Shadow AI spreads faster than policy
Employees adopt convenient tools before ownership, approval and safe-use rules are defined.
AI Governance & Risk · AI Use Policy
Create an AI Use Policy People Can Follow — and Governance Teams Can Enforce
DataConsultant helps organisations define practical rules for workplace AI use across employees, contractors, approved tools, sensitive data, external outputs, automation and high-impact decisions. The result is a policy that connects day-to-day behaviour with governance, privacy, security, legal, HR and business ownership.
Approved tools, data boundaries and prohibited-use rules
Human review, output verification and escalation points
Privacy, security, IP, records and vendor-control alignment
Rollout guidance, AI literacy and policy-review cadence
Scope is adapted to organisation size, jurisdictions, current AI adoption, existing policies, tool landscape and required implementation support.
AI Use Policy Operating ViewIllustrative governance workflow
Policy in operation
Current AI UseTools, users, data and business activities
Policy RulesAllowed, conditional and prohibited use
ControlsTool approval, data boundaries, review
MonitoringExceptions, incidents and adoption
ImproveTraining, policy updates and governance
Policy coverageDefined
Tool approvalControlled
Human reviewRisk-based
EvidenceTraceable
Illustrative only. Actual controls, metrics and policy requirements depend on the organisation’s environment and agreed scope.
01
Why AI Use Policies Break Down in Practice
A policy fails when it is written as a legal statement but not connected to real tools, data, business processes, approvals, training and escalation routes.
Data boundaries are vague
Users do not know whether confidential, personal, client or regulated information may enter a given AI service.
Human review is undefined
Teams are told to “check AI output” without criteria for material decisions, external content or high-risk work.
Tool terms change
Retention, training use, integrations and enterprise controls can differ by plan, configuration and vendor.
Exceptions happen off-record
Urgent business needs bypass governance because there is no practical route to request, approve and document exceptions.
Move From Informal AI Rules to a Policy Your Teams Can Use
Start with the tools, data categories, employee behaviours and risk decisions that need clear enterprise guidance.
02
From Uncontrolled AI Use to a Managed Policy Target State
The engagement connects policy language to operating controls, ownership and evidence so that approved AI use can scale without depending on ad hoc judgment.
Typical Current State
- AI tools adopted without common approval
- Acceptable-use rules do not mention generative AI
- Sensitive-data guidance is inconsistent
- Human review expectations vary by team
- AI incidents and exceptions are not recorded
- Policy ownership is fragmented across functions
Managed Target State
- Clear allowed, conditional and prohibited use
- Approved-tool and vendor-control requirements
- Data classifications mapped to AI use
- Risk-based human review and escalation
- Defined incidents, exceptions and records
- Named ownership, training and review cadence
03
What the AI Use Policy Can Cover
Scope is designed around the organisation’s real AI usage patterns, data classes, workforce, customer obligations, legal environment and risk appetite.
Policy foundation
- Purpose and scope
- Definitions and roles
- Principles and accountability
- Policy ownership and review
Data & confidentiality
- Personal and client data
- Confidential information
- Classified or regulated data
- Redaction and approved environments
Human oversight
- Output verification
- High-impact decisions
- External communications
- Escalation and sign-off
Security & tool use
- Approved AI services
- Credentials and access
- Connected tools and agents
- Vendor terms and integrations
Risk & exceptions
- Prohibited uses
- Restricted use cases
- Incident reporting
- Exception workflow
IP & content
- Copyright and licensing
- Source attribution
- Brand and factual review
- Third-party content
Software & code
- AI-generated code review
- Secrets and repositories
- Security validation
- Open-source and licensing checks
Workforce guidance
- Employee responsibilities
- Contractor expectations
- Role-based AI literacy
- Examples and quick-reference rules
Monitoring & evidence
- Tool register linkage
- Policy exceptions
- Incidents and trends
- Review evidence
Continuous improvement
- Policy review triggers
- Vendor and regulatory change
- Lessons from incidents
- Feedback and adoption metrics
Align AI Data Boundaries, Tool Approval and Human Review
Translate broad responsible-AI principles into usable controls for the way people actually work with copilots, chatbots, assistants, models and agents.
04
The AI Use Policy Operating System
Policy works when people, content, processes, technology and monitoring reinforce the same rules and decision rights.
AI Use Policy
Operating System
Operating System
PeopleEmployees, leaders, legal, privacy, security, HR, AI owners and business teams
Policy ContentRules, examples, data boundaries, approved tools, prohibited and conditional use
ProcessesTool requests, exceptions, incidents, approvals, policy updates and evidence
TechnologyIdentity, access, enterprise AI controls, DLP, logging, catalogue and security tooling
GovernanceOwnership, decision rights, escalation and risk acceptance
MonitoringAdoption, exceptions, incidents, tool changes and policy effectiveness
Illustrative AI Use Policy Maturity Assessment
| Dimension | Current | Target | Gap |
|---|---|---|---|
| Policy ownership | 2 | 4 | |
| Approved tool governance | 1 | 4 | |
| Data-use rules | 2 | 5 | |
| Human oversight | 2 | 4 | |
| Prohibited-use coverage | 2 | 5 | |
| Exception management | 1 | 4 | |
| Incident linkage | 2 | 4 | |
| Training & literacy | 2 | 4 | |
| Monitoring & reporting | 1 | 4 |
Illustrative maturity view. Scores shown are examples only and are not DataConsultant client results.
05
Map Business Activity to AI Use, Data and Controls
A usable policy should tell people what changes when the business context, data sensitivity, audience or AI tool changes.
1. Business Activity
Draft, analyse, code, research, summarise, decide or automate
2. AI Tool
Approved enterprise tool, public service, embedded feature or agent
3. Data Class
Public, internal, confidential, client, personal or restricted
4. User Role
Employee, contractor, developer, analyst, HR, legal or executive
5. Affected Party
Internal user, customer, candidate, employee, supplier or public audience
6. Output Type
Internal draft, external content, code, recommendation or decision support
7. Review Level
User review, specialist review, manager sign-off or formal approval
8. Record
No record, retained prompt/output, approval evidence or exception record
9. Escalation
Security, privacy, legal, HR, risk, AI governance or business owner
10. Outcome
Allowed, conditional, restricted, prohibited or approved by exception
Operationalise the Policy With Clear Requests, Exceptions and Evidence
Turn policy statements into intake, approval, escalation and monitoring workflows that fit your current operating model.
06
Target AI Use Policy Operating Model and Service Workflow
Define who owns the policy, who approves AI tools and exceptions, who advises on specialist risks and how policy issues are triaged.
Target Operating Model
Executive Sponsor / Accountable Leadership
AI Governance Owner / Policy Owner
Business OwnersUse-case accountability and acceptance
AI / TechnologyTool capability, configuration and integration
Privacy / SecurityData, access, security and incident controls
Legal / Risk / HRContracts, regulation, workforce and risk advice
AI Use Policy Service Desk / Workflow
1. IntakeSubmit policy, tool, data or use-case question
2. ClassifyIdentify request type, data, user and risk context
3. RouteAssign business, AI, privacy, security or legal owner
4. AssessReview policy conditions, vendor terms and controls
5. DecideAllow, restrict, prohibit or approve by exception
6. RecordCapture decision, rationale, evidence and expiry
7. MonitorTrack incidents, exceptions and policy-change signals
07
Technology Enablement, Monitoring and Policy Reporting
The policy does not require a specific technology stack, but it should connect to the systems that can support access, approved tools, data protection, evidence and monitoring.
Policy Technology Enablement
Identity & Access
SSO / IAM
RBAC / groups
MFA
Privileged access
AI Platforms
Enterprise copilots
Approved LLMs
AI gateways
Agent platforms
Data Protection
DLP / classification
Encryption
Redaction
Data access controls
Evidence
Tool inventory
Exceptions
Incidents
Training records
Enterprise controls: security · privacy · access · retention · approved tools · monitoring · policy enforcement
Control Monitoring & Service Reporting
92%training completion
84%approved-tool coverage
12open policy requests
3active exceptions
Request ageing
- 0–7 days: 7
- 8–30 days: 4
- 30+ days: 1
Policy themes
- New tool approval
- Confidential data
- External content
Change triggers
- Vendor terms
- New regulation
- Incident lessons
Illustrative measures only. Metrics and reporting cadence are defined during implementation.
08
Prioritise Policy Gaps and Transition to Managed AI Use
Not every gap has the same urgency. Prioritisation should reflect business criticality, data sensitivity, affected people, regulatory exposure, frequency of use and effort to remediate.
Policy Remediation Matrix
Quick controlsHigh-value policy clarifications, approved-tool guidance and immediate data boundaries.
Strategic controlsHigh-impact use cases, agent permissions, human oversight and regulated workflows.
Lower priorityRare low-impact use where current controls already reduce material exposure.
Larger programmesComplex vendor, data, security or operating-model changes requiring coordinated delivery.
Transition to Managed AI Use
1. DiscoverMap current AI use
2. DraftDefine policy rules
3. ValidateReview with stakeholders
4. LaunchCommunicate and train
5. OperateRun requests and exceptions
6. ImproveMonitor and refresh
The path can be compressed for a focused policy refresh or expanded into a broader AI governance programme.
Connect Your AI Use Policy to Recognised Governance and Risk References
Use standards and regulatory context as reference points without turning the employee policy into an unreadable compliance manual.
09
Standards, Regulation and Security Reference Points
The policy can be mapped to relevant frameworks and obligations where useful. Mapping is scoped to the organisation’s role, jurisdiction and use cases and does not constitute legal advice, certification or a formal conformity assessment.
NIST AI RMF
Use governance, risk mapping, measurement and management concepts to shape accountability, risk-based policy rules and evidence.
View NIST AI RMF ↗NIST GenAI Profile
Use the GenAI risk profile to inform policy treatment of generative AI risks such as misuse, harmful outputs, privacy, security and human oversight.
View NIST GenAI Profile ↗ISO/IEC 42001
Align policy ownership, objectives, processes and continual improvement with an AI management-system perspective where relevant.
View ISO/IEC 42001 ↗EU AI Act
Where applicable, connect user-facing rules to organisational obligations including AI literacy, prohibited practices and risk-based governance.
View EU AI Act ↗India DPDP Rules 2025
Coordinate AI policy data-handling rules with the organisation’s privacy obligations for digital personal data in India where applicable.
View MeitY source ↗OWASP GenAI Security
Use current security risk guidance to inform safe tool use, sensitive information handling, prompt injection awareness and output handling.
View OWASP 2026 ↗10
Delivery Methodology and Tangible Deliverables
The engagement is designed to create a usable policy package, not just a document. Deliverables are tailored to the chosen scope and current governance maturity.
Delivery Methodology
UnderstandStakeholders, AI use and risk appetite
AssessTools, data, policies and gaps
DraftRules, examples and role guidance
ValidateLegal, privacy, security and business review
EnableRollout, training and workflows
ImproveReview cadence and change triggers
Tangible Deliverables
- AI use policy document
- Executive policy summary
- Employee quick-reference guide
- Allowed / conditional / prohibited use matrix
- Data classification to AI-use mapping
- Approved-tool governance criteria
- Human-review and escalation rules
- AI exception request template
- AI incident reporting guidance
- Tool/vendor review checklist
- Role and responsibility matrix
- AI literacy / briefing material
- Policy rollout plan
- Policy review and change log
- Implementation backlog
- Governance handover pack
11
Engagement and Commercial Guidance
DataConsultant pricing is scope-led. Public India market examples show wide variation because a short acceptable-use policy, a multi-entity policy pack and an enterprise governance programme are not equivalent services.
AI Use Policy Review
For an organisation that already has policy material and needs a targeted gap review, refresh and implementation recommendations.
DataConsultant priceRequest a Quote
- Current policy review
- Key stakeholder interviews
- Priority gaps and edits
- Implementation recommendations
AI Use Policy Development
For organisations that need a new, practical enterprise AI use policy with clear rules, examples and governance linkage.
DataConsultant priceRequest a Quote
- Discovery and tool/use mapping
- Policy drafting and validation
- Data and human-review rules
- Employee-facing guidance
Policy + Rollout Controls
For teams that need the policy plus intake, exceptions, tool approval, training and monitoring design.
DataConsultant priceRequest a Quote
- Policy package
- Tool approval workflow
- Exception and incident linkage
- Rollout and training plan
Managed Policy Governance
For organisations that need recurring policy review, request handling, reporting and continuous improvement.
DataConsultant priceRequest a Quote
- Review cadence
- Policy change management
- Governance reporting
- Ongoing specialist support
Indicative market context, not DataConsultant pricing: public India offerings reviewed in September 2026 advertise focused AI governance/policy work from approximately ₹75,000, policy packs around ₹1–3 lakh, and broader enterprise governance assessments at materially higher levels. Scopes differ significantly, so these figures should not be treated as equivalent quotes or a price promise. DataConsultant confirms scope, deliverables and fees after discovery.
Choose a Scope That Matches Your AI Adoption and Risk Profile
A focused policy refresh may be enough for one organisation; another may need tool governance, rollout, training and ongoing operating support.
12
Business Outcomes the Policy Is Designed to Support
The goal is not to stop useful AI adoption. It is to give employees and leaders a safer, clearer and more auditable path to use AI responsibly.
Clearer employee decisions
People can distinguish routine approved use from situations that require specialist review, restriction or escalation.
Reduced shadow AI exposure
Approved routes and practical guidance reduce the incentive to bypass policy when teams need AI capabilities quickly.
Stronger data protection
Data classifications and confidentiality rules are translated into clear AI input, output and tool-use boundaries.
More consistent human oversight
Review requirements are tied to business consequence, affected people, external use and error tolerance rather than vague instructions.
Better procurement and vendor decisions
Tool approval criteria connect policy expectations with vendor terms, enterprise settings, integrations and data-handling requirements.
Defensible governance evidence
Ownership, exceptions, incidents, training and policy updates can be recorded and reported as part of the broader AI governance programme.
14
AI Use Policy FAQs
Answers to common questions about policy scope, AI tools, data handling, human oversight, standards, regulation, rollout, timelines and pricing.
What is an AI use policy?
An AI use policy defines how employees, contractors and other authorised users may use artificial intelligence tools for work. It normally sets boundaries for approved tools, data handling, prohibited activities, human review, output verification, intellectual property, privacy, security, incidents, exceptions and accountability.
How is an AI use policy different from an AI governance framework?
An AI use policy is the practical rule set for people using AI in day-to-day work. An AI governance framework is broader: it can define oversight structures, risk classification, lifecycle controls, committees, inventories, assurance, monitoring and reporting. The use policy should connect to that broader governance model rather than operate as an isolated document.
What should an enterprise AI use policy cover?
Typical coverage includes scope and definitions, approved tools, prohibited uses, data classification, confidential and personal data, intellectual property, prompt and output handling, human review, high-impact decisions, external communications, software and code generation, agents and automation, vendor terms, records, incidents, exceptions, training, monitoring and periodic review.
Should employees be allowed to use public generative AI tools?
That depends on the organisation’s data, contracts, risk appetite, tool settings and use cases. A policy can distinguish approved enterprise tools from consumer services, define what information may be entered, require specific controls for sensitive use and provide a route for requesting new tools instead of relying on blanket prohibitions.
How does the policy address confidential or personal data?
The policy can map organisational data classifications to AI-use rules, including what must never be entered into unapproved tools, when redaction or de-identification is required, which enterprise configurations are acceptable, and when privacy, security or legal review is required. Applicable obligations depend on jurisdiction, contracts and the specific processing activity.
Can the policy cover Microsoft Copilot, ChatGPT, Gemini and other AI tools?
Yes. The policy can be tool-neutral at principle level while maintaining an approved-tool register and tool-specific conditions. Vendor terms, enterprise controls, retention, training-data settings, access, integrations and data handling can change, so operational tool guidance should be maintained separately from the core policy where practical.
Does an AI use policy need to address agents and autonomous actions?
Yes when agentic or tool-using AI is in scope. Controls can cover permissions, credentials, transaction limits, human approval gates, logging, connected systems, data boundaries, exception handling and the circumstances in which an AI system may take an external action.
How are NIST AI RMF and ISO/IEC 42001 used in an AI use policy engagement?
They can be used as reference points for risk, accountability, governance, documentation, human oversight, transparency, monitoring and continual improvement. The final policy should still be tailored to the organisation’s actual roles, systems, contracts, jurisdictions and risk appetite rather than copying a framework verbatim.
Can the AI use policy support EU AI Act readiness?
Where the EU AI Act applies, the policy can help translate relevant organisational expectations into user-facing rules, escalation routes, training and evidence. Regulatory applicability depends on the organisation’s role, location, systems and use cases, so the service does not replace legal advice or a formal conformity assessment.
How does India’s DPDP framework affect AI use policies?
Where AI use involves digital personal data in India, policy controls should be coordinated with the organisation’s privacy programme and applicable DPDP Act and Rules requirements. The exact obligations depend on the processing context and should be validated with qualified legal or privacy specialists where necessary.
How long does an AI use policy engagement take?
A reliable schedule is confirmed after scoping. Timing depends on the number of business units, current policy maturity, tool landscape, jurisdictions, stakeholder groups, review cycles, regulatory and contractual requirements, and whether rollout materials, training, tool approval workflows or implementation support are included.
How is AI use policy pricing calculated?
DataConsultant does not publish a fixed fee for this service. Pricing is scope-led and can depend on organisation size, number of user groups and jurisdictions, current AI adoption, tool and vendor landscape, policy depth, workshops, stakeholder review, legal and control mapping, training materials, implementation support and required turnaround.
Can DataConsultant help roll out and operationalise the policy?
Yes. Follow-on support can include tool approval workflows, role guidance, employee communications, AI literacy material, exception handling, control design, AI inventories, governance reporting, policy review cadence and managed governance support. Responsibilities and acceptance criteria are agreed during scoping.
What information should we prepare before the engagement?
Useful inputs include existing acceptable-use, privacy, information-security and records policies; approved and commonly used AI tools; data classifications; customer or contractual constraints; key AI use cases; incident history; vendor terms; regulatory obligations; organisation structure; and access to AI, security, privacy, legal, risk, HR and business stakeholders.
AI Use Policy Enquiry
Request an AI Use Policy Scope Review
Share your contact details and requirement. DataConsultant can review likely scope, stakeholders, evidence, deliverables and the appropriate next step.
Make Responsible AI Use a Managed Enterprise Capability
Give employees practical boundaries, give control functions clear escalation routes and give leadership a policy that can evolve as tools, risks and obligations change.