Make AI a Managed Asset, Not a Hidden Risk

AI governance as a service is an outsourced program that gives a company everything it needs to use AI responsibly: a policy, a risk framework, an inventory of AI systems, data and security controls, employee training, and ongoing monitoring and reporting. Proven ROI builds, runs, and reports on the program so leadership has assurance without standing up an internal function from scratch.

Employees, vendors, and embedded product features are already using AI inside most companies. Without governance, that creates legal, security, brand, and compliance exposure. A formal program reduces incident risk, satisfies enterprise customers and auditors, and unlocks AI adoption with confidence rather than fear.

We align programs to the NIST AI Risk Management Framework and ISO 42001, and map controls to SOC 2, GDPR, CCPA, HIPAA, and the EU AI Act where they apply. The output is a single program that satisfies multiple obligations rather than parallel checklists.

Most clients have a published policy, an AI inventory, and an initial risk register within 30 to 60 days. A full operating program with training, monitoring, and quarterly reporting typically reaches steady state inside one quarter.

Templates are a starting point, not a program. We build the policy, but we also stand up the inventory, run the risk assessments, train your teams, monitor usage, and produce the reporting your board, customers, and auditors expect.

Engagements typically range from $4,000 to $18,000 per month based on company size, AI footprint, and regulatory exposure. We offer a free AI governance readiness review to scope the right program for your business.

Accountability stays with the human owner of the business process the AI supports, not with the model or the vendor. Our governance program assigns a named accountable owner to every AI system in your inventory, defines the decisions the AI is allowed to make autonomously versus those that require human approval, and documents the escalation path when an AI output is wrong, biased, or contested. The result is a clear chain of responsibility that satisfies internal audit, customers, and regulators.

At minimum, employees need a written acceptable use policy that covers approved tools, prohibited use cases, what data can and cannot be entered into AI systems, when AI assistance must be disclosed to customers or counterparties, when human review is required before publishing or sending AI output, and how to report suspected misuse or AI incidents. We codify all of this into a short, plain language policy plus role specific training so every team knows exactly what is allowed in their day to day work.

Protecting sensitive data inside AI workflows takes layered controls: vendor due diligence and signed data processing terms, blocking consumer AI tools from handling regulated or confidential data, prompt and output logging with PII redaction, role based access to AI systems, retention and deletion policies for prompts and embeddings, and contractual restrictions on training with your data. We design these controls around your existing security stack so AI usage stays inside the same guardrails as the rest of your business.

The right level of oversight depends on the risk tier of each use case. Low risk tasks like drafting internal copy can run with light spot checking. Medium risk tasks like customer facing content or lead scoring need defined review gates before output goes live. High risk decisions like hiring, lending, pricing, medical, or legal outputs require explicit human in the loop approval and auditable records. Our risk tiering framework makes those rules concrete for every AI system in your inventory.

AI outputs should be verified through a combination of source grounded retrieval (so answers cite real documents), structured review checklists for high risk content, factual spot audits sampled by an independent reviewer, and incident logging when hallucinations or errors are caught in production. We help build these verification loops into the workflows where AI is used, rather than relying on individual employees to catch mistakes after the fact.

Alignment starts at the policy layer: your AI policy should explicitly reference your company values, brand standards, customer commitments, and the regulations that apply to your business. From there, every approved AI use case is screened against that policy during intake, controls are mapped to specific compliance frameworks like SOC 2, GDPR, HIPAA, and the EU AI Act, and quarterly reporting ties AI usage back to business objectives like revenue, efficiency, customer experience, and risk reduction.

Effective AI monitoring covers four layers: usage telemetry (who is using which tools and how much), quality and accuracy metrics (hallucination rates, override rates, customer complaints), risk and incident tracking (policy violations, security events, near misses), and business outcome metrics (cost saved, revenue influenced, time recovered). We stand up dashboards and a quarterly governance review cadence so leadership can see how the AI program is performing and adjust policy, training, and controls based on real data instead of assumptions.