SDVOSB · Veteran-led · AI Governance practice: established Q3 2024
§ I · AI Governance
Written by people who shipped models

Your model is a liability the day it ships. Govern it accordingly.

If your buyer, your auditor, or the EU AI Act is asking: you have a date. We have a 90-second triage and a senior practitioner who has shipped the controls, not just read the standard.

EU AI Act · high-risk
Aug '26 deadline
AI governance lead
Desarie Green · AIGP, JD
Senior practitioner
48-hr SOW
The map · one program

Six frameworks. One coherent program.

We map each control once: once written, every applicable framework references the same evidence. You answer a vendor questionnaire in hours, not weeks.

Coverage
96% shared
Re-mapping
Continuous
Evidence
Single source
Auditor view
Per framework
ISO42001AIMS · since '24
NISTAI RMF 1.0+ Profiles
EUAI Act'25 tiering
AIUCAIUC-1Agent standard · ’26
OWASPLLM Top 10'25 rev
MITREATLASv4.6 · TTP map
§ II · Live triage instrument
Honest posture in 90 seconds

Triage your AI system.

Eight questions. Ninety seconds. We return the EU AI Act tier you sit in, the ISO 42001 / NIST AI RMF / SP 800-218A controls you most likely owe, and an honest verdict: including "don't engage us yet" when that's the right answer. No email gate. No follow-up sequence.
N
Nexurion Triage
AI system risk diagnostic · v1.0 · OWASP LLM 2025 · NIST AI RMF · EU AI Act
Senior practitioner only No PII collected
Question 01 / 08 Use case

What is the AI system doing for the business?

Pick the closest. The triage adapts to your answer.

Question 02 / 08Domain

Which domain does the system operate in?

Domain determines which regulatory regime applies, regardless of what you build.

Question 03 / 08Autonomy

How much can the system do without a human?

Autonomy level is the single biggest driver of risk tier under EU AI Act & ISO 42001 §8.

Question 04 / 08Inputs

What flows into the system at runtime?

Pick all that apply. Untrusted-content boundaries are scoped from this answer.

Question 05 / 08Outputs

What does the system produce?

Pick all that apply. Output type drives evaluation, monitoring, and audit trail requirements.

Question 06 / 08Identity

How does the system authenticate & act?

Non-human identity (NHI) sprawl is the most common gap we find in mid-stage AI deployments.

Question 07 / 08Buyers / regs

Who asks you about AI governance?

Pick all that apply. This determines which framework the diagnostic prioritizes.

Question 08 / 08Stage

Where are you today?

Triage's recommendation depends as much on stage as on system shape.

Verdict N-TRIAGE--
EU AI Act tier -

Calculating posture…

Synthesizing your answers against the 2025 OWASP LLM Top 10, NIST AI RMF Generative AI Profile, ISO/IEC 42001 §6–§9, and the EU AI Act risk classification.

Frameworks that apply α
    Three controls you most likely owe β
      Honest recommendation γ
      Triage v1.0 · ref OWASP LLM 2025 · NIST AI RMF GAI · ISO 42001 §6–§9 · EU AI Act
      Question 1 of 8 · Pick the closest match

      No PII required. Answers are evaluated client-side; nothing is transmitted unless you click Send to senior practitioner. Triage is a diagnostic, not a control. The verdict reflects our professional judgment under the cited frameworks and is not legal advice.

      § III · Position

      Where most AI-governance work goes wrong.

      We've inherited five programs that were already paid for. Here's what they had in common: so you can avoid spending the money twice.

      P · 01

      Policy without system inventory.

      An ISO 42001 policy is meaningless if you cannot list, on one page, the AI systems it covers. Inventory comes first: model, host, data flow, intended use, risk tier. Twenty-line registers beat sixty-page policies, every time.

      P · 02

      Risk vocabulary borrowed from information security.

      CIA triad does not cover AI risk. You need at minimum seven additional categories: confabulation, value-chain integrity, dangerous-capability emergence, NHI sprawl, prompt-injection, training-data IP, and human-oversight failure. Nexurion Field Notes, Vol. II, lays them out.

      P · 03

      Eval suite that only your ML team can read.

      If your governance evidence requires a PhD to interpret, the auditor: and your board: will not stress-test it. We translate eval output into Annex-A and Article-15 evidence a non-ML reader can sign their name to. Engineering keeps the suite; governance owns the readout.

      P · 04

      Treating agents as "just another integration."

      A tool-using agent is a non-human identity, a privileged service, a delegation chain, and an action surface: all at once. Most IAM programs cannot describe what an agent is allowed to do, when, on whose behalf, and with what audit trail. This is the gap that will define '26.

      P · 05

      Buying the platform before the program.

      Vanta-AI, Drata-AI, Credo, and the rest are useful: once you have a program for them to instrument. We've seen six-figure platform spends on companies that could not name their AI systems on the first call. Sequence: program, then platform.

      § IV · The bench
      Senior practitioners · AI governance practice

      Built by people credentialed in AI governance: not retrained for it.

      AI governance is run by named seniors with verifiable credentials. Every engagement is led by one of them, on the call, on the document.
      Desarie Green
      Privacy & AI Governance lead
      Desarie Green, JD

      Barred attorney. Global privacy leader with a product-embedded practice. Carries the AIGP: the IAPP's AI Governance Professional credential: alongside the full IAPP stack (CIPP, CIPT, CIPM, FIP). Built privacy programs revenue-adjacent in tech; carries the lawyer-grade rigor most AI governance work pretends to have.

      JD · FIP · CIPP · CIPT · CIPM · AIGP · CCEP · GISF · GLEG
      Akia Banks
      Cybersecurity & GRC
      Akia Banks, MS

      Cybersecurity and GRC practitioner. Master's in cybersecurity on a health-informatics foundation. Built GRC programs ground-up, led audit readiness through SOX and PCI, owned third-party risk and data privacy in fast-paced tech. Translates frameworks into the operating evidence auditors and regulators accept.

      MS Cybersecurity · Health Informatics · GRC program build · Audit readiness · TPR · Data privacy

      Two senior leads on every AI governance engagement.  ·  Bench expandable on senior demand  ·  See the full firm bench →

      The operating core

      One program. Every framework it answers.

      • ISO/IEC 42001
      • NIST AI RMF
      • EU AI Act
      • OWASP LLM Top 10
      • MITRE ATLAS
      • AIUC-1

      We map each control once. Inventory, risk tiering, lifecycle, agent identity, evaluation, and evidence run as one system: so a buyer questionnaire or an auditor request resolves in hours, not six disconnected framework projects.

      Six frameworks, one program.
      The Nexurion AI governance core: a central Nexurion processor surrounded by the six frameworks Nexurion reconciles, ISO/IEC 42001, NIST AI RMF, EU AI Act, OWASP LLM Top 10, MITRE ATLAS, and AIUC-1, over a dark architectural lattice.
      § V · Frameworks
      The three you'll be asked about · primers

      Three primers, written like we use them. Because we do.

      Most AI-governance content on the internet is summary-of-summary. These primers are how we actually scope the work: what each framework demands, which controls move first, and where the program tends to stall. Select one below to expand it.

      Select a framework above to read how we scope it: control map, typical timeline, and where programs tend to stall.

      ISO/IEC 42001: the AIMS standard.

      Published December 2023. The first internationally certifiable management-system standard for artificial intelligence. Plan-Do-Check-Act, but written for systems that learn.

      If you've been certified to ISO 27001, the structure will feel familiar: context, leadership, planning, support, operation, performance, improvement. The substance is different. You are governing not the data, but the model that mutates because of the data.

      Annex A defines nine control areas and roughly forty controls. The hard ones aren't the policies: they're the impact assessment (Annex B), the lifecycle controls (data, design, deployment, post-market monitoring), and the supplier-AI controls when a foundation model sits inside your product.

      We have written three full AI governance practice since the standard published. Two have certified; one is in stage-2 audit as of Q2 '26. One of a small number of US firms with that lived count.

      Our position

      Pursue ISO 42001 only when an enterprise buyer has named it, when you ship to the EU, or when your investors are asking. Otherwise: start with the AI RMF profile and build toward 42001 over twelve months. The standard is real; the rush to certify is not.

      ISO 42001 · Annex A control map 9 areas · 39 controls
      A.2AI policySenior-leadership owned, reviewed annuallyLive
      A.3Internal organizationRoles, responsibilities, reporting linesLive
      A.4ResourcesCompute, data, tooling: and humansLive
      A.5Impact assessmentPer-system, intended-use, harm taxonomyGap-typical
      A.6AI lifecycleDesign, dev, deployment, retirementLive
      A.7Data for AIProvenance, quality, labeling, driftGap-typical
      A.8Information for usersTransparency, model cards, limitationsLive
      A.9Third-party AIFoundation-model supplier controlsGap-typical
      A.10System-of-systemsAgent-to-agent & MCP boundaryOptional
      Typical timeline14–22 weeks
      EngagementFixed fee

      NIST AI Risk Management Framework: 1.0.

      Published January 2023. Voluntary. The vocabulary the rest of US AI policy is built on: and the one the EU AI Act, OMB M-24-10, and FedRAMP have all quietly adopted.

      The framework is four functions: Govern, Map, Measure, Manage: and the genius of it is that it doesn't tell you which controls to implement. It tells you which questions to be able to answer about each system you ship.

      NIST has since released the Generative AI Profile (NIST-AI-600-1) and cross-walks to ISO 42001, ISO 23894, and the EU AI Act. The profile is where the GenAI-specific risks live: confabulation, dangerous capability emergence, value chain integrity, and the eight other categories in §3.

      The AI RMF is not certifiable. It is the readiness baseline we build from on every engagement, regardless of which framework the client ultimately certifies to.

      Our position

      Every AI-touching engagement starts with an AI RMF profile, Govern controls first, then Map the systems, then Measure what you can. Most clients spend 6–10 weeks here before they decide what to certify. That sequencing saves money.

      AI RMF: function map 4 fns · 19 categories
      G.1Govern · accountabilityRoles, escalation, board reportingLive
      G.2Govern · cultureDiversity, training, raise-the-flagLive
      G.3Govern · supply chainVendor AI & weight provenanceGap-typical
      M.1Map · context & intended useSystem cards, scope statementsLive
      M.2Map · categorizationRisk tier & impact levelLive
      M.3Map · capabilities & limitsEval suite, red-team coverageGap-typical
      Ms.1Measure · methodsQuantitative + qualitative metricsLive
      Mn.1Manage · prioritizeTreatment plan, residual-risk sign-offLive
      Mn.4Manage · post-marketDrift monitoring, incident reportingGap-typical
      Profile timeline6–10 weeks
      EngagementFixed fee

      EU AI Act: Regulation (EU) 2024/1689.

      In force August 2024. Phased application through 2027. Extra-territorial: if your model is used by a person in the EU, you are in scope, no matter where you are headquartered.

      The law tiers AI systems into four risk classes: prohibited (Art. 5: social scoring, certain biometric categorisations), high-risk (Art. 6, Annex III use cases plus Annex I products), limited risk (transparency obligations under Art. 50), and minimal risk (no specific obligations).

      For high-risk systems, the obligations are substantial: risk management system, data governance, technical documentation, record-keeping, transparency, human oversight, accuracy & cybersecurity, conformity assessment, post-market monitoring, and registration in the EU database. General-purpose AI models (Art. 51–55) carry their own regime.

      Penalties top out at €35M or 7% of worldwide turnover for prohibited-practice violations, €15M or 3% for high-risk obligations, €7.5M or 1% for false information.

      Our position

      If you sell into the EU, the question is not "do we comply": it is "can we prove we comply by August 2026." Our deliverable is a tiering memo, an Article-by-Article gap analysis, and the conformity-assessment plan. Twelve weeks, fixed-fee.

      EU AI Act · obligation map Art. 9 – Art. 17 · high-risk
      Art.9Risk management systemLifecycle, iterative, documentedLive
      Art.10Data & data governanceTraining, validation, testing setsGap-typical
      Art.11Technical documentationAnnex IV: pre-market & in-lifeGap-typical
      Art.12Record-keepingAutomatic logging, Annex IV §6Live
      Art.13Transparency to usersInstructions for use, capabilitiesLive
      Art.14Human oversightDesigned-in measures · meaningfulGap-typical
      Art.15Accuracy, robustness, cyberResilience to adversarial inputsLive
      Art.16Provider obligationsQMS, CE-mark, post-market planLive
      Art.43Conformity assessmentInternal · or notified bodyGap-typical
      Tiering memo3–4 weeks
      EngagementFixed fee

      AIUC-1: the agent standard.

      Published 2026. The first standard built specifically for AI agents: refreshed quarterly, grounded in independent technical testing, and structured for enterprise adoption. Phil Venables (ex-Google Cloud CISO): "SOC 2 for AI agents."

      Developed with 100+ Fortune 500 CISOs and technical contributors including MITRE, Stanford, Cisco, ElevenLabs, OWASP, and UiPath. Operationalizes the OWASP Agentic Top 10, Cisco's AI Security Framework, and MITRE ATLAS into a certifiable control set. Schellman is the first accredited auditor; ElevenLabs the first voice-AI company to achieve certification.

      The standard is forward-looking by design: where SOC 2 is a backward-looking attestation, AIUC-1 requires forward-looking policies, ongoing adversarial testing, and at-least-quarterly retesting. Certification displays for 12 months: conditional on continued technical compliance.

      Coverage is structured across six domains: Data & Privacy, Security, Safety, Reliability, Accountability, and Society: with explicit crosswalks to ISO 42001, NIST AI RMF, EU AI Act, MITRE ATLAS, and OWASP LLM Top 10.

      Our position

      If you ship agentic AI, AIUC-1 is the standard your enterprise buyers will start asking for. ISO 42001 is the management-system layer; AIUC-1 is the operating-controls layer. We run them together: 42001 establishes the AIMS, AIUC-1 evidences that the AIMS actually does what it says.

      Six control domains
      AData & PrivacyCustomer data policies, access, IP & PII safeguardsDomain
      BSecurityAgent identity, MCP boundary, prompt-injection defenseDomain
      CSafetyForward-looking adversarial testing, eval harnessDomain
      DReliabilityPerformance, regression, output integrityDomain
      EAccountabilityLogging, replay, audit trail, governanceDomain
      FSocietyMisuse, third-party risk, societal-impact controlsDomain
      AuditorSchellman · accredited
      CadenceQuarterly retesting
      Maps toISO 42001 · ATLAS · OWASP
      First certElevenLabs · '26
      § VI · Threat model
      Nexurion threat model · revision 4 · live

      Every AI engagement opens with this table.

      Twelve threat categories, mapped to OWASP LLM Top 10, MITRE ATLAS, and the AI RMF Map function. Severity reflects the published frontier-AI literature and our own field reading: not industry vibes. We update it the morning of every Field Notes.
      IDThreatOWASPATLASSeverity
      T · 01 Prompt injection: direct & indirectAdversarial input in user message, retrieved doc, tool output, or external page that overrides system intent.Mitigation · our defaultSystem-prompt hardening, untrusted-content boundary at retrieval, output filter on tool args, prompt-injection eval suite (PromptBench-derived), and red-team in CI before each release. LLM01AML.T0051High
      T · 02 Memory & vector poisoningAdversary contaminates the RAG index, agent memory, or fine-tune set so future queries return attacker-controlled output.Mitigation · our defaultSigned retrieval corpus with provenance metadata, write-time validation against allow-listed sources, drift detection on retrieval distribution, periodic re-index from canonical source. LLM03AML.T0020High
      T · 03 NHI sprawl & over-permissionService accounts, API keys, and agent identities multiply faster than IAM can track. Most lack reviews, expiration, or scoped least-privilege.Mitigation · our defaultNHI inventory tied to AIMS register, scoped tokens with TTL, just-in-time elevation for tool calls, quarterly access reviews mapped to ISO 42001 clause 8.2. LLM02AML.T0008High
      T · 04 Tool-call & MCP abuseFunction-calling and MCP servers let a model invoke real-world actions; loose schemas and unscoped tools become a privilege-escalation path.Mitigation · our defaultStrict JSON-schema arg validation, per-tool capability scoping, human-confirm on destructive verbs, audit log of every tool call with payload hash, MCP server attestation. LLM06AML.T0046Medium
      T · 05 Sensitive data exfil via contextSystem prompt, retrieved document, or tool output exposes secrets the requester should not see: or hands them to an attacker via T-01.Mitigation · our defaultContext-redaction layer keyed to caller identity, DLP scan on tool output, system-prompt secret-scrub at deploy, output diff against allow-listed disclosure set. LLM02AML.T0024Medium
      T · 06 Model & weight supply chainProvenance of base weights, fine-tune deltas, training data, and adapters: and the SBOM-equivalent the buyer will eventually demand.Mitigation · our defaultModel-SBOM (CycloneDX-AI), signed weights with cosign attestation, training-data manifest, fine-tune lineage to base, SP 800-218A-aligned dev controls. LLM05AML.T0010Medium
      T · 07 Confabulation in regulated outputModel fabricates a citation, dose, dollar figure, or statute. The probability is not zero; the question is whether your post-hoc check catches it.Mitigation · our defaultDomain-specific eval set with ground-truth, citation-presence verifier, regulated-output review gate with human sign-off, deterministic post-processor for numerics. LLM09AML.T0048High
      T · 08 Agent loop & self-delegationAn agent recursively calls itself, spawns sub-agents, or escalates its own tool grant: without a human in the chain.Mitigation · our defaultHard recursion limit, sub-agent capability inheritance ceiling, budget cap per session, kill-switch on cost or call-count anomaly, scoped trace for every nested call. -AML.T0050Medium
      T · 09 Output-handling injectionModel output rendered in a downstream system without sanitization, XSS, SSRF, SQL injection, prompt re-injection in another LLM.Mitigation · our defaultTreat model output as untrusted by default, escape at the render boundary, ASVS-aligned output validators, second-LLM-as-judge for any output that drives action. LLM05AML.T0049Medium
      T · 10 Training-data IP & licensing exposureData the model was trained on becomes the basis of a copyright, GDPR, or trade-secret claim against you.Mitigation · our defaultTraining-data manifest with license tag, opt-out honor list, GDPR Article 22 disclosure where applicable, EU AI Act Article 53 summary maintained for foundation-model use. LLM10AML.T0016Medium
      T · 11 Human-oversight failureHuman-in-the-loop becomes human-at-the-loop: reviewer signs off without reading. Designed-in oversight that doesn't survive contact with production.Mitigation · our defaultForced-attention design (ask the reviewer the model's confidence), oversight-effectiveness metric per AI RMF MS-2.6, sample audit, escalation path with named role. --Tracked
      T · 12 Dangerous-capability emergenceModel gains capability between training runs that the previous risk assessment did not anticipate. Nexurion Field Notes tracks this monthly.Mitigation · our defaultCapability eval re-run on every model swap, frontier-AI literature watch (Field Notes), trigger-based AIMS review per ISO 42001 clause 6.1, OMB M-24-10 test alignment for federal use. -AML.T0015Tracked
      § VII · Modules
      Six engagement modules · scoped, fixed-fee

      The work, broken into six honest modules.

      Stand-alone or stacked. Each module is fixed-fee, runs the same shape, and produces evidence the next module can build on. Most clients start with the AI RMF profile, then add what their auditor or buyer asks for.
      M · 01 / Foundation6–10 wks

      AI RMF profile.

      The readiness baseline. System inventory, risk tiering, Govern controls in writing, and a Map–Measure plan you can run for the next two quarters.

      • Inventory: every AI system, model, dataset, and agent: with owner
      • Tiering memo: low / limited / high / prohibited with rationale
      • Govern controls: policy, roles, escalation, board reporting
      • Map & Measure plan: twelve-month evidence calendar
      EngagementFixed fee, written
      M · 02 / Certification14–22 wks

      ISO 42001 readiness & audit.

      Full Annex-A package, AIMS scope statement, Annex-B impact assessments, and stage-1 / stage-2 audit liaison through certificate issuance.

      • AIMS scope & SoA: 39 controls, applicability rationale
      • Annex-B AI impact assessments per system in scope
      • Lifecycle & supplier-AI control buildout
      • Stage 1 & stage 2 audit support, through certificate
      EngagementFixed fee, written
      M · 03 / Regulatory12–20 wks

      EU AI Act readiness.

      Tiering memo, Article-by-Article gap analysis for in-scope systems, technical documentation per Annex IV, and the conformity-assessment plan.

      • Tiering memo with legal-defensible rationale
      • Article 9–17 gap analysis per high-risk system
      • Annex IV technical documentation drafted
      • Post-market monitoring & serious-incident plan
      EngagementFixed fee, written
      M · 04 / Operational8–12 wks

      Agent & NHI governance.

      The 2026 problem: governing non-human identities, MCP servers, and tool-using agents. Inventory, scoping, audit trail, and the runtime guardrails.

      • Agent & NHI register: capability, scope, owner, expiration
      • Tool & MCP boundary: schema review, scoped grants
      • Action audit: every model-initiated action, replayable
      • Runtime guardrails: input, output, and tool-call policy
      EngagementFixed fee, written
      M · 05 / StrategicRetainer

      vDPO + AI governance lead.

      Embedded fractional leadership. JD-credentialed vDPO with AI-governance specialty, on retainer. Board reporting, EU representation, regulator liaison.

      • Fractional vDPO, 8 hrs/wk minimum, named individual
      • Board & investor reporting on AI posture quarterly
      • EU AI Act provider rep for non-EU companies
      • Incident-response leadership for AI-specific events
      EngagementMonthly retainer
      M · 06 / Diagnostic2–3 wks

      Inherited-program audit.

      The we-already-paid-for-this engagement. Diagnostic of an in-flight or completed AI-governance program: what's defensible, what's gap, what we'd cut.

      • Document & evidence review against current standards
      • Auditor-perspective gap memo with severity scoring
      • Remediation plan with build-vs-rebuild recommendation
      • Fixed fee · written deliverable in three weeks
      EngagementFixed fee, written
      § VIII · Nexurion Field Notes
      Quarterly · written by the practice · clients first

      Field notes from the AI-governance frontier.

      Nexurion Field Notes is our quarterly position paper. It's where we publish our updated threat model, our regulatory read, and the controls we're building before the standards body gets to them. Named for Claude Shannon, who taught us that signal is what survives the channel.

      Each issue: the revised threat model, a regulatory read across NIST / ISO / EU / OMB, the controls we're shipping before the standards bodies get to them, and the field arguments we've made to auditors and boards.

      The thirty-minute call · AI-governance scoping

      Tell us the model. We'll tell you the program.

      Thirty minutes with a senior AI-governance practitioner: someone who has shipped models, not someone who has read a slide deck about them. We'll review the system you're building, the regulators or buyers in your future, and the trigger that brought you here.

      You'll leave with a recommended module, a realistic timeline, and a fee range: even if the recommendation is "don't engage us yet."

      We'll send a written statement of work within forty-eight hours of the call, or a short memo of what we'd advise instead. No pitch deck, no nurture sequence.

      Nexurion Field Notes, Vol. I, will be in your inbox the next morning either way.

      N Before you go Get your EU AI Act tier in 90 seconds: no email required.