Advisory Engagement
Solve a defined governance need.
Assess maturity, establish an operating model, design a risk framework, prepare for assurance, or operationalize controls, through a defined outcome and fixed scope.
AI Governance · Advisory
Put AI into operation where accountability matters.
Stratenity AI Governance turns policy and principles into operating capability. Establish decision rights, risk tiers, controls, evidence, monitoring, and human accountability so AI can move from experimentation into governed enterprise use, with human judgment and accountability applied where consequential decisions require it.
Two ways to build governance capability
Establish it once, or maintain it continuously.
AI governance is not a one-time policy exercise. Risk, regulation, AI inventory, approvals, monitoring, and controls do not stop when a project deliverable is finished. Establish the foundation through an Engagement, and maintain it through Advisory Membership.
Advisory Membership
Maintain governance as an operating capability.
Embed ongoing AI governance around your AI portfolio: risk review, use-case governance, policy evolution, executive guidance, regulatory readiness, monitoring oversight, and governance cadence.
Six governance capabilities
Build the governance your AI operating model requires.
The governance model spans six connected capabilities. Address one defined requirement through an Advisory Engagement, combine several into a governance program, or maintain the capability continuously through Advisory Membership. Each capability produces operating controls, accountable ownership, evidence, and execution mechanisms, not policy alone.
Area 01
AI Governance Operating Model & Council
Who decides, who owns, who signs off.
Establish the governance structure that makes AI accountable: executive oversight, decision rights, accountable owners, tiered approval pathways, policy authority, escalation routes, and evidence requirements.
Decision rights · Accountability · Approval
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Area 02
AI Risk & Model Risk Management
Apply controls proportionate to AI risk.
Classify AI use cases and models by risk, evaluate material exposures, define proportionate controls, establish review and escalation requirements, and monitor the risks that matter across performance, bias, security, privacy, resilience, and human impact.
Risk tiers · Model review
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Area 03
Responsible & Ethical AI
Fair, transparent, explainable, and accountable.
Translate responsible-AI principles into measurable controls for fairness, transparency, explainability, human oversight, disclosure, and accountability, with evidence that the controls are operating as intended.
Fairness · Explainability
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Area 04
Regulatory Compliance & Assurance
Build evidence against the standards regulators and assessors expect.
Map your AI estate against relevant requirements and recognized frameworks, including the EU AI Act, NIST AI RMF, and ISO/IEC 42001 where applicable; close material gaps; and assemble the documentation, evidence, and audit trail required to support internal assurance, external assessment, and regulatory readiness.
EU AI Act · NIST · ISO 42001
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Area 05
Data Governance & Privacy for AI
Govern the data AI depends on.
Establish controls for data lineage, quality, access, provenance, privacy, retention, consent, and appropriate use across training, retrieval, inference, and AI-enabled workflows, so AI data use stays traceable, appropriate, and controlled.
Lineage · Quality · Privacy
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Area 06
AI Lifecycle Controls & Monitoring
Governance that survives production.
Embed governance across the AI lifecycle, from intake and approval through development, testing, deployment, monitoring, change control, incident management, and retirement, with versioning and provenance maintained throughout and an incident and escalation path when model performance, behavior, or risk moves outside approved thresholds.
Gates · Monitoring · Incident
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Where are you starting?
A governance maturity pathway.
The six domains are what governance covers. This is how governance is purchased and matured, from a first assessment to continuous capability.
Establish
AI activity, limited formal governance.
Governance assessment, operating model, risk tiering, and core policies.
Defined Advisory Engagement
Operationalize
Governance exists but is not embedded.
Approval gates, AI inventory, lifecycle controls, evidence architecture, and governance cadence.
Governance Program
Scale & Assure
AI operating across functions or units.
Monitoring, assurance, regulatory mapping, exceptions and incidents, and continuous governance.
Advisory Membership
| Governance need | Commercial model | Outcome |
|---|---|---|
| Governance Assessment | Fixed engagement | Current state, gaps, risk, and roadmap |
| Governance Foundation | Fixed engagement | Operating model, policies, risk tiers, controls |
| Governance Operationalization | Fixed program | Workflows, gates, evidence, monitoring, cadence |
| Governance Membership | Monthly | Continuous oversight, assurance, evolution, executive support |
Built for your complexity
The principles stay consistent. The operating surface changes.
Smaller organizations do not receive less rigorous governance. The governance principles hold; the scope of the operating surface changes with organizational complexity.
- Startup
- Fewer use cases, owners, systems, and jurisdictions.
- Small organization
- Multiple functions and more formal decision and control requirements.
- Mid-market
- Broader AI portfolio, cross-functional governance, and executive oversight.
- Enterprise
- Multiple business units, geographies, systems, risk classes, and regulatory environments.
- Nonprofit / NGO
- Mission, stakeholder, funding, privacy, and responsible-use requirements adapted to organizational complexity.
Capability-Based Governance Pricing
Priced to the estate, not the hours.
Stratenity does not price AI Governance by consultant hours or staffing levels. Pricing reflects the AI estate in scope, organizational complexity, regulatory exposure, governance maturity, and the controls required.
Available as a defined Advisory Engagement or ongoing Advisory Membership.
How we work
Assess. Design. Operationalize. Assure.
Governance starts with a clear view of your current AI estate, risk, controls, and accountability, and ends with governance operating inside the way AI is selected, built, deployed, monitored, and changed. AI-native in execution. Human-governed where accountability matters.
Assess
Establish the current AI inventory, use-case portfolio, governance maturity, accountable owners, risk exposure, control environment, and applicable requirements. Prioritize gaps by materiality and consequence, not checklist completion.
Design
Design the target governance operating model: decision rights, accountable owners, risk tiers, policies, controls, approval pathways, evidence requirements, escalation, and governance cadence.
Operationalize
Put governance into operation through approval workflows, registers, decision records, controls, monitoring, and operating cadence. Where Stratenity applications are used, governance can continue through VelorStrategy, StratenAI, OneMind Strata, and SXM-supported workflows rather than ending with a static deliverable.
Assure & Evolve
Maintain evidence, review control performance, monitor changes in the AI estate, manage incidents and exceptions, and evolve governance as technology, regulation, and organizational use change. Ongoing assurance and governance cadence can continue through Advisory Membership.
Why it holds
Governance you can evidence.
Stratenity is built around governed execution. Governance is not added after AI is deployed; it is designed into the decision, workflow, evidence, approval, and monitoring architecture from the start.
Auditable by design
Governance establishes traceability across consequential AI activity, including relevant sources, model or system context, approvals, decisions, and evidence. The level of technical logging depends on the systems and applications in scope.
Human-governed where consequential
Human oversight is applied according to risk and consequence. Higher-impact decisions, exceptions, regulated use cases, and material external outputs require defined review or approval before action.
Evidence & rationale
Consequential recommendations carry sufficient evidence, sources, assumptions, limitations, and decision rationale for the level of risk involved.
Standards-aligned
Controls can be mapped to applicable requirements and recognized frameworks, including the EU AI Act, NIST AI RMF, and ISO/IEC 42001, so governance can be evidenced in terms executives, auditors, assessors, and regulators understand.
Governed AI, deployed where it matters. Not a pilot. A capability.