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In this episode, we shift to a part of A I governance that beginners rarely think about at first, which is what happens when an A I system should stop being used. Decommissioning is the controlled process of retiring a model, a service, or an A I feature so that it no longer influences decisions, no longer consumes resources, and no longer creates hidden risk. The reason this matters for auditors is that organizations are good at building and launching things, but they are often less disciplined about ending things, especially when the system is still technically running in the background. A retired A I system can still cause harm if it keeps making recommendations, keeps scoring people, or keeps storing sensitive data that no longer has a clear purpose. For beginners, a helpful analogy is an old bridge that still stands after a new road is built; if people keep wandering onto it, the risk remains, and the organization is responsible for either closing it safely or maintaining it. Decommissioning is therefore a safety and accountability topic, not a housekeeping detail, and it requires clear retirement criteria and careful data cleanup duties to prevent long-term damage.

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Retirement criteria are the rules that define when an A I system should be taken out of service, and the first audit question is whether those criteria exist and are written down. A system might need retirement because it no longer supports business objectives, because it is too costly to maintain, because it fails to meet new policy requirements, or because monitoring shows it is degrading in ways that cannot be fixed safely. Retirement criteria also matter when the organization’s risk tolerance changes, such as when a model moves from a low-stakes use case into a higher-stakes environment and the organization decides the model is not appropriate for that level of impact. Beginners sometimes assume that retirement happens only when something breaks, but mature organizations retire systems proactively when evidence shows the system is no longer justified. Auditors look for criteria that are tied to measurable conditions, such as sustained performance decline, repeated incidents, inability to explain outcomes to an acceptable standard, or new legal and compliance obligations. Without criteria, retirement becomes an emotional decision driven by budget cycles, leadership changes, or public pressure, which can lead to inconsistent and risky outcomes.

A key part of retirement criteria is understanding the difference between an A I system being outdated and being unsafe. Outdated can mean it is less competitive or less effective, while unsafe means it creates unacceptable risk to people, operations, or compliance. A system can be outdated but still safe enough to use temporarily while a replacement is built, and a system can be unsafe even if it still appears to perform well on average. This is why criteria must include risk signals, not just performance signals, because harm can hide inside averages and inside edge cases. For example, a model might produce correct results most of the time but still create unfair outcomes for a certain group or unsafe recommendations in a specific scenario. Auditors want to see that retirement criteria consider these deeper issues, because organizations can be tempted to keep a model running as long as it seems useful. For beginners, the important idea is that retirement decisions should be based on evidence and risk, not just convenience or habit.

Once retirement criteria exist, an audit will examine how the organization makes the retirement decision and who is authorized to make it. Retirement is a governance decision because it affects operations, customer experience, and sometimes revenue, so the organization needs an accountable owner who can weigh tradeoffs and accept responsibility. A weak environment might leave retirement decisions to whoever happens to notice a problem, while a strong environment has a defined process for evaluating evidence and making a controlled choice. Auditors also look for documentation that explains why the system is being retired, what risks were considered, and what safeguards will be used during the transition. This documentation is important because retirement often overlaps with change management, and the organization needs to avoid creating new risk while removing old risk. Beginners should remember that governance is not only about controlling what is launched, but also about controlling what is removed, because removal can be disruptive if it is rushed or poorly planned.

Decommissioning is not a single switch, and a beginner-friendly way to see it is as a sequence of steps designed to ensure the system is truly out of the decision loop. A model might be retired from production decisions but still used for testing, reporting, or historical analysis, which means the organization must be clear about what retirement actually means in each context. Auditors will look for evidence that the model is no longer used in ways that could affect people or operations without oversight. They also check for shadow usage, where teams keep using a retired system informally because it is familiar or because the replacement is inconvenient. Shadow usage is a classic risk because it bypasses modern controls and monitoring, and it can continue unnoticed if the organization does not track where the model is integrated. For beginners, it is useful to think about how software often spreads into many places, like spreadsheets, scripts, and internal services, and decommissioning requires finding and closing those paths so the system truly stops influencing outcomes.

A major part of decommissioning is planning for continuity, because if you remove a decision-making capability, something must replace it, even if the replacement is a simpler method. Sometimes the replacement is a new model, and sometimes it is a rules-based approach or a human review process, depending on risk and practicality. Auditors will ask whether the organization has ensured that business processes continue safely during the transition, because unsafe gaps can appear when the A I system is removed. For example, if a model triaged support tickets and you retire it without planning, important issues may get lost or delayed, creating harm that is different from the harm the model might have caused. This is why decommissioning should be treated as a controlled change, with clear timing, clear communication, and clear ownership. Beginners should take away the idea that retirement is not simply stopping, it is redesigning a workflow so the organization remains safe and functional without the retired system.

Now we reach data cleanup duties, which are often the most sensitive and misunderstood part of decommissioning. A I systems often touch multiple kinds of data, including training data, validation data, logs, user inputs, and sometimes derived features that can still contain personal or sensitive information. Cleaning up data does not always mean deleting everything, because organizations may have legal obligations to retain certain records for audits, investigations, or business continuity. The real duty is to manage data deliberately, meaning the organization classifies what must be retained, what should be deleted, what must be anonymized or minimized, and how access will be controlled after retirement. Auditors look for evidence that data retention decisions are aligned with policy, privacy obligations, and security needs, rather than being accidental leftovers. Beginners should understand that data can create risk even when a model is no longer running, because stored data can be breached, misused, or repurposed beyond the original intent.

Data cleanup also includes model artifacts, which are the stored outputs of building the model, such as trained weights, configuration files, and snapshots that can be used to reproduce or redeploy the model. These artifacts can carry risk because they may embed information learned from training data, and they can be reused without proper controls if left accessible. Auditors will examine whether retired model artifacts are archived securely, access is restricted, and reuse requires approval, because a model that is supposed to be retired should not be easily redeployed by a well-meaning team trying to save time. Another concern is whether pipelines and automation scripts still run, generating new logs or pulling new data even though the system is officially retired. Those leftovers can quietly collect data for no reason, increasing privacy exposure and storage costs. For beginners, the key lesson is that decommissioning must include shutting down both the decision function and the data collection habits that were built around it.

A practical way to understand data cleanup duties is to separate data that supports accountability from data that creates unnecessary exposure. Accountability data includes records that show what decisions were made and why, such as audit logs, model version identifiers, and incident reports, which may need to be retained to answer questions later. Exposure data includes raw inputs, sensitive attributes, or unnecessary copies that do not serve a clear purpose after retirement. An audit checks whether the organization can explain the purpose of retained data and whether the retained data is protected with appropriate access controls and retention periods. It also checks whether deletion is real, meaning data is removed from active systems, backups are handled appropriately, and access pathways are closed. Beginners often assume deleting a file is the end, but in real environments data can exist in multiple locations, including archives and redundant storage, so cleanup requires a thoughtful process. The goal is not simply to delete, but to reduce risk while preserving what must be preserved.

Another important element is documenting decommissioning activities and proving that they were completed. Auditors rely on evidence, so they will look for records of what was retired, when it was retired, what dependencies were removed, and how the organization verified that the system is no longer in use. Verification matters because it is easy to miss an integration point, and a retired model can remain active in a small service or a background job that no one remembers. Evidence might include change records, access revocation logs, system configuration updates, and confirmation that monitoring no longer shows the retired system producing outputs. Auditors will also look for sign-off by accountable owners, because that sign-off shows the organization has accepted the retirement and validated it. For beginners, it is enough to remember that decommissioning without verification is like locking a door but leaving a window open, because risk can still slip through.

Decommissioning intersects with incident management in a way that can surprise beginners, because sometimes retirement is itself an incident response action. If monitoring reveals serious harm, or if a model is discovered to violate policy, the safest response may be to halt or retire the system quickly. In these moments, the organization needs emergency retirement procedures, including how to disable the model, route decisions to safer paths, and preserve evidence for investigation. Auditors will look for whether the organization can do this responsibly, because a hurried shutdown can create chaos if business processes depend on the model. This is where earlier planning pays off, because retirement criteria and transition plans make it possible to act decisively under pressure. Beginners should recognize that retirement is not always calm and scheduled; sometimes it is a safety brake, and having that brake ready is part of responsible governance. A mature organization can retire a system quickly without losing control of operations or evidence.

It is also worth understanding how decommissioning helps prevent a subtle but common risk: the accumulation of forgotten A I systems. Organizations often experiment, pilot, and launch small models across departments, and over time those models can become invisible, with no clear owner, outdated documentation, and minimal monitoring. These forgotten systems are risky because they may be making decisions without oversight, and they may be storing data no one remembers. Auditors care about this because hidden systems undermine trust and compliance, and they create a situation where the organization cannot confidently describe how decisions are made. Strong decommissioning practices help prevent this accumulation by forcing teams to define end-of-life plans, assign ownership, and clean up data and integrations when systems are no longer justified. For beginners, this connects to a bigger lesson: governance is about keeping the organization’s technology landscape understandable and controllable. When systems are retired cleanly, the organization reduces complexity and makes remaining systems easier to monitor and secure.

To bring the concepts together, imagine an A I tool used to recommend who should receive a special offer, and over time the business changes and the offer is discontinued. If the model continues to run, it could still influence marketing decisions, potentially causing unfair or confusing outcomes for customers. Retirement criteria might include the decision that the business purpose no longer exists, or that the model cannot be justified without the offer. Decommissioning would require removing the model from workflows, ensuring no teams are still using it out of habit, and confirming that the new process does not depend on the old outputs. Data cleanup duties would involve deciding what logs and decision records must be retained for accountability and what personal data should be deleted to reduce exposure. An auditor would ask for evidence that the model is truly retired and that the organization can prove both the decision and the cleanup were handled responsibly. This example is simple, but it shows how retirement and cleanup protect both the organization and the people affected by its decisions.

When you step back, auditing A I decommissioning is about confirming that the organization can end an A I system as responsibly as it began it. Retirement criteria make the decision predictable and evidence-based, rather than emotional or accidental. Data cleanup duties ensure that retiring the system does not leave behind a trail of sensitive information, reusable artifacts, or hidden integrations that continue to create risk. For new learners, the main takeaway is that the life of an A I system includes an end, and that end must be planned, controlled, and documented to protect users and maintain trust. Decommissioning is not a failure, it is a sign of maturity, because it shows the organization can adapt when objectives change, risks evolve, or better solutions emerge. If you can explain how retirement criteria and data cleanup duties work, and why auditors demand evidence for both, you are building the kind of governance thinking that Task 8 expects in the real world.