ai & machine learning · governance & security

Find the AI you run, rate the risk, and map controls only to the rules that apply to you.

The first governance question is simpler than it sounds. Which AI systems do we run, and who owns each one? Many teams can't answer it on the first try, because AI arrives inside SaaS products as often as it is built. We build the inventory first, then the controls.

platforms
ai-machine-learning / ai-governance 7 platforms
01 · INVENTORY 02 · RISK METHOD 03 · CONTROLS 04 · EVIDENCE AI system inventory: owner · purpose · data used AI system inventory owner · purpose · dataused Agents and identities: Entra ID · Okta · SailPoint Agents andidentities Entra ID · Okta ·SailPoint Risk assessment: NIST AI RMF · GenAI Profile Risk assessment NIST AI RMF · GenAIProfile Applicable rules: EU AI Act · Colorado AI Act Applicable rules EU AI Act · ColoradoAI Act Access scoping: least privilege · reviews Access scoping least privilege ·reviews AI security testing: prompt injection · data leakage AI security testing prompt injection ·data leakage Monitoring: drift · usage logs Monitoring drift · usage logs Evidence pack: ISO/IEC 42001 · audit · customers Evidence pack ISO/IEC 42001 · audit· customers
Anthropic ClaudeOpenAI APIMicrosoft Entra IDOkta
in short·20-second read

Governance comes down to knowing which AI systems you run, who owns them, how their risk is assessed and which rules apply.

We build the inventory first. Then come the risk assessments and controls mapped to the NIST AI RMF, ISO/IEC 42001 and the EU AI Act, while we track US state laws such as Colorado's. We also test the systems for prompt injection, data leakage and over-permissioned agents.

you probably need this if
  • Ask for a list of the AI in use, with owners and the data each one touches, and nobody can produce one.
  • There's a customer questionnaire asking about NIST AI RMF or ISO/IEC 42001, and nobody's got an answer ready.
  • You sell in the EU or Colorado, and nobody can say for sure which AI rules your products fall under.
terms on this page Prompt injectiondefinitionInstructions tucked into content an AI reads, like a document or a ticket comment, meant to make it do something its user never asked for.explained in a field note →
Ask an engineer facts checked 2026-10-20 · 9 sources
how it runs
  1. 01Inventory
  2. 02Classify
  3. 03Design controls
  4. 04Operate
you keep
  • AI system register with owners and risk ratings
  • Regulatory applicability analysis per system
  • Control set mapped to NIST AI RMF functions and ISO/IEC 42001 Annex A
+ 3 more documents
who does it
AI governance leadAI security engineerModel risk analyst
the story · ai governance & security

From the first call to a setup your own team can run.

Here's what usually prompts a call about your AI Governance & Security, what we do about it and in which order, and where you end up.

  1. chapter 01 · where it hurts

    Why teams reach out

    • Ask for a list of the AI in use, with owners and the data each one touches, and nobody can produce one.
    • There's a customer questionnaire asking about NIST AI RMF or ISO/IEC 42001, and nobody's got an answer ready.
    • You sell in the EU or Colorado, and nobody can say for sure which AI rules your products fall under.
  2. chapter 02 · what we change

    How we solve it

    • AI system inventory and ownership
    • NIST AI RMF and the Generative AI Profile
    • ISO/IEC 42001 readiness
    • EU AI Act readiness
    • US state AI laws
    • AI security testing
  3. chapter 03 · how it runs

    Our approach

    • Inventory
    • Classify
    • Design controls
    • Operate
  4. chapter 04 · where you end up

    What changes

    • There's a named owner and a documented purpose now.
    • The next questionnaire gets a real answer, a pointer to a running management system and its records.
    • We had the mark on the output before 2 December, and the tests behind the choice of method are on file for anyone who asks.
    • AI system register with owners and risk ratings
what we do

Where we spend the hours

6 areas of work, each one done by an engineer who has run it in production.

  1. We list the AI you built and the AI you bought, each with owner, purpose, data, model provider and the decisions it touches. SaaS features count too. That's where most of the unreviewed use is.

  2. We organized the controls around the RMF's Govern, Map, Measure and Manage functions. AI RMF 1.0 came out in January 2023, the Generative AI Profile (NIST AI 600-1) in July 2024, and NIST says the RMF is being revised now. We're tracking that.

  3. We build an AI management system to ISO/IEC 42001:2023. It covers policy, roles, risk and impact assessment and the Annex A controls, in good enough shape for an accredited certification body to audit.

  4. Some of the AI Act already applies. Prohibited practices since February 2025, general-purpose AI model duties since August 2025. The high-risk dates moved. The Digital Omnibus, Regulation (EU) 2026/1744, pushed Annex III obligations to 2 December 2027 and Annex I to 2 August 2028.

  5. SB 24-205 never took effect. SB 26-189, signed in Colorado on 14 May 2026, repealed it and reenacted it as an automated decision-making technology law. Its core is notice, explanation of adverse outcomes, and human review.

  6. Testing against the OWASP Top 10 for LLM Applications 2025, prompt injection, sensitive information disclosure and excessive agency among them. We also check what each agent's credentials can actually reach.

capability 01 / 06in production

AI system inventory and ownership

We list the AI you built and the AI you bought, each with owner, purpose, data, model provider and the decisions it touches. SaaS features count too. That's where most of the unreviewed use is.

capability 02 / 06in production

NIST AI RMF and the Generative AI Profile

We organized the controls around the RMF's Govern, Map, Measure and Manage functions. AI RMF 1.0 came out in January 2023, the Generative AI Profile (NIST AI 600-1) in July 2024, and NIST says the RMF is being revised now. We're tracking that.

capability 03 / 06in production

ISO/IEC 42001 readiness

We build an AI management system to ISO/IEC 42001:2023. It covers policy, roles, risk and impact assessment and the Annex A controls, in good enough shape for an accredited certification body to audit.

capability 04 / 06in production

EU AI Act readiness

Some of the AI Act already applies. Prohibited practices since February 2025, general-purpose AI model duties since August 2025. The high-risk dates moved. The Digital Omnibus, Regulation (EU) 2026/1744, pushed Annex III obligations to 2 December 2027 and Annex I to 2 August 2028.

capability 05 / 06in production

US state AI laws

SB 24-205 never took effect. SB 26-189, signed in Colorado on 14 May 2026, repealed it and reenacted it as an automated decision-making technology law. Its core is notice, explanation of adverse outcomes, and human review.

capability 06 / 06in production

AI security testing

Testing against the OWASP Top 10 for LLM Applications 2025, prompt injection, sensitive information disclosure and excessive agency among them. We also check what each agent's credentials can actually reach.

typical situations

Situations we walk into

We pieced these together from AI Governance & Security situations that come up a lot. They're patterns. You won't find a specific client in them.

case file 01

Applicant-ranking tool used for Colorado hires

composite · not a client story
  1. Beforethe situation

    Recruiters only see applicants after a vendor tool has ranked them, and some of the roles are in Colorado. Nobody has written down how the ranking works or what data feeds it. If an applicant is rejected, there's no way to ask a person to look again.

  2. What we dothe work

    The tool went into the AI register first. With your counsel we mapped it against SB 26-189 and asked the vendor for its documentation. We drafted the applicant notice and the adverse-outcome explanation, designed the human review path, and tested outcomes across applicant groups where the historical data allowed.

  3. Afterwhere you end up

    There's a named owner and a documented purpose now. Counsel decides compliance from that file, and it's reusable for EU AI Act work if hiring ever reaches the EU.

case file 02

ISO/IEC 42001 questions showing up in security questionnaires

composite · not a client story
  1. Beforethe situation

    Enterprise buyers have started asking in security questionnaires whether the product's AI features follow ISO/IEC 42001. LLM features ship monthly. There is an ISO 27001 ISMS already, but no AI policy of any kind.

  2. What we dothe work

    Rather than stand up a second management system, we extend the ISMS you have: AI scope and policy, an impact assessment process, and whichever Annex A controls apply. Each release picks up a model approval step with an evaluation record. Internal audit runs before you talk to a certification body.

  3. Afterwhere you end up

    The next questionnaire gets a real answer, a pointer to a running management system and its records. Whether to certify is now a choice about a gap you already know about.

case file 03

EU image generation and the 2 December 2026 marking date

composite · not a client story
  1. Beforethe situation

    Marketing images and copy for EU customers have come out of this product since early 2026. That puts it on the market before 2 August 2026, so under the Digital Omnibus it has until 2 December 2026 to comply with the Article 50(2) marking duty.

  2. What we dothe work

    Once counsel confirmed scope, we picked a machine-readable mark for each output format it could carry, embedded metadata or a watermark for example, and checked whether it survived typical edits and exports. The method and test results went into the technical documentation.

  3. Afterwhere you end up

    We had the mark on the output before 2 December, and the tests behind the choice of method are on file for anyone who asks.

case studies

AI Governance & Security case studies

All case studies
AI rollout AI and agents

AI pilot to production with forward deployed engineers

Taking an AI pilot into production with forward deployed engineers who work with the business team, build evals, connect real systems and hand over cleanly.

read the case study
AI rollout AI and agents

Microsoft 365 Copilot or Gemini Enterprise rollout with real adoption

Rolling out Microsoft 365 Copilot or Gemini Enterprise with oversharing fixed first, use cases tied to real tasks and adoption measured past login counts.

read the case study
AI rollout AI and agents

Classic RPA bots to agentic automation

Moving classic RPA bots to agentic automation in UiPath or Power Automate. APIs first, agents only where judgment is needed, and controls on both.

read the case study
AI rollout AI and agents

Identity, scopes and audit for AI agents and MCP servers

Giving AI agents their own identities, putting remote MCP servers behind OAuth with per-tool scopes, and logging which agent did what for whom.

read the case study
upgrade CRM, ITSM and automation

ServiceNow family upgrades, CMDB health and CSDM

Upgrading ServiceNow to the Brazil family and fixing CMDB health against CSDM 5 at the same time, so upgrades get easier and AI features read good data.

read the case study
AI rollout AI and agents

Turning on Agentforce, Now Assist, Joule and Copilot safely

Turning on vendor AI in Salesforce, ServiceNow, SAP and Dynamics 365, with permissions checked, an owner for each use case, agents tested and costs watched.

read the case study
architecture

How an AI governance program fits together from inventory to evidence

We put every AI system in the inventory with an owner, then assess its risk under the NIST AI RMF and the laws that apply. Controls like access scoping and security testing follow from the rating, and what they produce is the evidence auditors and customers ask for.

How an AI governance program fits together from inventory to evidence We put every AI system in the inventory with an owner, then assess its risk under the NIST AI RMF and the laws that apply. Controls like access scoping and security testing follow from the rating, and what they produce is the evidence auditors and customers ask for. 01 · INVENTORY 02 · RISK METHOD 03 · CONTROLS 04 · EVIDENCE each system non-human identities obligations control set control set control set review records findings logs AI system inventory owner · purpose · dataused SP Agents andidentities Entra ID · Okta ·SailPoint Risk assessment NIST AI RMF · GenAIProfile Applicable rules EU AI Act · Colorado AIAct Access scoping least privilege · reviews AI security testing prompt injection · dataleakage Monitoring drift · usage logs Evidence pack ISO/IEC 42001 · audit ·customers
Read the diagram as text
  1. AI system inventory → Risk assessment: each system
  2. Agents and identities → AI system inventory: non-human identities
  3. Applicable rules → Risk assessment: obligations
  4. Risk assessment → Access scoping: control set
  5. Risk assessment → AI security testing: control set
  6. Risk assessment → Monitoring: control set
  7. Access scoping → Evidence pack: review records
  8. AI security testing → Evidence pack: findings
  9. Monitoring → Evidence pack: logs
how it runs

A typical engagement

We don't start a step until the last one has produced something you can check.

  1. step 01

    Inventory

    Find the AI systems. Procurement records, SSO and OAuth app grants, network logs and interviews all turn some up. Then assign owners.

  2. step 02

    Classify

    A risk rating per system, and a note of which legal regimes apply. The legal reading is your counsel's to confirm.

  3. step 03

    Design controls

    We size policies, approval workflow, testing requirements and monitoring to each risk tier. Low-risk tools shouldn't be buried in paperwork.

  4. step 04

    Operate

    After launch it's ongoing work. Periodic reviews, incident handling, evidence collection, and a tracker that follows regulatory changes as they happen.

you keep

What you're left with

We write these for your AI Governance & Security setup, and they're working documents. Your team should be able to keep things running from them long after we've gone.

  • inventory01

    AI system register with owners and risk ratings

    yours to keep
  • report02

    Regulatory applicability analysis per system

    yours to keep
  • evidence03

    Control set mapped to NIST AI RMF functions and ISO/IEC 42001 Annex A

    yours to keep
  • design04

    AI acceptable-use and model approval policies

    yours to keep
  • tests05

    AI security test results and remediation plan

    yours to keep
  • document06

    Regulatory change tracker

    yours to keep
talent and delivery · ai governance & security

Certified engineers, wherever you need them.

Some clients want a project team, some want a couple of engineers on theirs. Either works. Onsite, nearshore or offshore, with handovers across time zones that keep things moving.

Onsite

Engineers at your sites in the US

Nearshore

Overlapping hours with US teams

Offshore

Overnight progress on your backlog

All time zones

Coverage handed over across time zones

24-hour coverage · US Central time
OnsiteUS business hours
Nearshoreoverlapping US hours
Offshoreovernight progress
Combinedhandover, not hand-off
roles we staff
  • AI governance lead
  • AI security engineer
  • Model risk analyst
  • Identity engineer for AI agents
  • Data protection engineer (DLP, classification)
01When do the EU AI Act's high-risk rules apply now?

Later than planned for high-risk systems. The Digital Omnibus (Regulation (EU) 2026/1744) has been in force since 27 July 2026. Annex III high-risk systems, like those used in employment or credit, now have obligations from 2 December 2027. Systems covered by Annex I product legislation follow on 2 August 2028. Article 50 transparency duties weren't postponed, though generative systems on the market before 2 August 2026 have until 2 December 2026 for the Article 50(2) marking duty.

02What happened to the Colorado AI Act?

Colorado changed it twice. SB 24-205's start date moved to 30 June 2026, and then SB 26-189, signed on 14 May 2026, repealed and reenacted the law. What's left applies to automated decision-making technology used in consequential decisions. You owe consumers notice, a plain-language explanation within 30 days of an adverse outcome and a path to human review. Developer documentation duties start 1 January 2027. The attorney general enforces it, with no private right of action.

03Is the NIST AI RMF mandatory?

No, it's voluntary. NIST publishes it for anyone to use. We'd adopt it anyway, because customers, regulators and auditors increasingly ask how you manage AI risk and the RMF gives you shared terms to answer in.

04Do we need ISO/IEC 42001 certification?

Only if customers or contracts ask for it. Plenty of organizations build the management system to the standard first and decide about certification later. An accredited certification body does the audit, and we get you ready for it.

05Do you give legal advice?

No. We map systems and controls to the regulations and work alongside your counsel, who makes the legal calls.

related

Often paired with

Projects on AI Governance & Security usually end up touching one of these, so they're worth a look.

sources 9 sources linked last checked 2026-10-20 content fingerprint 6f914fc7
  1. 01NIST: AI Risk Management Frameworknist.gov ↗
  2. 02NIST AI 600-1: Generative AI Profile (July 2024)nvlpubs.nist.gov ↗
  3. 03ISO/IEC 42001:2023iso.org ↗
  4. 04EUR-Lex: Regulation (EU) 2024/1689 (AI Act)eur-lex.europa.eu ↗
  5. 05EUR-Lex: Regulation (EU) 2026/1744 (Digital Omnibus on AI)eur-lex.europa.eu ↗
  6. 06AI Act Explorer: Digital Omnibus on AI, amended datesartificialintelligenceact.eu ↗
  7. 07Colorado General Assembly: SB26-189 Automated Decision-Making Technologyleg.colorado.gov ↗
  8. 08Colorado General Assembly: SB24-205leg.colorado.gov ↗
  9. 09OWASP Top 10 for LLM Applications 2025genai.owasp.org ↗
Reviewed by the Cohash team Product names are trademarks of their owners. how we handle facts and data →
next step

Tell us what you're running. We'll tell you what we'd do first.

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useful to bring
  • The platform and the version you're on
  • Where you need to get to, and by when
  • What's custom: rules, plugins, integrations
  • Any audit finding or deadline driving it