Certified: The ISACA AAISM Audio Course

In this episode, we take a step back from individual controls and look at the bigger journey an A I system takes across its entire life, from the first idea all the way to retirement. That journey is called the A I development life cycle, and it matters because security and safety problems often happen when people treat A I as a one-time build rather than a living system that changes. A beginner might think the work ends when a model is deployed and starts producing useful outputs, but that is usually when the hardest risks begin to show up. Over time, data changes, users change, and threats change, and the system has to keep up without drifting into unsafe behavior. When you understand the life cycle clearly, you can predict where risk concentrates and why certain protections must exist long before anything goes live.

Before we continue, a quick note: this audio course is a companion to our course companion books. The first book is about the exam and provides detailed information on how to pass it best. The second book is a Kindle-only eBook that contains 1,000 flashcards that can be used on your mobile device or Kindle. Check them both out at Cyber Author dot me, in the Bare Metal Study Guides Series.

The life cycle starts with an idea, but ideas are not harmless because the first decisions you make can lock in risk. At the idea stage, teams usually define the problem they want to solve and the value they expect, such as better recommendations, faster support, or improved decision making. This is also the moment to define what should never happen, such as exposing personal information, causing harmful outputs, or making decisions that cannot be explained. Beginners sometimes assume these questions are legal or management issues, but they are security issues too, because they define the boundaries the system must respect. A system built without clear boundaries tends to grow in unpredictable ways, and later controls become patchwork fixes. Thinking carefully at the start saves you from building something that is impressive but unsafe.

After the idea is approved, the next stage is planning, which is where the project becomes real work with real constraints. Planning includes deciding who owns the system, who will operate it, and what data and integrations it will require. This is the moment when trust boundaries become concrete, because you decide what is inside your organization’s control and what depends on vendors or external services. Planning is also where you decide how success will be measured and what evidence will be collected to prove the system is behaving properly. Beginners often expect planning to be about timelines and features, but for A I security management, planning must also include risk assumptions. If a plan ignores risks, the system may ship quickly but become difficult to govern, making incidents more likely and remediation more expensive.

Data work is the next major stage, and it is often the most underestimated by new learners because it looks like a simple input to the model. In reality, data selection, collection, cleaning, labeling, and storage shape what the model can learn and what it might accidentally reveal. Data can contain sensitive details, hidden bias, or harmful correlations that lead to unsafe outcomes. Data can also be manipulated, either through mistakes or by adversaries who try to poison the dataset to change model behavior. This stage is where you need to decide what data is allowed, what data is excluded, and how data lineage will be tracked so you can explain where information came from. If you cannot explain your data, you cannot explain your model, and that becomes a long-term security and governance problem.

Once data is prepared, the system enters model development, which includes choosing approaches, training, and tuning. At this point, many people focus on performance, such as accuracy or usefulness, but security and safety should be part of the definition of good performance. A model that answers quickly but leaks sensitive information is not a successful model. A model that is accurate on average but behaves dangerously in edge cases is not ready for real use. Model development also introduces new assets that must be protected, like model weights, training configurations, and evaluation artifacts, because these can reveal intellectual property or enable attackers to replicate or manipulate the system. Beginners should think of this stage as building an engine and also building a shield, because capability without protection is a liability.

As the system matures, it moves toward deployment, which is where architecture and controls become critical. Deployment is not just turning the model on; it is deciding where it runs, how it is accessed, and what it is allowed to connect to. This is where identity becomes real, because you decide which users and services can call the model and which actions are restricted. This is also where isolation and segmentation matter, because you do not want a model service to become a bridge into sensitive data stores or internal systems without control. Deployment decisions also include what gets logged, what gets monitored, and what signals should trigger alerts. Beginners should recognize that deployment is where you create the operational environment that either supports safety or quietly undermines it.

Integration is closely related to deployment, but it deserves its own attention because integrations expand both value and risk. An A I capability may be integrated into applications, workflows, and decision processes, and each integration creates new data flows and new consequences. If the model output is used to guide a human, the risk is different from the model output triggering automated actions. If the model is connected to internal knowledge sources, the risk includes data exposure through prompts, retrieval, and logging. If the model is connected to customer interactions, the risk includes harmful or misleading outputs that damage trust. This stage is where teams must be honest about how the system will be used and how humans will interpret outputs. A common beginner mistake is believing a model output is a fact rather than a generated response that must be handled with care.

After deployment and integration, the system enters operations, which is where the life cycle becomes a loop rather than a line. Operations include monitoring usage, tracking model behavior, managing access changes, and responding to incidents. This stage is where you discover whether your controls are practical and whether your architecture supports real-world needs. If monitoring is too weak, you will not see misuse. If access control is too loose, people will use the system in ways you did not anticipate. If the system is too brittle, small changes will cause failures and create pressure to bypass controls. Beginners should understand that operations is not a maintenance chore; it is where security and safety are proven daily, because that is when real users and real attackers interact with the system.

Change management is another stage that often runs in parallel with operations, because A I systems evolve. Models may be updated, prompts may be refined, new data sources may be introduced, and new integrations may be requested. This is where the idea of a controlled Software Development Life Cycle (S D L C) becomes important, because changes should not reach production without review, testing, and clear ownership. A I adds complexity because changes can affect behavior in subtle ways, not just functionality. A new model version might improve one type of output while worsening another, or it might become more vulnerable to manipulation. Beginners should see change management as a safety gate that keeps improvements from becoming regressions. The best systems treat updates as planned events with evidence, not as spontaneous patches driven by urgency.

Incident response is also part of the life cycle, even though teams often wish it were not. Incidents can include classic security events, such as unauthorized access or data leakage, and they can include A I specific events, such as harmful outputs, unsafe behavior, or a model being manipulated through inputs. A life cycle mindset prepares you to plan for these events before they happen, because response is easier when roles, communication paths, and evidence sources are defined. This stage also connects back to earlier stages because incident lessons should influence data handling, evaluation practices, and deployment controls. Beginners sometimes think incidents are proof that a system failed completely, but in mature security thinking, incidents are expected possibilities, and success is measured by how quickly you detect, contain, and learn. A well-run life cycle turns incidents into improvements rather than repeating failures.

Governance and documentation run across the entire life cycle, and they are what keep the system accountable to the organization’s goals and rules. Governance means the organization can decide what is acceptable, what evidence is required, and who owns risk decisions. Documentation means those decisions are recorded clearly so teams do not rely on memory or informal habits. This matters for A I because systems can outlive the original team and because models can become embedded in critical processes. If governance and documentation are weak, the system becomes a mystery, and mysterious systems are hard to secure. If governance and documentation are strong, leaders can ask informed questions and auditors can confirm that controls align with design. For beginners, the message is simple: clarity is a security control, and the life cycle is where clarity must be maintained.

Eventually, every system reaches a stage that many teams ignore until it is too late: retirement. Retirement means the system is taken out of service, but the work is not finished when the model stops responding. You need to consider what happens to stored data, logs, embeddings, and model artifacts, because these can remain sensitive long after a system is no longer active. You also need to consider what happens to integrations, credentials, and access paths, because abandoned connections can become hidden attack routes. Retirement should include revoking access, removing unnecessary data, and confirming that dependencies are updated so the organization does not keep relying on a system that no longer exists. Beginners often assume retirement is only about turning something off, but secure retirement is about cleaning up risk so the system does not linger as a silent vulnerability.

A useful way to tie the life cycle together is to see it as a chain where each stage affects the next. The idea stage sets boundaries and determines what risks are unacceptable. Planning defines ownership, trust boundaries, and evidence needs. Data work shapes what the model can learn and what it might leak. Development and evaluation determine whether the model is capable and safe enough to deploy. Deployment and integration determine exposure, access, and consequences. Operations, change management, and incident response determine whether the system stays safe over time. Retirement determines whether risk is removed rather than forgotten. When you hold the chain in your mind, Task 22 becomes easier because you can place controls and responsibilities where they belong instead of treating security as one step.

To close, understanding the A I development life cycle from idea to retirement is one of the most practical foundations for A I security management because it stops you from thinking in snapshots. The life cycle teaches you that security and safety are built through early decisions, validated through evaluation, protected through careful deployment and integration, and maintained through operations, change control, and incident response. It also teaches you that retirement is not optional because leftover data and forgotten access paths can become tomorrow’s incident. When you can explain the life cycle clearly, you can also explain why governance, evidence, and controls must follow the system across time rather than appearing only at the end. That is the heart of Task 22, and it is the mindset that keeps A I systems useful, trusted, and defensible long after the excitement of the first launch fades.