
09
Cross-Market AI Compliance (US/EU/UK/APAC)
AI compliance advisory for organizations deploying the same systems across the US, EU, UK, and APAC markets simultaneously, reconciling divergent AI-specific rules into a single deployment strategy instead of a market-by-market scramble.