Creating your AI Governance Policy
Here's how to create a straightforward, practical AI governance structure and policy aligned to ISO42001 - plus a complete policy template you can adapt and use.
In previous articles, I've described a journey from developing the business case for AI governance to creating a comprehensive set of required master controls that align with multiple regulatory frameworks and international standards. Now it's time to build the actual AI governance foundations for real in your organisation.
The first crucial step in operationalising AI governance is creating the right policies and guidance—starting with a well-crafted AI Governance Policy. This policy is not just an administrative document; you should think of it as the strategic blueprint that defines how your organisation will oversee, direct, and control its use of AI. Doing it right will help ensure that every AI initiative is developed responsibly, deployed ethically, and continuously monitored for performance, fairness, and compliance. It builds a foundation that aligns with internationally recognised standards, such as ISO 42001, SOC2, the EU AI Act and emerging standards and regulations through the master controls that we previously discussed—spanning 44 key requirements across 12 domains.
For organisations that already have an ISO 27001-aligned security management system1 in place, much of this will feel quite familiar. You'll recognise common elements like policy frameworks, governance committees, and escalation paths. The goal isn't to duplicate these structures but to thoughtfully extend them to address AI-specific considerations.
Establishing Purpose and Scope
The heart of your AI Governance Policy has to be a clear statement of purpose. It sets out to answer fundamental questions: Why are we governing AI, and what do we hope to achieve? It articulates a commitment to responsible innovation—ensuring that AI systems not only drive business value but also operate ethically and responsibly.
I think it’s best to start your policy by articulating objectives that connect to globally recognised AI principles. When crafting these objectives, consider how they align with established frameworks such as the OECD AI Principles2, UNESCO's Recommendation on the Ethics of AI3, the EU's Ethics Guidelines for Trustworthy AI4, and the IEEE Ethically Aligned Design5 standards, along with the cultural principles and values of your organisation. These frameworks provide a really solid foundation upon which to build your governance approach, but where possible you’ll want to translate them to the language and familiar cultural principles of your own organisation.
Each of these global frameworks takes a slightly different perspective around common themes. For instance, the OECD AI Principles emphasise accountability—ensuring clear responsibility for AI systems throughout their lifecycle. You can see how that can become tangible through our master control GL-1 (Executive Commitment and Accountability) and specific statements in policy. For example, when your CTO chairs the AI Governance Committee and reviews high-risk deployment decisions, they're bringing accountability to life. When system performance metrics reach the executive dashboard, accountability flows through your organisation's decision-making at multiple levels. The governance structure is all about bringing this purpose into tangible actions that are allocated and executed.
Similarly, both the EU's Ethics Guidelines and UNESCO's Recommendation highlight fairness as a non-negotiable principle. Your governance structure and policy may transform this principle into concrete objectives around equitable treatment and bias prevention. Master control RS-5 (Fairness and Bias Management) outlines the practical mechanisms—regular testing across demographic groups, statistical analysis of outcomes, and intervention processes when disparities emerge. Again, this shifts fairness from an aspirational objective to specific control actions.
The IEEE Ethically Aligned Design standards emphasise transparency and explainability—principles that your policy might translate into objectives around making AI decisions understandable to those affected by them. Controls like RS-4 (Explainability and Interpretability) ensure technical teams document how models reach conclusions and communicate them in terms users can comprehend.
I hope you can see my point: objectives stated in policy are translated into specific imperatives that in turn align with the required controls. By anchoring your policy objectives in these global frameworks in this way, you create a governance approach that not only reflects international best practices but also prepares you for evolving regulatory landscapes. This isn't about adding bureaucratic layers; it's about creating a bridge between universal ethical principles and the day-to-day decisions your teams make when developing and deploying AI. Your policy shows how these principles will be upheld throughout your AI initiatives while integrating with existing frameworks like ISO 27001 for security and GDPR for data protection. As new laws or standards emerge, it is easier to accommodate them within a framework that is led by principles and objectives that have already achieved broad global consensus.
The Core Structure, Roles & Responsibilities
A well-designed governance structure creates clear pathways for decisions, oversight, and accountability without drowning your organisation in paperwork. It’s about ensuring signals flow efficiently to where decisions need to be made. In small and medium-sized companies, every governance role likely does double duty, so the structure must be exceptionally efficient.
The governance architecture distributes leadership and responsibility through your organisation, serving as a bridge between your strategic vision and daily operations. Our master controls, particularly GL-1 (Executive Commitment), GL-2 (Roles, Responsibilities & Resources), and GL-3 (Strategic Alignment & Objectives), form the foundation of this structure.
The Strategic Layer: AI Governance Committee
In most mid-sized organisations of say 200-400 people, the AI Governance Committee emerges naturally from existing technology governance. Your CTO likely already chairs monthly sessions where AI topics feature, making this a natural starting point. Core membership typically includes your engineering lead, lead data scientist, head of legal, and business leaders whose departments rely heavily on AI systems. Rather than maintaining a standing panel of experts, most organisations find it more effective to have one or more trusted external advisors who may join quarterly reviews and remain available for particularly complex challenges.
This committee—anchored in master controls GL-1 and GL-3—reviews strategic initiatives, sets risk tolerances, and ensures AI activities align with organisational objectives. Their mandate includes reviewing proposals for new AI systems or major architectural changes, ensuring each meets defined performance, fairness, and ethical criteria before approval.
Typically, the CTO or equivalent executive must chair this committee, reinforcing the principles in GL-1 and GL-3—ensuring leadership remains visible and accountable. This committee becomes the guardian of your AI principles, translating high-level ethical commitments into practical governance decisions.
As an aside, the corporate board's role deserves special consideration. Rather than creating a separate AI committee, most organisations add AI governance to their existing risk or technology committee's scope. It's important to ensure at least one board member has sufficient AI literacy to provide meaningful oversight, potentially requiring board education or bringing in an advisor for periodic deep dives on AI governance.
The Operational Layer: Where Governance Happens Daily
Beneath the strategic committee sits the AI Operational Committee, charged with day-to-day governance. This group typically includes the AI Technical Lead, senior engineers, and a risk specialist. They monitor system performance, manage routine updates, and serve as the first line of defence when issues arise. When technical teams encounter challenges outside routine parameters, these issues get escalated to this committee, ensuring no problem goes unnoticed. This process embodies the continuous risk assessment and escalation mechanisms described in master controls RM-4 (Risk Monitoring and Response) and RO-2 (Transparency, Disclosure, and Reporting).
Most medium-sized organizations can't sustain multiple separate forums, so they establish a mechanism like an "AI Operations Review" that meets perhaps every two weeks. This group reviews new deployments, significant changes, recent incidents, and performance metrics in focused sessions. They make decisions within some clearly defined parameters and know when to escalate to the governance committee.
The effectiveness of this operational layer depends on clear decision tiers. Technical teams handle routine matters following established guidelines. The Operational Committee approves significant changes or deployments. And the Governance Committee reviews strategic initiatives or high-risk systems. This clarity eliminates bottlenecks while ensuring appropriate oversight at crucial decision points.
Connecting the Layers: The AI Governance Lead
The bridge between strategic oversight and operational management is formed by the AI Governance Lead. This person - possibly you - typically splits time between hands-on work with science/engineering teams and governance oversight, keeping one foot in technical implementation and another in governance policy. This dual role keeps governance grounded in technical reality while ensuring technical decisions consider broader implications.
Reporting lines require careful consideration. The AI Governance Lead needs direct connections to both technical leadership and relevant business units. In practice, they might report to the head of engineering or a business leader while maintaining a dotted line to the CTO for governance matters. This creates necessary escalation paths and fosters collaboration across organisational boundaries.
The AI Governance Lead works directly with System Owners—experienced engineers or data scientists who take responsibility for specific AI systems alongside their development duties. These System Owners ensure their systems meet performance, fairness, and compliance standards. This ownership model works effectively when supported by clear guidelines from the AI Governance Lead and doesn't require dedicated roles for every AI system.
Integrating with Existing Functions: Risk, Compliance, and Security
Most mid-sized organisations can't dedicate multiple people solely to AI risk management. Instead, success comes from extending existing roles. The risk analyst who handles security assessments adds AI risk evaluation to their toolkit. The data protection officer who oversees privacy expands their scope to include AI ethics and fairness. The key is providing these professionals with focused training on AI-specific considerations and appropriate support.
Cross-functional coordination emerges naturally with thoughtful role positioning. The AI Governance Lead regularly joins security reviews when AI systems are discussed. System Owners participate in risk assessments for their systems. Business leaders whose departments use AI join governance discussions affecting their operations. I have seen this organic collaboration often proving more effective than formal, forced coordination mechanisms.
Some organisations create a virtual AI ethics function rather than maintaining a standing committee. They identify a small group—perhaps legal counsel, a senior engineer, a business leader with strong ethical judgment, and an external expert—who evaluate ethical implications when needed. This group convenes only when specific issues require consideration.
Documentation and Authority
Documentation transforms this structure from concept to reality. Each role needs clear terms of reference specifying responsibilities, authority limits, and interaction patterns. System Owners must know exactly when to escalate issues to the AI Governance Lead. The AI Governance Lead should have clear triggers for involving executive leadership. Risk analysts need criteria for initiating ethical reviews.
An authority matrix using a RACI model (Responsible, Accountable, Consulted, Informed) clarifies who makes which decisions about AI systems. This becomes particularly important in organisations where individuals wear multiple hats, preventing confusion when critical decisions arise.
This governance structure I’ve described—while lighter than what large enterprises might implement—provides effective oversight when well-executed. It's about being realistic about what each person can contribute while ensuring critical responsibilities don't fall through the cracks. As your AI footprint grows, you might dedicate more people to specific governance aspects, but starting with clear, manageable roles that integrate with existing responsibilities creates a foundation that can scale organically with your needs.
The AI Governance Policy Document
A written policy document is the formal embodiment of your organisation's commitment to responsible AI. It is not simply a list of rules; it is a comprehensive guide that communicates the strategic vision, ethical considerations, and operational procedures required to govern AI effectively. However, it also needs to be succinct, practical and digestible.
At the outset, the policy document should articulate your organisation's high-level vision for responsible AI. As I described in the section on Purpose, this involves stating a commitment to ethical principles such as fairness, transparency, accountability, and respect for privacy. The document then has to bridge the gap between high-level objectives and existing or new organisational policies and mechanisms. If your company already adheres to ISO 27001 for IT security or follows GDPR for data privacy, the AI Governance Policy should explain how these frameworks are extended to encompass the unique challenges of AI. This might involve additional safeguards such as continuous monitoring for model drift or enhanced procedures for incident reporting—areas which are later detailed in our master controls such as OM-1 (System Performance Monitoring) and IM-1 (Incident Detection and Response).
An essential element of the policy is its decision framework. It delineates three tiers of decision-making:
Team-Level Decisions: Routine operational choices managed by the technical teams, in accordance with established guidelines.
Operational Committee Reviews: Significant system changes or deployments that require review by the AI Operational Committee.
Governance Committee Approval: Critical decisions, such as launching a new AI system or major architectural changes, that must be reviewed by the AI Governance Committee.
The most effective AI Governance Policy documents I’ve seen are very focused and succinct: clear principles for safe, ethical AI use, key responsibilities, and critical requirements that everyone needs to understand. They explicitly connect to the organisation's values and objectives, making it clear why these guidelines matter. As you set about drafting the policy document, don't be tempted to include everything plus the kitchen-sink. Be careful using published templates, some of which are so comprehensive they span to 40+ pages. Your AI Governance Policy should be a few pages long at most.
Sidebar: AI Governance Policy Template
Here’s a template that I put together from my experience implementing AI Governance in complex organisations, working through the challenging process of transforming governance principles into operational reality. It's designed to be comprehensive enough to address the full scope of AI governance while remaining succinct and adaptable to your organisation's specific needs. You'll notice it follows a natural progression - from clear definitions and scope through specific requirements for every aspect of the AI lifecycle.
The policy isn't meant to be adopted wholesale - think of it as a starting point you'll refine based on your organisation's AI landscape, risk appetite, and governance maturity. Also note that I’ve included references to the Master Controls throughout, which would normally be removed (for brevity) in a final document.
I’ve chosen to make it available under an open-source CC-BY-SA license, so feel free to make use, adapt and reuse as you see fit. I only ask that you provide attribution so that others can similarly learn from these resources and the guidance that goes along with it. At this point in time, Substack does not support Word documents natively, so I placed a Word version at our GitHub Repo: https://github.com/The-Company-Ethos/doing-ai-governance/tree/main/documents
Supporting policies build out from this foundation in the AI Governance Policy, but here's where pragmatism and reuse is important. If you already have a robust data classification and handling policy under your privacy framework, don't create a separate one for AI - extend the existing policy to address AI-specific considerations. Your security policies likely already cover system access and monitoring; they just need enhancement to address AI-specific risks. Don’t create an AI Risk Management Policy; evolve the existing broader Risk Management Policy with AI-specific considerations. This integration prevents the policy sprawl that often undermines governance efforts.
Crafting the right set of supporting policies is about finding the sweet spot between comprehensive coverage and practical usability. I've seen too many organisations create towering sets of policies that gather dust while teams just push ahead with their own approaches ignoring them. The art lies in building a framework that genuinely guides decisions while remaining lean enough for teams to internalise and actually use.
However, some aspects of AI governance do demand their own policy attention. Model lifecycle management, for instance, introduces unique requirements that might not fit neatly into existing frameworks. Here, you'll want dedicated policies that outline how models progress from development through deployment to retirement. Similarly, you might need specific policies around automated decision-making and human oversight that go beyond traditional technology governance.
The key to making these policies work lies in their interconnections. It's useful to create a visual map of how different policies relate to each other. This can help someone understand which policies apply to their work without having to wade through the entire framework. For your own benefit, it also helps identify and eliminate overlaps and contradictions that are creating confusion and winnow out legacy policies that are likely no longer relevant.
Always keep in mind, the goal isn't to create the most exhaustive policy framework; it's to create one that genuinely shapes how your organisation develops and uses AI systems responsibly. The foundation is there to grow as your AI footprint expands. You can add more detailed policies as specific needs emerge, always maintaining that crucial balance between comprehensive coverage and practical usability. But starting with a clear, integrated framework that teams can actually use creates the basis for sustainable governance.
Governance Mechanisms and Oversight
Policy maintenance becomes crucial for keeping this framework relevant - and it doesn’t happen by accident. Annual reviews are standard, but the pace of AI means you may need mechanisms for more responsive updates. I think an effective approach is to have quarterly "policy health checks" where key stakeholders briefly review recent developments in AI governance and identify any urgent policy updates needed. This keeps your framework current without creating excessive overhead. There may also be some points where advances require some rethink on multiple policies - for example, dealing with agentic AI.
The true test of policy effectiveness comes in how teams use them day-to-day. The most successful organisations treat their policies as living documents, regularly referenced in decision-making rather than just during audits. They achieve this by making policies accessible and actionable - clear guidance on what to do rather than just what not to do, along with supporting resources that provide specific examples rather than abstract principles alone, and practical decision frameworks rather than blanket rules.
The policy's effectiveness depends on the mechanisms that enforce it—mechanisms that are both dynamic and deeply integrated with your organisation's day-to-day operations. Effective governance mechanisms are the practical tools that transform strategic principles into real-world actions.
In my own experience, I’ve found that the difference between governance that works and governance that fails often comes down to the practical mechanisms through which these decision checkpoints actually happen. At the heart of any working governance system lies the review and approval process. I have found that the most successful approaches create what we might call "governance touchpoints" - specific moments in the development lifecycle where teams must pause for structured oversight. For instance, many organisations establish three critical review points: an initial concept review before significant resources are committed, a pre-deployment review focusing on testing and validation results, and a post-deployment review after three months of operation. This cadence gives teams enough freedom to work efficiently while ensuring appropriate oversight at crucial decision points.
The key is making these reviews substantive rather than ceremonial. Each review needs clear acceptance criteria, specific documentation requirements, and defined participant roles. When a team brings a new AI system for review, they should know exactly what evidence they need to present - from fairness metrics to security assessments. The reviewing body should have a structured framework for evaluating proposals, ensuring consistent decision-making across different systems and teams.
Consultation requirements form another crucial mechanism, determining when teams need to seek input from specific experts or stakeholders. Rather than making every decision a committee matter, effective governance establishes clear triggers for consultation. A team working on a customer-facing AI system might need to consult with legal when handling personal data, with ethics reviewers if making automated decisions, or with risk specialists if exceeding certain impact thresholds. These requirements should be specific enough to be actionable but not so burdensome they become bottlenecks.
Integration with existing change management processes proves crucial for sustainability. Most organisations already have mechanisms for managing technical changes of code - the goal is extending these to handle AI-specific considerations rather than creating parallel processes. When a team proposes a significant change to an AI system, it should flow through the same change advisory board that handles other technical changes, just with additional AI-specific criteria to evaluate. This integration helps ensure AI changes don't bypass established controls while leveraging familiar processes.
Another essential mechanism is robust documentation. Your policy may mandate that every decision, system update, and risk assessment be recorded—a practice underscored by master control LC-4 (Technical Documentation). This comprehensive record-keeping supports transparency and facilitates both internal audits and external regulatory reviews. When combined with integrated change management processes—aligned with LC-5 (Change Management & Version Control)—you build a policy that ensures that any modifications to AI systems are traceable, justified, and compliant with the overall governance strategy.
Clear escalation procedures further reinforce your policy. You might choose to state that when a technical team encounters an issue that falls outside of predefined parameters, it must be escalated to the appropriate committee. A streamlined process—from the technical team to the Operational Committee, and ultimately to the Governance Committee—ensures that high-risk issues are promptly reviewed and addressed.
Oversight is maintained through continuous performance monitoring and regular reporting cycles. Your policy may specify that AI systems must be monitored not only for traditional metrics such as accuracy and latency, but also for more nuanced indicators like fairness, bias, and drift. Automated monitoring systems—guided by master control OM-1—capture these metrics, while periodic reports ensure that leadership is informed of any deviations or incidents. This process, reinforced by internal assurance controls (such as AA-1 to AA-3), creates a dynamic feedback loop that is essential for ongoing policy refinement and operational excellence.
Incident management follows a similar pattern. Your organisation likely has incident response procedures under its security framework - these need thoughtful extension to cover AI-specific incidents. What happens when a model starts showing bias in its decisions? When automated systems make errors that affect customers? The incident classification scheme needs to expand to include these scenarios, and response procedures need to account for AI-specific investigation and remediation requirements.
The effectiveness of these mechanisms often comes down to how well they balance oversight with operational efficiency. They need to be robust enough to catch significant issues while remaining streamlined enough that teams can maintain velocity. Regular review and refinement based on practical experience helps achieve this balance, with mechanisms evolving as your organisation's AI footprint grows and new challenges emerge.
The success of an AI governance framework ultimately depends on having clear visibility into how well it's actually working. Effective monitoring requires focusing intently on the signals that matter most while avoiding drowning in a sea of metrics. AI system performance isn't just about technical metrics like accuracy or latency. While these technical indicators matter enormously, effective oversight requires a broader view that encompasses fairness, reliability, and business impact. You might want to setup an "AI health dashboard" - a focused set of metrics that gives leadership clear visibility into how their AI systems are performing across multiple dimensions. Such a dashboard could track technical performance through standard metrics like accuracy and drift-detection, as well as monitor fairness indicators across different customer segments and measure business outcomes like error rates in customer-facing decisions.
Building Organisational Capability and Evolving the Policy
Implementing an AI Governance Policy is as much about building organisational capability as it is about drafting the document itself. Successful governance requires a combination of technical skills, cultural change, and continuous education each of which needs to be deliberately fostered.
Developing Skills and Understanding
First, role-specific training is vital. Employees across the organisation—from AI developers to compliance officers—need to be equipped with a clear understanding of their responsibilities. Training programs should cover the technical aspects of responsible AI (drawing on master controls such as RS-1 (Human Oversight and Intervention)) as well as ethical and regulatory dimensions (as emphasised by PR-1 (Privacy by Design) and RS-5 (Fairness and Bias Management)). This training ensures that every stakeholder is not only aware of the policy but also capable of applying its principles in their daily work.
The journey typically starts with baseline training that helps everyone understand their role in responsible AI development. But this isn't about sitting through generic presentations on AI ethics. Effective training connects directly to how people work day-to-day. Start by mapping out exactly what different roles need to know. Engineers need practical techniques and specified tools for testing model fairness and detecting drift. Product managers need to explore how to evaluate AI risks during feature planning. Business leaders need to work through scenarios of AI-related decisions they might face.
This role-specific training lays the foundation, but the real capability building happens through hands-on experience. Consider deliberately creating mentoring or shadowing relationships - pairing less experienced team members with those who have deeper governance expertise. A junior data scientist might work alongside a senior engineer during model validation, learning not just the technical steps but the critical thinking that drives governance decisions. This apprenticeship model proves far more effective than formal training alone.
Creating a Culture of Responsible AI
Cultural integration is equally important. A governance policy that is viewed as an administrative burden will fail to inspire the necessary engagement. Instead, your policy should foster a culture where responsible AI is seen as a core organisational value—a value that enhances innovation while mitigating risk. Regular forums, cross-functional workshops, and open channels for feedback help embed this culture. Such collaborative efforts, aligned with master control OM-3 (Continuous Improvement), ensure that governance becomes an ongoing, dynamic process rather than a one-time exercise.
This shift happens gradually, often catalysed by specific experiences. When teams see how governance processes help catch issues early or prevent problems that would have affected users, they begin to internalise its value. Leadership plays a crucial role here - when executives consistently demonstrate that they value thorough governance over rushing to deploy, it sends a powerful message throughout the organisation.
Crafting Effective Training Programs
When rolling out your governance framework, consider a layered approach to training:
Executive Education: Begin with senior leadership. They need to understand the business case for AI governance, the major risks it addresses, and how it enables rather than hinders innovation. This education should be tailored to their strategic perspective, focusing on accountability, decision-making authorities, and the competitive advantages of responsible AI.
Practitioner Training: For those directly working with AI systems, develop hands-on training that addresses their specific responsibilities. Data scientists need practical skills in fairness testing and documentation. Engineers need tools for implementing monitoring systems. Product managers need frameworks for assessing AI impact.
Awareness Building: Even staff not directly involved with AI need a basic understanding of your governance approach. Everyone should recognise AI systems in their environment, understand basic ethical considerations, and know how to report concerns when they arise.
The most effective training approaches combine formal learning with practical application. Consider workshops where teams apply governance principles to real projects, interactive case studies that explore complex scenarios, and governance office hours where experts provide guidance on specific challenges.
Documentation of training completion and effectiveness becomes essential for regulatory compliance and demonstrating due diligence. Track not just attendance but actual capability development through assessments, project reviews, and performance metrics.
Knowledge management becomes the thread that ties all this training and capability building together. Teams need easy access to governance guidance, decision frameworks, and lessons learned from past experiences. But traditional documentation often proves inadequate. The most effective approach combines clear written guidance with rich examples and case studies. When teams encounter a governance question, they should be able to find not just the relevant policy, but examples of how similar situations were handled in the past.
This knowledge sharing needs to be active, not passive. Regular forums where teams discuss governance challenges and share solutions help build collective capability. When one team discovers an effective approach to validating a safety aspect in their models, that learning should spread quickly to others doing similar work. These knowledge-sharing mechanisms need to be lightweight but consistent - perhaps monthly lunch sessions where teams present governance lessons from recent projects.
Evolution and Growth of Your Governance Framework
Finally, the governance structure and policy must be designed to evolve. AI technology and its regulatory landscape are in constant flux, and your governance framework has to be agile enough to keep pace. Regular reviews—both scheduled (such as an annual review) and ad hoc (triggered by new developments)—ensure that the policy remains current. Adaptive mechanisms allow the policy to incorporate new risk factors, update decision criteria, and realign with emerging regulatory standards.
Creating a governance framework that can evolve alongside rapidly advancing AI technology requires careful forethought. The governance structures that serve you well today might prove inadequate as your AI footprint expands or as new capabilities emerge.
The most valuable approach is maintaining consistent governance principles while evolving specific mechanisms. Your core commitments to fairness, transparency, and responsible AI use should remain constant, but how you implement these principles can adapt as your AI capabilities grow more sophisticated. This balance between stability and adaptability helps teams understand and embrace governance evolution rather than seeing it as arbitrary change.
As organisations scale their AI initiatives, governance needs to grow both broader and deeper. Broader in the sense of covering more systems and use cases, deeper in terms of more sophisticated oversight mechanisms. But this scaling needs to happen thoughtfully. One effective pattern involves creating tiered governance requirements that adapt to the risk and complexity of different AI applications. Simple, low-risk systems might need only basic oversight, while more complex or consequential applications receive progressively more rigorous governance.
The true test of governance evolution comes in how well it anticipates and adapts to emerging challenges. The most effective organisations maintain close connections with the AI governance community, learning from others' experiences and staying ahead of emerging risks and requirements. They participate in standards development, publish research, engage with regulators, and actively share their own governance lessons. This external engagement helps them evolve their governance proactively rather than reactively. The ultimate measure of organisational capability comes in how teams handle novel challenges. When they encounter a new type of AI risk or an unfamiliar ethical consideration, do they have the skills and knowledge to evaluate it effectively? Have they internalised governance principles deeply enough to make sound decisions even in unprecedented situations? Building this kind of deep capability takes time, but it is essential for sustainable AI governance.
The AI Governance Policy is not just a static document—it is the strategic blueprint that underpins every AI initiative within your organisation. By clearly defining its purpose, establishing a scalable governance structure, delineating roles and responsibilities, and integrating robust oversight mechanisms, the policy lays a solid foundation for responsible AI development and use. Anchoring these elements in our comprehensive set of master controls ensures that your approach is both aligned with international standards, adaptable to future challenges, and prepared for processes of external audit and certification.
In the next article, I'll walk through building a pragmatic risk management framework that works in the real world, not just on paper. We'll explore how to identify AI risks that actually matter to your business—moving beyond theoretical concerns to focus on specific threats that could impact your systems, customers, and reputation.
I'll share five practical approaches for identifying and assessing these risks efficiently, ranging from scenario modelling to dependency chain analysis. I’ll also go through how to select appropriate risk treatments that balance protection with practicality, ensuring your controls are proportionate to the risks they address. The goal isn't perfect risk elimination (which doesn't exist), but rather thoughtful risk management that enables responsible innovation while protecting what matters most.
Thank you for reading. Please do subscribe and as always, I really welcome any feedback or suggestions.
https://www.iso.org/isoiec-27001-information-security.html
https://www.oecd.org/going-digital/ai/principles/
https://unesdoc.unesco.org/ark:/48223/pf0000381136
https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-ai
https://standards.ieee.org/industry-connections/ieee-ethically-aligned-design.html
This is so useful! Do you mind if I cite it in one of my future posts?
Spot on. Clear and actionable guidance. Loved how you synthesized the IA policy. Great job