Certified: The ISACA CGEIT Audio Course

In this episode, we’re going to focus on a practical problem that shows up in almost every organization that relies on technology: things rarely break all at once, and most failures are preceded by a period of slow drift. Beginners often imagine that risk is something you either have or do not have, like a switch that flips when an incident happens, but governance failures usually develop more like a crack spreading through a wall. The organization might still be delivering, systems might still be running, and leaders might still be hearing positive updates, even as hidden weaknesses accumulate underneath. Monitoring governance with leading indicators is how you detect that drift early, while there is still time to correct it calmly. A leading indicator is a signal that changes before the outcome changes, which means it can warn you about growing risk before the failure becomes obvious. The aim here is to show how Governance of Enterprise IT (G E I T) becomes more reliable when it uses early signals to stay aligned, rather than waiting for outages, audit findings, or customer harm to reveal that governance drifted off course.

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.

A useful starting point is understanding what drift means in a governance context, because drift is not always visible to the people living inside it. Drift can mean priorities slowly shifting away from the approved strategy as urgent requests crowd the portfolio. Drift can mean controls being applied less consistently because teams are overloaded and exceptions become routine. Drift can mean architecture standards being bypassed because a faster workaround seems harmless in the moment. Drift can also mean metrics becoming less trustworthy because definitions change quietly or because data quality deteriorates. None of these changes necessarily produce immediate catastrophe, which is why they are dangerous, because they can become normal before leaders realize anything is wrong. Beginners sometimes assume governance is a document or a committee, but governance is really a set of behaviors and decisions repeated over time. When those behaviors change, governance changes, even if the official policy never changes. Leading indicators give you a way to observe the behavior shift while it is still reversible, so governance stays practical and stable.

To appreciate why leading indicators matter, it helps to contrast them with lagging indicators, because many organizations rely too heavily on lagging evidence. A lagging indicator tells you what already happened, such as the number of outages last quarter, the number of audit findings, or the number of security incidents. Those measures are still important, but they often tell you the story after damage has already occurred, which limits your options to repair and recovery. A leading indicator, by contrast, might show that change success rates are deteriorating, that patch backlogs are growing, or that access reviews are being delayed, all of which increase the likelihood of future failures. Beginners often think early warnings are uncertain and therefore less useful, but uncertainty is exactly why they are valuable, because they invite investigation and correction before the outcome becomes unavoidable. Leading indicators are not about predicting the future perfectly; they are about noticing directional movement early enough to intervene. In governance terms, they let leaders steer, not just explain what crashed.

A common beginner misunderstanding is thinking leading indicators must be complicated or heavily technical to be meaningful. In reality, the best leading indicators are often simple, consistent, and tied to behaviors that are known to create stability or instability. For example, if a governance program depends on consistent risk reviews, then the timeliness and completeness of those reviews can be an early signal of health. If governance depends on disciplined lifecycle management, then the percentage of resources that are within support windows can signal whether risk is building quietly. If governance depends on change management discipline, then the rate of emergency changes can signal whether planning is failing and whether teams are rushing into risky actions. Leading indicators work because they measure whether the enterprise is doing the things that prevent failure, not merely whether failure already occurred. This also makes them easier to explain to leaders, because the indicators describe concrete behaviors, like delays, backlog growth, and exception patterns. When those behaviors shift, governance is drifting, and the drift can be corrected before it becomes an incident.

When you design leading indicators for governance monitoring, you want to anchor them to enterprise priorities so leaders can interpret them without needing deep technical translation. If the enterprise prioritizes resilience, then leading indicators should reflect readiness to respond, such as whether incident playbooks are current, whether ownership is clear, and whether recovery exercises are being performed as expected. If the enterprise prioritizes controlled delivery speed, then leading indicators should reflect whether delivery practices remain stable, such as whether testing coverage is keeping pace with change, whether key dependencies are being managed, and whether quality signals are trending in the wrong direction. If the enterprise prioritizes trust and compliance, then leading indicators should reflect whether data handling rules are being followed, whether retention and disposal processes are being executed, and whether audit evidence is being produced consistently. Beginners sometimes assume governance monitoring is about counting everything, but effective monitoring is about measuring what matters for the enterprise’s chosen tradeoffs. A leading indicator that does not connect to a priority becomes noise, while a leading indicator that connects to a priority becomes a steering tool.

One powerful category of leading indicators is process adherence signals, not because you want rigid compliance for its own sake, but because repeated process shortcuts often precede operational breakdown. For example, when teams frequently bypass architecture review, the environment becomes inconsistent, integration becomes fragile, and support burden rises later. When exception requests increase and are granted without review dates, controls become optional and risk becomes invisible. When documentation updates stop happening, knowledge becomes trapped in individuals, and incidents become harder to resolve. None of these issues necessarily cause immediate outages, but they change the operating conditions so that outages become more likely and recovery becomes slower. Beginners sometimes interpret process adherence as bureaucracy, but in governance, process adherence is often the protective layer that keeps complexity manageable. Leading indicators here might include the ratio of exceptions to standard approvals, the age of unresolved risk decisions, or the percentage of critical processes that have not been reviewed within the expected cadence. When these indicators trend negatively, drift is occurring even if everything still looks fine on the surface.

Another important category is capacity and workload signals, because overload is one of the fastest ways to create drift across many governance areas at once. When teams are overloaded, they naturally prioritize immediate delivery and immediate incident response, while deferring maintenance, reviews, and controls that protect long-term stability. This is not usually a moral failure; it is a predictable human response to pressure. Leading indicators that reveal overload include increasing backlog of high-priority work, increasing time-to-response for governance reviews, rising unplanned work percentages, and rising turnover or burnout signals, because people capability is part of governance capacity. If a security review team becomes a bottleneck, teams may start bypassing review, which creates risk drift. If an operations team is constantly firefighting, lifecycle management will slip, increasing vulnerability and reliability risk. Beginners often assume overload just means hiring more, but governance uses these signals to rebalance demand, adjust priorities, and protect critical controls before the environment becomes unstable. Capacity signals give leaders early warning that the system is being stretched beyond safe limits.

Technology health signals also serve as governance leading indicators, especially when they reflect growing technical debt and increasing fragility. When the percentage of unsupported components rises, risk increases because patches may not be available and failures become harder to diagnose. When monitoring coverage declines or becomes inconsistent, visibility drops, and issues will be detected later and resolved slower. When mean time between incidents begins shrinking, even if total incidents have not yet spiked, it can signal that underlying stability is eroding. When the number of recurring incidents increases, it can indicate that root causes are not being addressed and governance follow-through is weakening. Beginners sometimes think technology health is purely operational, but in G E I T it is governance-relevant because governance is responsible for ensuring the enterprise can operate reliably within its risk tolerance. Leading indicators in this area are valuable because they reveal drift in lifecycle execution and change discipline before a major failure occurs. They also help leaders justify foundational investments, because the indicators provide evidence that the environment is trending toward higher risk and higher cost if nothing changes.

Data and information governance signals are another category, because many enterprise failures are driven by poor information quality and poor control over sensitive data, even when the technology stack itself seems stable. Leading indicators here can include rising numbers of data quality exceptions, increasing disagreement between reports, increasing use of shadow data stores, or increasing delays in data stewardship decisions. If classification and handling rules are not being applied consistently, you may see more incidents of misdirected sharing or more uncertainty about where sensitive data exists. If retention and disposal controls are drifting, you may see rising volume of data held beyond policy, which increases exposure and complicates compliance responses. Beginners often assume data problems are annoying but not dangerous, yet poor data controls can create real harm through privacy exposure, financial misreporting, and operational confusion. Leading indicators make these risks visible early by showing where governance behaviors are weakening, such as delayed access reviews or unclear ownership decisions. When you monitor these signals, you can correct drift by strengthening stewardship, clarifying definitions, and reducing uncontrolled copying before the organization becomes ungovernable.

A key governance concept for beginners is that leading indicators should include both compliance signals and capability signals, because governance is not only about avoiding failure but also about sustaining performance. Compliance signals might show whether required reviews are happening, whether controls are being executed, and whether evidence exists. Capability signals might show whether the enterprise can deliver change safely, respond to incidents effectively, and maintain stable operations while evolving. If you monitor only compliance, you might miss that teams are technically complying while outcomes are worsening, such as meeting a review requirement but producing low-quality review decisions. If you monitor only capability, you might miss that controls are being skipped, such as fast delivery that quietly increases risk. The best leading indicator sets balance both, so the organization can move with speed and maintain control. Beginners sometimes think balance means averaging everything, but balance means selecting indicators that reflect the tradeoffs leaders are actually making. When indicators are balanced, leadership can see whether the enterprise is drifting toward reckless speed or toward paralyzing caution, and governance can correct course while outcomes are still stable.

It is also important to design leading indicators so they are actionable, because a warning that does not guide action becomes a source of anxiety rather than improvement. An actionable indicator has a clear owner, a clear threshold for concern, and a clear set of likely response options. For example, if emergency changes rise, the response might include reviewing why planning is failing, adjusting release sequencing, or investing in stabilization work before new features. If patch backlog grows, the response might include reprioritizing workload, improving testing capacity, or reducing scope of new initiatives temporarily. If exception volume increases, the response might include reviewing whether standards are practical, whether capacity is insufficient to follow the standard path, or whether enforcement and communication are weak. Beginners might assume indicators should identify the root cause directly, but leading indicators often only identify a direction, and the next step is investigation and correction. What matters is that the indicator triggers a governance response mechanism rather than being observed and ignored. When indicators drive action, drift is corrected early and the enterprise avoids larger disruptions later.

Governance monitoring also needs to be resilient to manipulation and misunderstanding, because people naturally react to what is measured. If teams feel punished by indicators, they may hide issues or game the numbers, which reduces visibility and makes drift worse. This is why the tone of governance monitoring matters, because the goal is early learning and correction, not blame. A healthy approach treats leading indicators as signals of system health, inviting questions like what changed, what constraints are pressuring teams, and what support is needed to restore stability. Beginners might not expect culture to matter here, but culture determines whether indicators produce honesty or defensiveness. If leaders use indicators only to demand faster results, indicators become pressure amplifiers that increase drift. If leaders use indicators to make tradeoffs explicit, indicators become steering instruments that improve outcomes. Governance should communicate that early warning is valuable and that reporting a drift signal is responsible behavior. When the organization trusts the monitoring process, it becomes easier to surface problems early, which is the entire point of leading indicators.

Another essential piece is cadence, because leading indicators lose value when they are reviewed too rarely or in a chaotic, irregular way. Drift happens gradually, so the monitoring rhythm should be frequent enough to detect directional change before it becomes severe, while still being stable enough that the organization can act rather than constantly react. Many enterprises benefit from a regular governance review cadence that includes a small set of leading indicators, trend discussion, and decision-making about corrective actions. The important part is that the review produces commitments, like adjusting priorities, allocating capacity, strengthening controls, or correcting ownership issues. Beginners might assume monitoring is a dashboard someone glances at, but governance monitoring is a decision practice, where leaders use indicators to steer. Cadence also supports learning because you can observe whether corrective actions are working by watching indicator trends over time. When cadence is consistent, drift becomes visible as a gradual curve rather than as a sudden surprise, and the enterprise can respond calmly rather than in emergency mode.

Finally, leading indicators reveal drift most effectively when they are integrated across domains, because governance drift often shows up in patterns rather than in a single signal. For example, rising emergency changes, rising incident frequency, and rising patch backlog together suggest an environment under stress that is losing control of its change discipline. Rising exception counts, rising tool sprawl, and rising integration complexity together suggest architecture drift that will increase cost and reduce reliability later. Rising data quality exceptions, rising shadow datasets, and rising access review delays together suggest information governance drift that will threaten trust and compliance. Beginners sometimes look for one perfect indicator, but governance is a system, and system health is revealed through relationships between signals. Integrating signals helps leaders see whether a problem is local or systemic and whether the correct response is process improvement, resource rebalancing, or strategic reprioritization. When indicators are integrated and interpreted as patterns, governance becomes more predictive and less reactive. That is how leading indicators fulfill their purpose of revealing drift before failure.

As we close, monitoring governance with leading indicators is a way of making G E I T proactive instead of reactive, because it gives leaders early visibility into the behaviors and conditions that precede failure. Drift is the quiet enemy of governance, because it accumulates through small exceptions, overloaded teams, inconsistent controls, and growing complexity that still feels manageable until it suddenly is not. Leading indicators reveal that drift early by showing changes in process adherence, capacity pressure, technology health, and information governance behavior before outages, audit findings, and major incidents force attention. The most effective indicators are aligned to enterprise priorities, balanced across compliance and capability, and designed to be actionable with clear ownership and response options. When reviewed on a steady cadence and interpreted as patterns, these indicators become steering instruments that protect value delivery and reduce risk while there is still time for calm correction. For brand-new learners, the key takeaway is that governance success is not proven only by surviving failures, but by detecting and correcting drift early enough that many failures never occur. That is how leading indicators turn governance into a reliable system of control rather than a set of rules remembered only after something breaks.