Creating your AI Risk Management Policy
A blueprint for implementing AI Risk Management in your organisation, along with a fully developed template policy that you can build on.
My last four articles have ventured across the landscape of AI risk—from categorising different types of risks, identifying them in your systems, assessing their priority, to selecting the right controls. Now we've arrived at the last step: bringing it all together into a cohesive policy that your organisation can implement. In this article, I’m going to run through what it takes to build an AI Risk Management Policy, one that complements the AI Governance Policy1 that we discussed previously and reflects the ideas I’ve gone through for real-world AI risk management. I’m hoping to help you create one policy that doesn’t just sit around gathering digital dust, that instead it proves to be a living document that people across your organisation consult, update, and most importantly, implement.
When I work with teams implementing AI governance, I've found a clear pattern: organisations that successfully manage AI risks don't treat their policy as a compliance checkbox. Instead, they treat it as something like an operational manual—a translation aid from high-level principles into day-to-day practices.
Unlike your broader AI Governance Policy that focuses on the "who" and "why" of AI initiatives, this Risk Management Policy zeroes in much more on the practical "how." It's the difference between declaring "we'll use AI responsibly" and specifying exactly how you'll spot emerging threats, measure their potential impact, and implement controls before they can cause harm. If you've been following along with our previous discussions on categorising risks, identification techniques like pre-mortems and red-teaming, and assessment approaches, you already have the building blocks. Now we're creating the framework that makes these insights part of your everyday organisational practice.
You'll find that this policy doesn't try to extend beyond the scope of what it takes to do AI risk management - you won’t see anything here about your overall AI strategy, budgets, or ethics approvals—those aspects belong elsewhere in your governance framework. Instead, it focuses on giving your teams a structured way to think about risk: how to identify it systematically, track it continuously, mitigate it effectively, and keep it within acceptable boundaries as your AI systems evolve and interact with the real world. I’ll go through everything that needs to be covered in your policy from start to finish and then give you a template example that you can download and use.
Setting clear scope boundaries for your risk policy
When I think through the right scope for an AI Risk Management Policy, I aim for something both comprehensive and focused. By this, I mean that it should be broad enough to cover your entire AI landscape, yet specific enough that people know exactly when and how to apply it.
The policy should apply to any meaningful interaction with AI technology—whether you're building a new model from scratch, fine-tuning a large language model, or simply deploying an off-the-shelf solution. However, this doesn't mean applying the same intensity of risk process to every AI initiative regardless of its context. Instead, the policy should scale with the risk profile, allowing teams to appropriately adjust their risk management based on specific criteria and review mechanisms.
In practical terms, your policy should really cover any AI system that could potentially:
Process sensitive or personal data that could harm individuals if mishandled
Make or influence significant decisions affecting customers, employees, or business operations
Scale to a point where failures could create legal, reputational, or financial consequences
Interact dynamically with users or other systems in ways that could evolve unpredictably
A well-scoped policy won’t become a bureaucratic barrier to innovation. I've seen cases where AI projects go "underground" to avoid cumbersome governance processes. Think through for your own context an approach that creates a sliding scale, where the intensity of risk management correlates with potential harm. A prototype chatbot used by three people in your R&D team doesn't need the same scrutiny as an algorithm determining customer credit worthiness—but both should fall within the policy's scope, with the latter simply triggering significantly more intensive risk assessment and controls.
I generally encourage teams to err on the side of inclusion when defining scope. Start with the assumption that an AI initiative requires risk management, then create clear pathways to "right-size" the process based on the actual risk profile. This prevents dangerous blind spots where seemingly innocuous systems might harbour significant risks that go unexamined. At the same time, it creates a proportionate approach where teams don't waste resources over-analysing systems with minimal risk potential.
Remember that scope isn't just about which systems are covered—it's also about defining the boundaries between this policy and other organisational policies. Your AI Risk Management Policy shouldn't duplicate or conflict with enterprise risk management processes, cybersecurity frameworks, or data protection requirements. Instead, it really needs to complement them, focusing specifically on the unique risks that emerge from AI systems' learning capabilities, black-box nature, and potential for unexpected behaviours.
Definitions: Speaking the same language of risk
Promising risk initiatives can unravel simply because different teams don't agree on what "high risk" actually means. One person's "significant risk" might be another's "moderate concern," leading to misaligned expectations and inconsistent responses. That's why a clear set of definitions forms the foundation of any effective risk management approach.
When crafting definitions for your AI Risk Management Policy, focus on those terms that consistently create confusion in cross-functional conversations. I'm not suggesting you build an exhaustive dictionary—that would make the document unwieldy and less likely to be referenced. Instead, prioritise clarity around those small number of concepts that often lead to misunderstandings: the difference between "inherent risk" and "residual risk," what constitutes "acceptable risk tolerance," or how a "control" differs from a "mitigation strategy."
For example, when someone mentions a "risk assessment," are they referring to a quick, informal evaluation or a detailed scoring exercise with multiple stakeholders? Without shared terminology, your data scientists might assume a lightweight process while your compliance team expects something far more rigorous. These misalignments can lead to frustration, duplicated efforts, or dangerous gaps in your risk management approach.
Most organisations already have some form of enterprise-wide risk terminology, so don't reinvent the wheel. It’s useful to reference existing frameworks where appropriate, whether that's NIST's AI Risk Management Framework, ISO standards, or your company's established risk glossary. The key is ensuring that AI-specific risk concepts integrate smoothly with your broader risk language, and that the policy is unambiguous. For instance, if your enterprise defines risk levels on a five-point scale, maintain that consistency rather than creating a separate three-point system just for AI risks.
I’ve seen this confusion over definitions extend to seemingly unending policy debates over how to handle risk. For example, some have argued that the EU AI Act’s focus on “risk as potential harm” conflicts with ISO standards’ broader definition of risk as “uncertainty on objectives,” leading them to conclude that ISO-based approaches cannot possibly align with the EU AI Act’s conformance requirements. Yes, they are different. But in practice, if it looks like a duck and quacks like a duck, it’s a duck—and if something looks and acts like a risk, it is a risk. Whether we call it “harm” or “uncertainty on objectives,” the outcome is the same: we still need robust mechanisms to identify, assess, and manage anything that jeopardises safety, compliance, or performance. Your policy needs to treat “risk” holistically: if there’s potential for negative impact or unmet objectives, then address it with the same rigour, regardless of how it is labelled by different frameworks. Try to cut through the noise and confusion caused by different definitions from external documents - instead just define terms as you need them to be interpreted within your organisation.
In my experience, the most useful definitions aren't the obvious ones like "artificial intelligence" or “risk” but rather those operational terms that guide daily decision-making: "What's the threshold for escalating a risk?", "What level of testing constitutes 'adequate' for a high-risk model?", or "When is human oversight required versus optional?" Clear definitions around these practical questions prevent the policy from becoming just theoretical guidance with no concrete application.
Making a statement
The Policy Statement within your policy is a concise, clear declaration of your organisation's approach to AI risk. While your broader AI Governance Policy might state, "We commit to using AI ethically and responsibly," your AI Risk Management Policy Statement needs to be more precise, something like: "We proactively identify, measure, and mitigate AI risks throughout the entire lifecycle, maintaining residual risks within clearly defined tolerance levels."
All the better if you can avoid vague platitudes and instead articulate specific principles that guide real-world decisions. If your organisation has a formal enterprise risk appetite statement, this is the perfect place to reference it, showing how AI risk management nests within your broader risk framework. For example: "Consistent with our enterprise risk tolerance, we accept moderate risks in AI innovation while maintaining zero tolerance for uncontrolled risks to customer privacy or safety."
The policy statement should also reflect your organisation's unique context and priorities. If you operate in a heavily regulated industry like healthcare or financial services, emphasise how regulatory compliance forms a non-negotiable baseline for all AI initiatives. If your business strategy emphasises rapid innovation, acknowledge how risk management supports rather than hinders that goal—enabling sustainable innovation by preventing costly missteps.
What matters most is that your policy statement provides genuine decision-making guidance. When teams face difficult trade-offs—like whether to delay a launch to conduct additional testing or whether to implement a more restrictive but potentially performance-limiting control—the policy statement should help them align their choices with organisational values and priorities.
The bits that make risk management work
Now we need to talk about the bridge between conceptual understanding and concrete action—showing exactly how risk management integrates into the lifecycle of every AI initiative. Be aware that it is often better to thoughtfully extend existing risk frameworks to address AI's unique characteristics. If your organisation already uses a risk register for tracking enterprise risks, your AI Risk Management Policy should explain how AI risks feed into that same system, perhaps with additional AI-specific metadata or assessment criteria that capture those distinctive velocity and feedback dynamics we discussed in earlier articles.
It’s all about creating clear, repeatable processes that team members can follow without having to reinvent the wheel each time. Map out how risk assessment happens at each critical phase of an AI system's lifecycle:
During concept and planning, teams should perform initial risk identification using techniques like pre-mortem simulations or incident pattern mining, categorising potential risks and establishing preliminary risk scores. This early assessment should influence fundamental design choices—like whether to proceed with development and what architecture or approach minimises inherent risk.
As development progresses, risk assessment deepens through more rigorous techniques like dependency analysis or adversarial testing, with findings documented in the risk register and informing specific control requirements before deployment can be approved.
Post-deployment, ongoing monitoring and periodic reviews help make sure that risks haven't evolved beyond their assessed levels, with clear thresholds for when changes in model behaviour, usage patterns, or external environments trigger reassessment.
Keep in mind that should be different paths based on initial risk screening. A low-risk or skunkworks AI project might follow a streamlined process with basic documentation, while high-risk initiatives trigger more intensive assessment, multiple approval gates, and mandatory controls. This proportional approach prevents risk management from becoming a bureaucratic obstacle but still maintains scrutiny where it matters most. You have to be explicit on the point when a skunkworks project becomes a mission-critical application, and the expectation of more thorough risk management processes to kick in beforehand. This level of detail doesn’t need to be exhaustively documented in the policy though - it can come in working guidance.
Most importantly, the policy has to specify concrete artifacts and decision points: What documentation must be completed? Who reviews risk assessments? What criteria determine whether additional controls are needed? When is executive approval required? By answering these practical questions, you transform risk management from a fairly vague aspiration into a defined workflow that teams can actually implement.
Creating clear accountabilities
Spelling out who owns each aspect of AI risk management is a crucial operational element of your policy. The most common pitfall I see isn't lack of risk awareness—it's the assumption that "someone else" is handling the risk. Data scientists assume legal teams are addressing compliance risks; product managers think IT security is covering all technical vulnerabilities; legal teams think their supplier contracts protect the organisation from toxic content in third party data, executives believe frontline teams are monitoring for potential harms. Without clear ownership, these gaps become blind spots where significant risks can fester unaddressed.
Your policy should map specific responsibilities to roles across the organisation, creating a matrix of accountability that leaves no aspect of risk management uncovered. This definitely doesn't mean centralising all responsibility with a single risk team—quite the opposite. Effective AI risk management distributes appropriate responsibilities throughout the organisation while maintaining clear oversight. Good risk management is cultural, not procedural.
For example, model developers might be responsible for initial risk identification during design and development, documenting potential failure modes in the risk register. Business owners might own the final risk acceptance decision, determining whether residual risks align with business objectives and organisational tolerance. A dedicated AI Risk Committee might review high-risk initiatives, approving mitigation plans or requesting additional controls. And executives may have specific responsibilities for significant risk decisions that affect enterprise reputation or regulatory standing. I've seen organisations create beautiful risk frameworks that collapse in practice because no one knows who's supposed to take action when risks emerge. By contrast, teams with clear accountability lines respond more quickly when warning signs appear.
This clarity serves another vital purpose—it creates natural escalation paths when issues arise. If a data scientist discovers a potential bias problem during testing, they should know exactly who to alert and what response to expect. Without this clarity, concerning findings might remain siloed or addressed too late, especially when teams face deadline pressure.
The governance structure
Establishing a governance structure for risk isn't about creating unnecessary bureaucracy—it's just about ensuring risk considerations about AI receive appropriate attention rather than being lost among broader strategic or operational discussions. Think of this as creating the right sensing mechanisms for your organisation. Much like a complex system needs specialised sensors to detect different types of threats, your AI governance needs specific oversight functions tuned to identify and respond to evolving risk patterns. This will likely take the form of a dedicated AI Risk Committee that meets more frequently than your main governance board, focusing exclusively on AI risk-related matters—emerging threats, control effectiveness, and incidents requiring investigation.
The governance structure you establish should reflect your organisation's scale and AI maturity. In smaller organisations or those early in their AI journey, risk oversight might be handled by an existing body like a technology steering committee, with dedicated time allocated to AI risk topics. Larger enterprises or those with significant AI footprints might warrant specialised committees with representatives from data science, legal, compliance, IT security, privacy, and business units.
Whatever form it takes, this oversight function should have clearly defined authority and responsibilities. Can it require changes to high-risk models before deployment? Does it set and enforce risk thresholds? How does it interact with existing enterprise risk governance? Answering these questions prevents confusion when difficult decisions arise.
Good risk governance has regular rhythms—perhaps monthly operational reviews of the risk register, quarterly deep-dives into emerging risk patterns, and annual comprehensive assessments of the entire risk landscape. These structured touchpoints set up continuous attention rather than sporadic crisis responses. They also create natural moments to evaluate whether existing controls are working as expected or whether new types of risks are emerging that warrant policy updates. This also means creating clear linkages to business outcomes. AI risk oversight shouldn't exist in a vacuum—it should connect directly to how your organisation makes decisions about AI investments, development priorities, and acceptable trade-offs. When business leaders understand how risk governance supports rather than hinders their objectives, they become partners rather than reluctant participants.
Putting the policy into practice
I've learned the hard way that even the most thoughtfully designed policy is only as effective as its implementation. You can craft the perfect risk management framework, but if teams don't understand it, if leadership loses interest, or if the policy gathers dust without regular updates, you've missed the opportunity to meaningfully reduce AI risks.
Implementation starts with thoughtful introduction to the organisation. Rather than simply announcing "we have a new policy" via email, think about how to make the rollout an educational opportunity. I've seen successful approaches where organisations develop targeted training for different stakeholder groups—technical workshops for data scientists that include hands-on risk assessment exercises, executive briefings that focus on governance implications, and general awareness sessions that help everyone understand why AI risk management matters.
What works particularly well is grounding these sessions in real examples—"war stories" from your own organisation or notable public failures that illustrate what can go wrong when risks aren't properly managed. When teams see concrete examples of AI harms rather than abstract possibilities, the importance of risk management becomes immediately clear. I still use the Microsoft Tay chatbot incident, the Australian Robodebt debacle, and the biased Amazon resume review system in training sessions because they vividly demonstrate how AI risks can materialise through data bias or escalate through feedback loops, a concept that might otherwise seem theoretical.
Equally important is how you maintain and evolve the policy over time. AI technology doesn't stand still, and neither should your approach to managing its risks. Establish a regular review cycle—perhaps every six months—when you reassess whether the policy still addresses current risks and technologies. This should include examining whether the controls you've identified remain effective and whether new classes of risk have emerged that aren't adequately covered.
This maintenance process should draw on multiple inputs: feedback from teams implementing the policy (Is it working in practice? Are parts overly burdensome or unclear?), tracking of near-misses or actual incidents (What does our experience tell us about gaps?), and monitoring of external developments (Are new regulations or best practices emerging?). By incorporating these diverse perspectives, your policy evolves from a static document into a learning system that improves with experience.
I find it valuable to track concrete metrics that indicate whether the policy is actually reducing risk. These might include how many risks were identified and mitigated before deployment, how quickly emerging risks were addressed, or how effectively controls prevented potential harms. These metrics not only demonstrate the policy's value to leadership but also help identify areas where your approach might need strengthening.
Remember that successful implementation isn't about perfect compliance with every procedural detail—it's about meaningfully reducing AI risks while enabling responsible innovation. If teams are engaging with risk thoughtfully but adapting the process to their specific context, that's honestly more often a sign of healthy adoption rather than concerning deviation.
Monitoring and continuous improvement
A risk management policy that remains static quickly becomes obsolete. You need an aspect of the policy that addresses how your approach will stay dynamic and responsive, preventing the policy from becoming a legacy artifact that fails to address emerging challenges.
I've witnessed how AI systems that initially seemed well-controlled can develop unexpected behaviours over time as data patterns shift, user interactions change, or the surrounding environment evolves. A recommendation algorithm that performs flawlessly at launch might gradually develop bias as usage patterns change. A chatbot trained on one cultural context might become problematic when deployed globally. These evolving dynamics require ongoing vigilance rather than one-time assessment.
The most effective monitoring approaches set up clear indicators for each significant risk—essentially creating a dashboard that shows whether risks remain within acceptable parameters. For technical risks like model drift, these might be quantitative metrics such as distribution shifts in input data or performance degradation on validation sets. For ethical risks like fairness, you might monitor outcome disparities across protected groups. The key is defining these indicators in advance, establishing acceptable thresholds, and creating automated alerts when boundaries are approached.
Regular review cycles complement continuous monitoring by providing structured moments to step back and assess broader patterns. I recommend establishing differentiated schedules based on risk levels—perhaps quarterly reviews for high-risk systems, semi-annual for moderate risks, and annual for lower-risk applications. These reviews should examine not just whether individual risks remain controlled but also whether new risks have emerged that weren't initially identified. Avoid the classic and regrettably frequent mistake of performing one all-up risk assessment in a workshop, then forgetting about it’s findings until after the product is launched. That’s checkbox compliance, not risk management.
What makes risk reviews particularly valuable is bringing together diverse perspectives—technical teams who understand the AI system's inner workings, business stakeholders who see its real-world impact, and risk specialists who can spot emerging patterns. This cross-functional dialogue often reveals insights that wouldn't emerge from siloed monitoring.
The continuous improvement aspect closes the loop, making sure that what you learn through monitoring and review actually strengthens your approach. This might mean updating risk assessment criteria to capture previously overlooked factors, refining control measures based on their observed effectiveness, or nudging escalation thresholds based on operational experience. By documenting these improvements and the rationale behind them, you create an institutional memory that preserves valuable risk management knowledge even as teams change.
A learning mindset is essential—viewing each near-miss, surprise, or incident as an opportunity to strengthen your approach rather than evidence of failure. When a new type of risk emerges, don't just address that specific instance but ask deeper questions: "Why didn't we anticipate this? What does this tell us about gaps in our risk identification process? What similar risks might exist that we're still missing?" This continual questioning transforms risk management from a compliance exercise into a genuine learning system.
Training and awareness in a risk-conscious culture
Even a beautiful risk management policy can't work if people don't understand it—or worse, don't even know it exists. I've watched elegantly crafted risk frameworks gather digital dust while teams unknowingly create the very risks those frameworks were designed to prevent. That disconnect doesn't just undermine governance; it creates dangerous blind spots where significant harms can develop undetected.
Effective training transforms abstract policy into living practice. Rather than viewing it as a compliance checkbox, consider it an investment in your risk management infrastructure. Just as you wouldn't install sophisticated security technology without training people how to use it, you shouldn't deploy a risk management policy without ensuring everyone understands their role in making it work.
Tailor your training to different audiences, recognising that data scientists, product managers, executives, and compliance teams each need different perspectives. For technical teams, hands-on workshops where they practice using risk assessment tools with realistic scenarios create practical competence rather than theoretical awareness. For business leaders and product owners who make critical go/no-go decisions, training should focus on recognition patterns—helping them spot warning signs that might indicate emerging risks even before formal assessments raise concerns. Use the technique of the "pre-mortem" where teams imagine their AI project has failed catastrophically and work backwards to identify what might have caused that failure, creating heightened sensitivity to potential risks. Invite external participants who can bring fresh perspectives.
Beyond formal training, building broader awareness means integrating risk consciousness into your organisational DNA. Some companies create simple "risk moments" at the beginning of AI project meetings—a brief discussion of potential concerns or lessons from similar projects. Others develop internal case studies from near-misses or actual incidents, transforming them into learning opportunities rather than blame exercises.
I've found that storytelling proves remarkably effective in making risk concrete. When I share the story of how Microsoft's Tay chatbot went from friendly assistant to generating hate speech in less than 24 hours, teams quickly understand the velocity of AI risk in a way that abstract warnings can't convey. Similarly, walking through how Amazon's experimental recruiting AI developed gender bias helps teams recognise how subtle, unintended patterns can emerge in seemingly objective systems.
Remember that awareness isn't a one-time achievement but an ongoing process. As AI technology evolves and new types of risks emerge, your training needs to evolve alongside it. Regular refreshers that highlight emerging risk patterns keep awareness current rather than outdated. By investing in making risk management part of everyone's mindset rather than a specialised function, you create an organisation where small issues get addressed before they become major problems.
The carrot or the stick
A policy without meaningful implementation becomes merely a suggestion—potentially dangerous when addressing risks with legal, ethical, or reputational implications. While no organisation wants to lead with punishment, having clear expectations and consequences creates the foundation for consistent risk management rather than optional good intentions.
I think there’s a common pattern in organisations that successfully manage AI risks: they treat compliance not so much as rigid rule-following but as a shared commitment to responsible innovation. The compliance mechanism works best when it's seen as protecting the organisation and its stakeholders rather than restricting creative freedom. Risk controls aren't fences to limit your people—they're guardrails that let you move quickly without driving off a cliff.
Start with clarity about what's expected. Help your teams understand both the letter of your policy (the specific steps and documentation required) and its spirit (the underlying risk management principles). This means explaining not just what to do but why it matters—helping people see how seemingly bureaucratic steps actually prevent real harms.
When compliance failures occur—perhaps a team bypasses required risk assessment or ignores identified mitigation requirements—your response should follow a graduated approach. Initial instances often reflect misunderstanding rather than deliberate evasion, making education and process improvement the appropriate first response. For example, if a data science team deploys a model without required testing, instead of immediate penalties, help them understand the risks they've created and establish a remediation plan.
However, your policy should also address more serious or repeated non-compliance. This might include escalation to leadership, additional oversight requirements for future projects, or in extreme cases, consequences for individuals who knowingly circumvent critical safeguards. The specific mechanisms should align with your broader organisational culture and HR policies, but the message should be clear: AI risk management isn't optional when significant harm could result.
You can also think about creating positive reinforcement alongside compliance requirements. Recognising teams that exemplify thorough risk management, sharing case studies where early risk identification prevented problems, or considering risk governance in performance reviews all send a powerful message that this work is valued.
Remember that the goal isn't perfect compliance with procedural details—it's effective risk management that prevents harm while enabling innovation. If your compliance mechanisms focus too heavily on documentation over substance, you'll create a checkbox mentality that undermines genuine risk awareness. Balance the necessary documentation with practical effectiveness, measuring compliance by risk outcomes rather than procedural perfection.
Exceptions and exemptions: Flexibility, not compromise
Even the most thoughtfully designed policy can't anticipate every scenario your organisation will encounter, particularly in a field evolving as rapidly as AI. Creating a structured process for exceptions prevents teams from either abandoning risk management entirely when facing unique circumstances or being paralysed by policies that don't quite fit their situation.
You can create a transparent, documented process rather than ad-hoc exemptions. When teams believe a standard risk control doesn't apply to their unique situation, they should submit a formal exception request that clearly articulates:
Which specific policy requirements they're seeking exemption from
Why the standard approach doesn't apply or creates undue burden without corresponding risk reduction
What alternative controls or safeguards they'll implement to address the underlying risk
The time period for the exception (exceptions should rarely be permanent)
This request then goes through appropriate review channels—perhaps your AI Risk Committee or a designated approver—who evaluates whether the exception truly maintains the spirit of risk management while adapting the letter of the policy. Importantly, all exceptions should be documented in a central registry, creating transparency and preventing a gradual erosion of standards through invisible case-by-case deviations.
A well-designed exception process actually strengthens overall compliance rather than undermining it. When teams know there's a legitimate path to request adaptations for unique circumstances, they're less likely to simply ignore requirements that seem ill-fitted to their situation. It creates a meaningful distinction between thoughtful adaptation (which strengthens risk management) and non-compliance (which undermines it).
The exception process also serves as valuable feedback for policy evolution. If you notice patterns in exception requests—multiple teams finding certain requirements impractical or certain controls ineffective for specific use cases—that signals areas where your policy might need refinement. You can treat exception patterns as learning opportunities, regularly reviewing them to identify policy elements that require adjustment to better match operational realities.
Remember though - not all exception requests should be approved. Some core risk safeguards may be non-negotiable, particularly those tied to regulatory requirements or protection against severe harms. Your policy may need to clearly identify which elements are eligible for exceptions and which represent minimum standards that must be maintained regardless of circumstances.
Full Example Template
To make implementation for you as easy as I can, I've created a comprehensive, downloadable template that translates everything we've discussed into a ready-to-adapt policy document. I've deliberately kept it straightforward, and it integrates with the AI Governance Policy I published previously. You'll notice how the policy statements map to the comprehensive controls mega-map I published in earlier articles, providing that crucial bridge between external requirements and practical implementation.
Think of this template as a head start rather than a final artifact—you'll need to adapt its language, thresholds, and processes to your organisation's specific context, risk appetite, and AI maturity. But I hope that having this concrete foundation eliminates the intimidating blank page that can delay your policy development. The template tries to achieve the balance I always advocate for: rigorous enough to meaningfully reduce risks while practical enough to support rather than hinder innovation. Download it, customise it, and transform it into a living document that evolves with your AI journey.
I’m making this available under a Creative Commons Attribution-ShareAlike (CC-BY-SA) license, so you're free to use, adapt, and build upon it—even for commercial purposes—I only ask that you credit the original source and share any modifications under the same open terms. I believe that better AI safety comes through shared knowledge and experience, so do feel free to use this template, but if you benefit, then consider paying it forward with our wider community.
As Substack doesn’t presently support the download of Word documents, you can get the raw file here:
From a document to lasting impact
Throughout this mini-series on AI risk management, I've gone from understanding the unique landscape of AI risks to identifying, assessing, and controlling them. This policy represents the culmination of that journey—bringing those insights together into a structured framework that transforms risk awareness into what I hope will be your consistent organisational practice.
What distinguishes true high-integrity AI governance isn't the elegance of its documentation but tangible reduction in harmful outcomes and the enablement of responsible innovation. The policy aims to achieve both goals: protecting your organisation and its stakeholders from AI-related harms while creating the confidence to pursue valuable AI opportunities with clear-eyed awareness rather than either reckless optimism or excessive caution.
I've seen firsthand how organisations that develop thoughtful, proportional approaches to AI risk management gain significant advantages. They avoid costly mistakes that damage reputation or trigger regulatory scrutiny. They build greater trust with customers and employees who interact with their AI systems. And perhaps most importantly, they create an environment where technical teams feel empowered to innovate within clear guardrails rather than constrained by fear of unknown risks.
Be aware, implementing this kind of policy requires more than document approval—it needs sustained leadership commitment, resources for training and tools, and consistent messaging that risk management represents a core value rather than administrative overhead. Go back to my previous articles on how to build the business case for AI governance and secure buy-in from leadership if you know what your policy should be, but don’t yet have that kind of sponsorship, resource and budget.
And with that, we conclude our deep dive into AI risk management—a journey that's taken us from understanding risk categories to crafting a comprehensive policy framework. But it’s not the end of the story.
In my next mini-series of articles, I'll be tackling another fascinating domain that often keeps AI leaders awake at night: model and data governance. I'll illustrate the challenges of managing model lineage, versioning, and provenance—essentially, how to keep track of what's happening inside the "black boxes" of AI systems as they evolve through development and deployment.
Just as with this risk management series, I won't stop at theory—I'll build towards a practical template policy that you can adapt to your organisation's specific needs, connecting directly to the governance foundations we've already established. I've found that getting this aspect of governance right often makes the difference between AI systems that remain manageable and those that gradually drift beyond understanding or control.
Stay tuned for those articles coming shortly. Thank you for reading, and my special appreciation to those of you generous with feedback, questions and thoughts on how these resources can be even more useful.
Grateful for your share. Excellent article and sample Policy. Thank you
Thank you once again for yet another beautiful piece and template.