Sample AI Governance Policy
This fictional sample demonstrates the type and quality of portfolio artifact participants learn to produce in the AI Governance Career Lab program.
Disclosure: This fictional sample is provided to demonstrate the type and quality of work participants will learn to produce. It is not legal advice, a template for organizational adoption, or a completed customer deliverable. Organizations should develop policies based on their own regulatory obligations, risk appetite, and operational context.
1. Purpose
This policy establishes requirements, responsibilities, and expectations for the development, procurement, deployment, and ongoing management of artificial intelligence systems at Northstar SaaS. It provides a governance framework that enables the organization to pursue the operational and competitive benefits of AI while managing the associated risks to customers, employees, regulatory standing, and organizational reputation.
This policy is not intended to prohibit or slow AI adoption. It is intended to ensure that AI systems are evaluated, documented, monitored, and managed with the same rigor applied to other business-critical technology decisions.
Sample Portfolio Artifact — Northstar SaaS (fictional company)
2. Scope and applicability
This policy applies to:
- Internally developed AI systems — models, algorithms, and automated decision-making processes built by Northstar engineering teams
- Third-party AI products and services — vendor-supplied AI tools, APIs, embedded AI features in licensed software, and AI capabilities in cloud-platform services
- Employee use of general-purpose AI tools — including but not limited to large language models, code-generation assistants, image generators, and AI-powered productivity tools, whether sanctioned or unsanctioned
- AI components embedded in products — any AI-driven features in Northstar's customer-facing ERP platform
This policy applies to all employees, contractors, and third parties acting on behalf of Northstar SaaS, across all business units and geographies.
Out of scope: Traditional rules-based automation (e.g., if/then workflow rules, static thresholds, formula-based calculations) that does not involve machine learning, pattern recognition, or generative capabilities. If uncertainty exists about whether a system qualifies as AI under this policy, the AI Governance Lead should be consulted.
Sample Portfolio Artifact — Northstar SaaS (fictional company)
3. Key definitions
| Term | Definition (as used in this policy) |
|---|---|
| AI System | Any system that uses machine learning, deep learning, natural language processing, computer vision, or generative models to perform tasks that involve prediction, classification, recommendation, content generation, or automated decision-making. |
| System Owner | The business-unit leader or functional head accountable for the intended use, outcomes, and compliance of an AI system. This is not the developer or vendor — it is the person who bears responsibility for what the system does in production. |
| Risk Classification | The categorization of an AI use case by governance tier (see Section 5) based on the nature of data processed, impact on individuals, regulatory exposure, and degree of autonomy. |
| AI Governance Committee | The cross-functional body responsible for reviewing high-risk AI use cases, adjudicating policy exceptions, and overseeing governance program maturity. See Section 7. |
| Shadow AI | AI tools or capabilities used by employees without organizational awareness, approval, or governance oversight. This includes personal accounts for AI services used for work purposes. |
| Human-in-the-Loop | A design pattern where a human reviews, approves, or can override an AI system's output before it takes effect or is communicated to affected parties. |
Sample Portfolio Artifact — Northstar SaaS (fictional company)
4. Governing principles
All AI activities at Northstar SaaS are expected to align with the following principles. These principles are not aspirational statements — they inform specific requirements in Sections 5 through 10 of this policy.
Accountability
Every AI system has an identified owner who is accountable for its behavior, outcomes, and compliance. Accountability cannot be delegated to the technology itself.
Transparency
Stakeholders affected by AI-driven decisions have a right to understand that AI is being used and how it influences outcomes that affect them. Internal teams must be able to explain what an AI system does and why.
Proportionality
Governance requirements are proportional to risk. Low-risk productivity tools do not require the same oversight as systems that influence customer outcomes or process sensitive data.
Data integrity
AI systems are only as reliable as their data. Training data, input data, and output data must be subject to quality controls, access restrictions, and retention limits appropriate to the use case.
Human oversight
AI systems that affect individuals, make consequential decisions, or operate in regulated domains require meaningful human oversight — not nominal checkboxes, but genuine ability to review, challenge, and override.
Continuous improvement
Governance is not a one-time assessment. AI systems must be monitored, re-evaluated when conditions change, and retired when they no longer meet performance or compliance standards.
Sample Portfolio Artifact — Northstar SaaS (fictional company)
5. Risk classification framework
All AI use cases must be classified into one of three governance tiers before deployment. Classification determines the level of documentation, review, and ongoing monitoring required.
Tier 1 — Standard Use
AI tools used for internal productivity that do not process customer data, personal data, or influence business decisions affecting individuals. Examples: grammar checkers, meeting-transcription tools (internal only), code-completion assistants for non-production code.
Requirements: Registration in the AI system inventory. Acceptable-use acknowledgment by user. Annual review.
Tier 2 — Elevated Oversight
AI systems that process customer or employee data, influence operational decisions, or are embedded in business workflows. Examples: support-ticket triage (NorthstarAssist), predictive analytics for customer churn, AI-assisted code review for production deployments.
Requirements: Full risk assessment. Documented human-oversight controls. Data-handling review. Vendor review (if third-party). Quarterly performance monitoring. Incident-response procedure.
Tier 3 — High-Risk / Restricted
AI systems that make or materially influence decisions affecting individuals' rights, access, employment, financial standing, or safety. AI systems operating in heavily regulated domains. AI systems with limited or no human oversight. Examples: automated hiring-screening tools, credit-decisioning models, AI-driven access-control decisions, autonomous customer-facing actions.
Requirements: AI Governance Committee review and approval prior to deployment. Comprehensive risk assessment with legal and compliance review. Documented bias and fairness evaluation. Defined escalation and override mechanisms. Semi-annual audit. Board-level reporting.
When a use case could reasonably fall into more than one tier, it must be classified at the higher tier until a formal assessment supports reclassification.
Sample Portfolio Artifact — Northstar SaaS (fictional company)
6. AI use-case intake and approval
No AI system may be deployed into a production environment, customer-facing workflow, or business process without completing the applicable intake and approval process.
Submission
The business sponsor submits an AI use-case intake form describing the proposed system, its purpose, data requirements, intended users, and expected impact. The form is submitted to the AI Governance Lead.
Risk classification
The AI Governance Lead classifies the use case into the appropriate governance tier (Section 5) and determines the required documentation, reviews, and approvals.
Assessment and documentation
The system owner completes the required risk assessment, vendor review (if applicable), data-handling review, and human-oversight documentation. The depth of each assessment is determined by the governance tier.
Approval
Tier 1: AI Governance Lead approves. Tier 2: AI Governance Lead and relevant functional stakeholder (e.g., CISO, Privacy Officer) approve. Tier 3: AI Governance Committee review and formal approval required.
Registration
Approved systems are registered in the AI system inventory with their risk classification, owner, approval date, review schedule, and links to supporting documentation.
Sample Portfolio Artifact — Northstar SaaS (fictional company)
7. Governance structure and roles
| Role | Responsibilities | Accountability |
|---|---|---|
| AI Governance Committee | Reviews Tier 3 use cases. Adjudicates policy exceptions. Provides strategic direction for AI governance program. Reports to executive leadership quarterly. | Committee Chair (CTO) |
| AI Governance Lead | Manages intake process. Conducts risk classifications. Coordinates assessments. Maintains the AI system inventory. Tracks compliance status and reports to the Committee. | Reports to CTO |
| System Owner | Completes required documentation. Ensures ongoing compliance. Manages system performance and monitoring. Reports incidents. Initiates re-assessments when system scope changes. | Business-unit head |
| CISO | Reviews security controls for Tier 2 and Tier 3 systems. Advises on adversarial testing, access controls, and data-protection requirements. | Security posture |
| Privacy Officer | Reviews data-handling practices. Advises on consent, retention, and cross-border data transfer requirements. Reviews privacy-notice updates. | Privacy compliance |
| All Employees | Comply with this policy. Report unsanctioned AI tool usage. Complete required AI awareness training. Consult the AI Governance Lead before adopting new AI tools. | Individual compliance |
Sample Portfolio Artifact — Northstar SaaS (fictional company)
8. Data governance requirements for AI systems
AI systems interact with data differently than traditional applications. The following requirements apply in addition to Northstar's existing data-governance policies:
Training data
All training datasets must be documented with source, collection method, date range, and known limitations. Training data containing personal information requires a documented legal basis and must not be retained beyond the period specified in the company's data-retention schedule unless a specific exemption is approved by the Privacy Officer.
Input and output data
Data flowing into and out of AI systems must be logged at a level sufficient to support audit, incident investigation, and performance monitoring. Sensitive data inputs must be minimized through redaction, anonymization, or field-level access controls where technically feasible.
Third-party data sharing
Customer data, employee data, or proprietary business data must not be transmitted to external AI services (including general-purpose LLMs) without a vendor review, a data-processing agreement, and approval from the Privacy Officer. This includes pasting data into AI chatbots, uploading files to AI-powered tools, or using browser extensions that transmit page content to external services.
Model outputs
AI-generated outputs (recommendations, predictions, content, decisions) must not be treated as verified facts. Outputs used in customer communications, regulatory filings, financial reporting, or contractual documents require human review and approval before use.
Sample Portfolio Artifact — Northstar SaaS (fictional company)
9. Acceptable use of general-purpose AI tools
Employees may use company-approved general-purpose AI tools (e.g., approved LLM platforms, code assistants) under the following conditions:
Permitted uses
- Drafting internal communications, presentations, and documentation (with human review)
- Brainstorming, research summarization, and ideation
- Code generation and debugging assistance for non-production code and internal tooling
- Analyzing publicly available information
Prohibited uses
- Inputting customer data, personally identifiable information, financial data, or trade secrets into unapproved AI tools
- Using AI outputs in customer-facing materials, contracts, or regulatory submissions without human review and attribution
- Relying on AI-generated legal, medical, or financial advice as authoritative
- Using personal AI accounts for work-related tasks that involve company data
- Disabling safety features, guardrails, or content filters in any AI tool
- Representing AI-generated work as original human work in contexts where the distinction matters (e.g., thought-leadership content, patent applications, expert testimony)
Requires governance review
- Integrating AI tools into production workflows or customer-facing processes
- Using AI for employee performance evaluation, hiring decisions, or access-control decisions
- Fine-tuning or training models on company data
- Deploying AI-generated content at scale (marketing automation, dynamic content generation)
Sample Portfolio Artifact — Northstar SaaS (fictional company)
10. Monitoring, audit, and review
Governance does not end at deployment. The following ongoing requirements apply:
| Activity | Frequency | Responsible |
|---|---|---|
| Performance metrics review (accuracy, error rates, drift detection) | Quarterly (Tier 2) / Monthly (Tier 3) | System Owner |
| Human-override pattern analysis | Quarterly | System Owner + AI Governance Lead |
| AI system inventory reconciliation | Semi-annually | AI Governance Lead |
| Vendor compliance verification | Annually (or upon contract renewal) | AI Governance Lead + Procurement |
| Risk re-assessment | Annually and upon material change to system scope, data inputs, or regulatory environment | System Owner |
| Policy review and update | Annually | AI Governance Lead + AI Governance Committee |
| Board-level governance reporting | Quarterly | CTO (Committee Chair) |
Sample Portfolio Artifact — Northstar SaaS (fictional company)
11. AI-related incident response
AI-related incidents — including but not limited to discriminatory outputs, data breaches through AI systems, significant accuracy failures, unauthorized deployments, and regulatory inquiries — must be reported immediately to the AI Governance Lead and managed through the following process:
- Identification and containment: The system owner or discovering employee reports the incident and, where possible, isolates the affected system to prevent further harm.
- Assessment: The AI Governance Lead assesses severity, scope, and potential impact in coordination with relevant functional leaders (CISO, Privacy Officer, Legal).
- Communication: Internal stakeholders are notified per severity level. External notification (customers, regulators) is coordinated through Legal and the executive team.
- Remediation: Root-cause analysis and corrective actions are documented. The AI Governance Lead tracks remediation to completion.
- Post-incident review: Lessons learned are incorporated into the governance program. Risk assessments are updated. Policy changes are proposed where warranted.
AI incidents are subject to the same reporting timelines as security and privacy incidents defined in Northstar's Incident Response Plan.
Sample Portfolio Artifact — Northstar SaaS (fictional company)
12. Training and awareness
- All employees: Complete an annual AI awareness module covering this policy, acceptable use expectations, shadow-AI risks, and how to report concerns. New hires complete the module within 30 days of start date.
- System owners and developers: Complete role-specific training on risk assessment, documentation requirements, monitoring obligations, and incident-response procedures before assuming ownership of an AI system.
- AI Governance Committee members: Participate in an annual briefing on regulatory developments, industry standards, and emerging AI governance practices.
- People managers: Complete supplemental training on AI-related employment law considerations and the prohibition on using unapproved AI tools for personnel decisions.
Sample Portfolio Artifact — Northstar SaaS (fictional company)
13. Policy exceptions
Exceptions to this policy may be requested by submitting a written justification to the AI Governance Lead. The request must include:
- The specific policy provision for which an exception is requested
- Business justification and expected duration of the exception
- Risk assessment of the exception, including compensating controls
- Acknowledgment of residual risk by the requesting system owner
Exceptions for Tier 1 and Tier 2 use cases may be approved by the AI Governance Lead. Exceptions for Tier 3 use cases require AI Governance Committee approval. All exceptions are documented, time-limited, and subject to review at expiration.
Sample Portfolio Artifact — Northstar SaaS (fictional company)
14. Enforcement and non-compliance
Non-compliance with this policy may result in:
- Suspension or decommissioning of the non-compliant AI system
- Revocation of access to AI tools and services
- Disciplinary action consistent with Northstar's employee code of conduct
- Contractual remedies for third-party non-compliance
The AI Governance Lead has the authority to suspend an AI system's operation pending review when there is reason to believe it poses an immediate risk to customer data, regulatory compliance, or organizational reputation.
Sample Portfolio Artifact — Northstar SaaS (fictional company)
15. Related documents and frameworks
This policy operates alongside and is informed by:
- Northstar SaaS Information Security Policy
- Northstar SaaS Data Privacy and Protection Policy
- Northstar SaaS Third-Party Vendor Management Policy
- Northstar SaaS Incident Response Plan
- Northstar SaaS Employee Code of Conduct
- Northstar SaaS Data Retention Schedule
- NIST AI Risk Management Framework (AI RMF 1.0)
- ISO/IEC 42001:2023 — AI Management System Standard
- EU AI Act — Regulation (EU) 2024/1689
Reference to external frameworks does not imply full compliance or certification. These frameworks inform Northstar's approach and are used as reference points for governance program design.
Sample Portfolio Artifact — Northstar SaaS (fictional company)
16. Document history
| Version | Date | Author | Changes |
|---|---|---|---|
| 0.1 | [Draft] | AI Governance Lead | Initial draft for internal review |
| 0.2 | [Draft] | AI Governance Lead | Incorporated feedback from CISO, Privacy Officer, and Legal |
| 1.0 | [Pending] | AI Governance Lead | Submitted for executive approval |
Sample Portfolio Artifact — Northstar SaaS (fictional company)
This fictional sample is provided to demonstrate the type and quality of work participants will learn to produce. It is not a template for organizational use, legal advice, or a substitute for professional counsel. Organizations should develop governance policies based on their own regulatory obligations, risk appetite, industry context, and operational needs. Northstar SaaS is a fictional company created for the program case study. No real employer information, customer information, confidential material, or proprietary documents are represented.
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