Responsible AI Governance

AI Governance and Compliance Services
Controls, Evidence, and Accountability Engineering Can Run

Devlyn helps product, engineering, security, legal, and compliance teams turn AI governance from policy documents into working controls. We map AI systems, classify risk, create ownership, build evidence trails, define human oversight, operationalize model cards and risk registers, and align implementation work to NIST AI RMF, ISO/IEC 42001, EU AI Act readiness, SOC 2 support, privacy obligations, and enterprise procurement expectations.

Assess our AI governance gaps See governance controls

AI inventory

Systems, use cases, owners

Audit evidence

Logs, approvals, records

Runtime controls

Gates, oversight, escalation

AI governance fails when it stays outside the delivery workflow

High-intent buyers for AI governance are usually not asking for another principle deck. They need a way to prove what AI systems exist, who owns them, what risks they carry, what controls are active, what evidence is retained, and how issues are escalated when the system changes.

What breaks

AI tools are approved informally, but there is no complete inventory of AI systems, embedded AI features, third-party AI tools, prompts, agents, datasets, or owners.

Policies exist in documents, but engineering teams cannot tell which controls apply to a release, dataset, model version, prompt change, agent tool, or high-risk workflow.

Model cards, data cards, risk registers, DPIA or FRIA notes, and approval records are created once and then become stale as the system changes.

Legal, security, product, and engineering teams use different language for the same risk, so accountability is unclear when buyers, auditors, or regulators ask for evidence.

Human oversight is described as a principle but not implemented as approval queues, escalation paths, override rights, review logs, or operator training.

How Devlyn reduces risk

We build an AI system inventory with owners, use cases, data sensitivity, model dependencies, provider exposure, risk class, lifecycle state, and review cadence.

We translate governance requirements into engineering controls: release gates, policy checks, monitoring signals, access rules, human review steps, incident workflows, and evidence capture.

We align controls to the frameworks and obligations that matter to your business, including NIST AI RMF, ISO/IEC 42001, EU AI Act readiness, SOC 2 support, privacy programs, and vendor due diligence.

We create living evidence structures: model cards, system cards, risk registers, decision records, test evidence, trace logs, change history, and management-review packs.

We leave your team with operating cadences, RACI ownership, runbooks, review templates, and governance workflows that can be maintained after handover.

What we deliver in AI governance and compliance

Every deliverable is designed to make governance observable and repeatable. The goal is not to slow AI delivery. The goal is to give buyers, internal leaders, and auditors confidence that AI systems are being managed deliberately.

01

AI system inventory and ownership

Create a register of AI systems, embedded features, models, prompts, agents, datasets, vendors, environments, owners, business purpose, and lifecycle status.

02

Risk classification and obligation mapping

Classify AI systems by use case, data sensitivity, user impact, autonomy, decision criticality, jurisdiction exposure, and applicable internal or external obligations.

03

Governance operating model

Define decision rights, review forums, RACI ownership, approval thresholds, escalation paths, change-control rules, and management-review cadence.

04

Model cards, system cards, and evidence packs

Create reusable documentation structures for intended use, limits, datasets, evaluations, risk mitigations, monitoring, human oversight, and release decisions.

05

Control implementation and policy-as-code

Embed selected controls into product and engineering workflows, including release gates, access checks, logging, review queues, human approvals, and policy enforcement where practical.

06

Audit trail and continuous evidence

Design evidence capture for approvals, model changes, prompt versions, data changes, evaluation results, incident reviews, monitoring signals, and remediation actions.

Frameworks we map into practical controls

Framework names are useful only when they become decisions in the system lifecycle. We help teams translate them into controls, owners, records, and workflows that match the actual AI product.

NIST AI RMF

Map governance work to risk management activities across govern, map, measure, and manage, including trustworthy AI considerations for design, development, use, and evaluation.

ISO/IEC 42001

Support AI management-system readiness with policies, objectives, operating processes, roles, evidence, management review, continual improvement, and AI-specific risk treatment.

EU AI Act readiness

Assess use-case classification, high-risk exposure, technical documentation needs, record keeping, data governance, transparency, human oversight, accuracy, robustness, and cybersecurity implications.

SOC 2 and security support

Connect AI controls to existing security, privacy, access, change-management, vendor-management, incident-response, and evidence workflows where they overlap.

Privacy and data governance

Review data provenance, consent basis, retention, PII exposure, access controls, retrieval boundaries, training-data use, and data-subject impact with your privacy team.

Enterprise buyer readiness

Prepare AI governance artifacts that help answer customer security questionnaires, procurement reviews, investor diligence, and responsible AI commitments.

Governance across the AI lifecycle

AI governance should cover the full path from idea intake to retirement. The riskiest gaps often appear after launch, when prompts, data, vendors, and user behavior change faster than documentation.

01

Intake and approval

Define what qualifies as an AI system, which use cases require review, what evidence is needed before build, and which stakeholders approve the work.

02

Design and data review

Document intended use, user groups, data sources, prohibited uses, privacy concerns, fairness considerations, retrieval boundaries, and expected human oversight.

03

Evaluation and release gates

Define quality, safety, robustness, hallucination, bias, latency, security, and user-impact checks before models, prompts, workflows, or agents are released.

04

Runtime monitoring

Track model behavior, failures, incidents, drift, user feedback, escalations, overrides, tool calls, and policy exceptions after launch.

05

Change management

Treat model swaps, prompt changes, data-source changes, new tools, new jurisdictions, and major feature changes as governed events with evidence.

06

Retirement and review

Close out AI systems with records for decommissioning, data handling, access removal, customer communication, and lessons learned.

How the AI governance engagement runs

The engagement starts with the systems and obligations that actually exist in your environment, then moves into controls and evidence your team can operate.

We review AI use cases, customer commitments, target markets, existing policies, audits, procurement pressure, regulatory exposure, and internal governance maturity.
Confirm scope and obligations
We identify AI systems, features, models, prompts, agents, datasets, vendors, environments, owners, risk indicators, and gaps in traceability.
Build the AI inventory
We classify systems by use case, impact, autonomy, data sensitivity, and jurisdiction, then map practical controls to the relevant frameworks and obligations.
Classify risk and map controls
We create model cards, system cards, risk registers, approval records, test evidence, management-review packs, incident workflows, and dashboard requirements.
Design evidence workflows
We support release gates, logging, oversight queues, policy checks, access controls, monitoring signals, documentation workflows, or dashboard implementation based on priority risk.
Implement priority controls
We train owners, document cadence, define review rituals, hand over templates, and leave a governance backlog for next maturity steps.
Handover the operating model

AI governance and compliance engagement models

Scoped options for teams that need clarity, implementation, or ongoing governance operations.

Readiness

Governance Gap Assessment

Best when leaders need a clear risk map and operating plan

Scoped

after discovery

AI inventory review

Framework gap map

Risk register starter

Governance roadmap

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Implementation

Operational AI Governance Program

Best for products or internal AI systems moving toward production

Scoped

after discovery

Control design

Evidence workflows

Model and system cards

Owner handover

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Ongoing

AI Governance Operating Support

Best for multi-team AI portfolios that need review cadence

Scoped

after discovery

Review rituals

Audit support

Evidence updates

Governance backlog

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Who this service is for

AI governance is most valuable when AI has moved beyond isolated experiments and now affects product behavior, business decisions, customer commitments, or regulated workflows.

01

AI products entering enterprise sales

Your customers ask for AI governance, model documentation, data handling, oversight, security, or responsible AI answers during procurement.

02

Regulated or high-impact use cases

Your AI affects employment, finance, healthcare, education, insurance, legal, safety, public-sector, or other consequential workflows.

03

Internal AI adoption at scale

Teams are using copilots, agents, automation, knowledge assistants, or third-party AI tools, but ownership and approval rules are not clear.

04

AI audit or board pressure

Leadership needs an AI inventory, risk view, evidence plan, and roadmap that can be reviewed by executives, security, legal, or auditors.

Security, legal coordination, and ownership

Governance work touches sensitive architecture, customer commitments, data flows, vendor relationships, and internal policy. We keep the engagement scoped, evidence-driven, and clear about responsibilities.

01

Scoped evidence access

We request the policies, logs, documents, architecture notes, vendor records, and product details needed for governance work, not broad access by default.

02

Legal and compliance alignment

We do not replace legal counsel. We help translate legal, privacy, security, and compliance requirements into implementation plans and evidence structures your counsel can review.

03

IP and documentation ownership

Your organization owns the governance artifacts, templates, records, runbooks, dashboards, and implementation assets created under the engagement terms.

04

Operational handover

We avoid creating a consultant-only governance model. Owners, review cadence, templates, escalation routes, and maintenance responsibilities are part of handover.

Turn AI governance into controls your team can prove

Share your AI use cases, target markets, customer governance questions, and current policies. We will help you identify the fastest path from policy intent to operational evidence.

Assess our governance gaps View AI security service

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AI inventory

Risk register

Evidence workflows

Owner handover

Frequently Asked Questions

Direct answers for teams comparing AI governance consulting, responsible AI programs, AI compliance readiness, and engineering-led implementation support.

They include AI system inventory, risk classification, control mapping, governance operating model, policy translation, evidence workflows, model cards, system cards, risk registers, oversight design, audit-trail planning, and handover documentation.

Policy consulting often stops at documents. Devlyn focuses on operational controls: release gates, logs, review workflows, ownership, evidence capture, monitoring signals, and engineering handover.

Yes. We can map your AI systems, controls, evidence, and governance cadence to NIST AI RMF concepts and help turn the framework into practical operating workflows.

Yes. We can support AI management-system readiness by helping define scope, roles, policies, objectives, risk treatment, evidence structures, internal review cadence, and continual-improvement practices.

Yes. We can help assess AI-system classification, technical documentation needs, record keeping, data governance, transparency, human oversight, accuracy, robustness, cybersecurity, and operational evidence with your legal and compliance stakeholders.

No. We coordinate with your legal and compliance advisors. Our role is to translate obligations and policies into technical controls, workflows, evidence, and implementation plans.

It is a register of AI systems, AI-enabled product features, agents, models, prompts, datasets, vendors, owners, use cases, risk classifications, environments, and lifecycle state.

They are structured artifacts that describe intended use, limitations, datasets, evaluations, monitoring, risks, mitigations, ownership, and change history for a model or AI system.

Yes. Depending on your stack, governance controls can become release gates, checklist approvals, evidence capture, policy checks, model-card updates, test evidence, and monitoring requirements.

We define where human review is required, who owns it, what information reviewers need, what override or escalation rights exist, and how review decisions are logged.

Yes. Agent governance often requires tool permission boundaries, approval steps, audit logs, loop limits, escalation rules, data-access controls, and runtime monitoring.

Useful inputs include current AI use cases, architecture diagrams, vendor lists, policies, risk registers, customer security questionnaires, audit requirements, data-flow maps, model documentation, and product roadmap context.

Typical stakeholders include product, engineering, security, legal, compliance, privacy, data, procurement, operations, and executive sponsors for higher-risk AI systems.

You own the inventories, documentation templates, evidence packs, runbooks, workflows, dashboards, decision records, and implementation artifacts created under the engagement terms.