AI governance consulting services from SDLC Corp turn AI policy into controls your teams actually use. We set up your AI inventory, risk tiers, decision rights and lifecycle gates, then the evidence, oversight and monitoring that keep them working, built around NIST AI RMF, ISO/IEC 42001 and the EU AI Act.
Proven AI Delivery
Trusted by Fortune Global 500 leaders, governments & top universities across 30+ countries
Our AI governance consulting services give business, technology, risk and compliance teams clear rules for how AI is approved, operated, changed and retired.
Structure
The policies, controls and evidence requirements that apply across your AI portfolio, from inventory and risk tiers to lifecycle gates, exceptions and monitoring.
Authority
Who makes governance decisions and how they move: decision rights, forums, delegated authority, escalation paths and evidence ownership.
Rules
Practical rules for how AI is selected, built, procured, tested, deployed and used, specific enough for delivery teams to apply. See how to manage AI usage policies.
Proportion
Classify AI use cases by business, legal, operational, security and user impact, and apply more control where impact is higher.
Models
Requirements for model selection, evaluation, approval, version changes, performance review and retirement.
Principles
Turn accountability, transparency, human oversight, privacy and fairness into operational requirements, not statements of intent.
Not every AI system needs the same controls. We classify each system by its potential impact, then match the evidence, approval and review it requires to that tier.
| Impact level | What it covers | Required controls | EU AI Act category |
|---|---|---|---|
| Low impact | AI used for limited internal assistance, where outputs are easily reviewed and errors have limited consequences. | Lightweight intake and periodic review. | Minimal or limited risk. |
| Moderate impact | AI affecting operational workflows, customer interactions or important business processes. | Defined evidence, a named owner and monitoring in production. | Limited risk, or high-risk if the use case is listed in Annex III. |
| Higher impact | AI influencing significant financial, legal, safety, employment, access or other consequential decisions. | Strongest evidence, human approval, frequent re-review and executive visibility. | High-risk (Annex III areas such as employment, credit and essential services). Prohibited practices are never deployed. |
Eight gates follow an AI system from intake to retirement, so governance does not stop at the approval to build.
Record the proposed use case, business owner, intended users and initial risk information.
Assign an appropriate governance tier and identify required reviewers.
Confirm architecture, data use, security, human oversight and evaluation requirements.
Generate the required technical and operational evidence.
Review whether defined acceptance criteria have been met.
Track quality, changes, incidents and relevant operational signals.
Re-evaluate the system when material changes occur.
Document shutdown, data handling and replacement requirements when the system leaves service.
Most governance programs stop at pre-deployment approval. The ones that hold up under audit also cover monitoring, change review and retirement.
AI governance works only when someone has the authority to decide. We assign who may approve, reject, escalate, grant exceptions and stop an AI system.
The rule
Each important governance decision has exactly one clearly accountable owner, not a committee that shares the blame. The other roles advise, review or escalate.
Owns the business outcome and accepts responsibility for how the AI is used.
Owns implementation, integration, technical operation and remediation.
Approves appropriate use of business data.
Reviews security architecture, access and technical exposure.
Reviews requirements relevant to higher-impact use cases.
Maintains governance standards, coordinates review and manages exceptions.
Handles major exceptions or decisions beyond delegated authority.
Agents may take actions rather than only generate information, so the controls have to exist before the action happens, not after.
Generative AI
A person reads the output and decides what to do with it.
AI agent
It calls tools and changes systems, so permissions, limits and traces matter.
Define exactly which applications, APIs and functions an agent can access.
State which actions are automatic and which require approval.
Ensure tool calls are tied to an appropriate user, service or agent identity.
Record important agent steps and tool usage.
Design important workflows so incorrect actions can be stopped or corrected where possible.
Define the situations where the agent must transfer control to a person.
To build agentic systems, see our Agentic AI Development Services.
Requirements differ by industry, jurisdiction and use case. We build capabilities that carry across frameworks, instead of designing around one regulation alone.
What it asks for
Organizes AI risk work into four functions: Govern, Map, Measure and Manage.
NIST AI RMFHow our work maps to it
The decision rights (Govern), inventory and risk tiers (Map), and the evidence and monitoring gates (Measure, Manage).
What it asks for
Sets requirements for an AI management system: policy, roles, risk treatment, controls and continual improvement.
ISO/IEC 42001How our work maps to it
The operating model, documented ownership and audit trail an auditor will ask to see.
What it asks for
Takes a risk-based approach: prohibited practices, strict duties for high-risk systems and transparency duties for others.
EU AI Act textHow our work maps to it
The risk classification, human approval and technical evidence required for higher-impact systems.
| Reference | Status | Source |
|---|---|---|
| EU AI Act | High-risk rules now apply from 2 Dec 2027 (Annex III) and 2 Aug 2028 (Annex I). | Gibson Dunn |
| ISO/IEC 23894 | Guidance on AI-specific risk management. | ISO |
| NIST AI 600-1 | Generative AI profile for the NIST AI RMF. | NIST |
Legal and compliance teams should determine the specific regulatory obligations that apply to your organization. This is not legal advice.
The same control system applies in every sector. High impact looks different in each one, so we set risk tiers around how the industry uses AI.
Govern model-driven recommendations, customer workflows, fraud systems and other high-impact AI applications with defined ownership and review controls.
Create clear oversight for AI used in administrative, operational and clinical-support contexts.
Establish AI inventories, accountable owners, procurement controls, review gates and transparent operating processes.
Govern predictive, vision and autonomous systems across production and operational environments.
Manage AI used across document processing, operational decisions, service and automation workflows.
Apply consistent governance across HR, finance, IT, customer service, sales and internal productivity applications.
Evidence, decision rights and review triggers working inside real AI workflows.
System baselines, TEVV evidence and acceptance decisions stay linked, and any change triggers reassessment.
Fixed policy rules, not the language model, decide when a call escalates to a person. Every action is logged.
AI document extraction with human verification and confidence scoring. Approval control stays with the finance team.
Assurio and Pulastya figures describe the published scenarios and system setup. They are not audited customer results.
Founders, CEOs and operating leaders share what it is like to build with SDLC Corp.
Eric Leist
CEO, Edgerton Strategies
Doug Schmidt
CEO, Roofaid USA
Reyzal Razmi
All Star Influencers
They've quickly understood our business context, moved fast, and brought recommendations rather than questions.
Their AI consulting expertise completely transformed how we manage and predict our logistics flow. The predictive shipping models they implemented have saved us both time and cost while improving our accuracy and customer satisfaction.
SDLC Corp delivered cutting-edge AI development services for our supply chain optimization project in the United States. Their data-driven methodology and exceptional command of artificial intelligence made a significant impact on our business operations.
Six stages that take governance from assessment to day-to-day operation.
Understand the AI portfolio, current policies, organizational structure, existing controls and governance concerns.
OutputGovernance gap summary
Create or improve the inventory of AI systems, models, vendors and important use cases.
OutputAI system inventory
Define risk tiers and classify representative systems.
OutputRisk tier definitions and classified systems
Develop governance controls, decision rights, policies, evidence requirements and operating processes.
OutputControls, decision-rights matrix and policy set
Create practical templates, review forums, handoffs and governance workflows that teams can use.
OutputTemplates, review forums and workflows
Measure governance effectiveness and refine controls as the AI portfolio changes.
OutputEffectiveness measures and review cycle
Already have an AI policy but struggle to apply it consistently? We review the operating system around the policy.
Governance works when delivery teams can follow it inside their normal workflow.
Deciding where to apply AI first? See our AI consulting services, or the full range of AI services from SDLC Corp.
Governance designed by people who have to live with it downstream.
Both Sides
Governance recommendations are informed by teams that also design and operate production AI systems.
Specific
Translate broad principles into specific controls, evidence and decision requirements.
Workable
Design governance that can operate alongside real product, engineering, security and business teams.
Certificate details and compliance documents are in the SDLC Corp Trust Center.
The published frameworks and guidance behind our approach.
FrameworkHow to structure AI inventory, risk tiers, lifecycle gates, evidence, exceptions and monitoring.
Operating ModelHow decision rights, governance forums, delegated authority and escalation turn policy into day-to-day governance.
LineageHow source-to-decision traceability supports investigations, impact analysis and governance evidence.
Create an AI governance system your business, technology and risk teams can actually operate.
From inventory and risk classification to decision rights, policies, lifecycle controls, evidence standards and production monitoring, we help turn governance requirements into repeatable organizational processes.
Straight answers on governance services, frameworks, operating models, risk tiers and how this work differs from AI consulting.
AI governance consulting services help organizations define how AI systems are inventoried, classified, approved, operated, changed, monitored and retired across the full AI lifecycle.
Typical work includes governance frameworks, AI policies, risk classification, operating models, model governance, human oversight, evidence requirements and monitoring processes.
An AI governance framework defines the rules and controls that apply across an organization's AI portfolio.
It can cover AI inventory, risk tiers, lifecycle gates, evidence standards, model and vendor controls, exceptions, monitoring and retirement.
An AI governance operating model defines how governance decisions are made in practice.
It establishes decision rights, reviewers, forums, delegated authority, escalation paths, evidence ownership and governance workflows.
Risk classification should consider the intended use, affected users, potential consequence of errors, data sensitivity, autonomy, scale, reversibility and existing human oversight.
The classification should determine the strength of required controls, so low-impact internal tools do not follow the same review process as systems influencing consequential decisions or taking autonomous actions.
Yes. If you already have AI principles or policies, the engagement can focus on inventory, risk classification, decision rights, lifecycle gates, evidence, review processes, exceptions and production monitoring.
This is the most common starting point: the policy usually exists, and what is missing is the operating system around it.
AI consulting focuses primarily on where and how AI can create business value.
AI governance consulting focuses on the policies, controls, ownership, evidence and oversight required to manage AI responsibly across its lifecycle. A generative AI consulting engagement may identify governance requirements for one initiative; this work builds the structure across all of them.
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