Certified: The GIAC GPCS Audio Course

In this episode, we take a practical view of storage abuse detection by treating logs like raw material for building meaningful stories. Most organizations have some amount of storage logging turned on, but far fewer turn those events into narratives that explain who did what, to which data, in what volume, and over what time window. Abuse is rarely announced; it shows up as subtle changes in access patterns that only become obvious when you connect the dots. The goal is to build the discipline to capture the right events, keep them long enough to matter, and interpret them in a way that supports confident action. When you can translate storage logs into a coherent story, you move from reactive forensics to earlier detection and faster containment. That is where logs stop being a compliance artifact and start becoming an operational control.

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.

Storage abuse, in the context of cloud and enterprise storage, is unusual reads, writes, deletes, or permission changes that indicate misuse, compromise, or a breakdown in expected controls. Reads become abusive when they exceed the normal purpose, scope, or rate for an identity, especially when they involve sensitive datasets or broad retrieval across many objects. Writes become abusive when they modify or overwrite data outside a defined workflow, when they change integrity-sensitive datasets, or when they appear to be staging data for later movement. Deletes are especially high signal because they can indicate ransomware behavior, cover-up activity, or destructive errors with high impact, and they often appear in spikes rather than as a steady trickle. Permission changes are a different category of abuse because they can be the setup step, where an attacker or a careless change opens the door before the data actions begin. The unifying idea is that abuse is defined by deviation from expected behavior and expected authority, not by a single event type in isolation.

To detect that deviation well, you need to understand which log fields reveal identity intent and target, because those fields are what allow you to separate normal operations from suspicious behavior. Identity context is the first pillar, and that includes who the principal is, whether it is a human identity or a workload identity, and how it authenticated into the environment. Action context is the second pillar, and it includes the operation performed, such as read, write, delete, list, copy, or permission edit, along with any associated API operation names the platform emits. Target context is the third pillar, and it captures which bucket, container, dataset, prefix, or object was affected, because abuse often reveals itself through unusual targets even when the action type looks routine. Request context adds the fourth pillar, including source network indicators, user agent strings, and request identifiers that help you link related events. Finally, outcome fields like success or failure, error codes, and bytes transferred are what turn a simple audit line into evidence of volume and intent. When these fields are consistently captured, you can build stories that are defensible and actionable rather than speculative.

A scenario that makes these ideas concrete is a sudden spike in deletes after unauthorized access, which is both common and operationally painful. Imagine an identity is compromised and used to perform listings and reads for reconnaissance, followed by an abrupt shift into delete operations across a sensitive dataset. If retention policies are weak or lifecycle settings are permissive, the attacker may also attempt to purge versions or delete markers to make recovery harder. Even a non-malicious version of this scenario exists, where a misconfigured automation job is given broad delete access and starts cleaning up objects outside its intended prefix. In either case, the spike itself is the clue, because normal delete patterns usually have a predictable cadence, like periodic cleanup of temporary files, rather than a sudden wide sweep. The story you want to build from logs is a sequence: how did the identity authenticate, what objects were enumerated, when did the behavior shift from reads to deletes, and what was the measurable impact. That story lets you decide whether this is a bug, an insider mistake, or an active attack, and it also guides the fastest containment action.

Detection programs often struggle not because the analytics are impossible, but because basic logging discipline is weak. Missing logs are the first pitfall, and they appear when storage read events are not captured, when policy changes are not recorded, or when only a subset of accounts and regions are monitored. Short retention is the second pitfall, and it becomes critical because many incidents are discovered days or weeks after the initial access, especially when abuse begins with low-and-slow reconnaissance. Inconsistent coverage is the third pitfall, where one team has detailed logs and another has almost none, making enterprise-wide baselines and correlation unreliable. These pitfalls also amplify each other. If retention is short, analysts learn that older investigations are impossible, so they stop trying to build long timelines, and then the organization loses the habit of learning from multi-day patterns. If coverage is inconsistent, you end up with false confidence because dashboards look clean in the places you can see, while blind spots remain. Logging discipline is not glamorous, but it is the foundation that makes every later detection idea actually work.

Quick wins start with ensuring logs cover reads and policy changes, because those two categories capture the beginning and the setup of most abuse stories. Read coverage matters because bulk retrieval is a common exfiltration step, and without read logs you cannot reliably detect or scope it. Policy change coverage matters because many incidents begin with permission broadening, key policy adjustments, or public exposure changes that precede data movement. When you guarantee those events are captured consistently, you raise the odds that an investigation starts with evidence rather than guesswork. A practical quick win is also to standardize how log sources are collected so that analysts do not have to remember which dataset emits which fields. Even small normalization steps, like consistent naming for identity fields and target fields across sources, reduce time-to-triage. Finally, treat logging as a product: it must be reliable, monitored, and tested, because detection fails quietly when logs stop flowing or start dropping key event types.

A valuable practice skill is building a timeline that links identity events to storage actions, because isolated storage events rarely tell the full story. Start the timeline with identity signals, such as new logins, unusual authentication methods, privilege changes, or session creation events. Then connect those identity events to storage operations by using request identifiers, principal identifiers, and time windows that show causality rather than coincidence. The timeline should show the sequence of actions, such as list operations that indicate discovery, followed by reads that indicate collection, followed by writes or deletes that indicate tampering or cleanup. You also want to capture changes in behavior, such as a shift from a narrow prefix to broad traversal of a dataset, or a shift from interactive access to automated high-rate access. The output of the timeline is not just a list of events. It is an explanation of progression, because progression is what distinguishes a benign anomaly from an unfolding incident. When you can narrate the progression clearly, you can justify containment decisions with confidence.

Baselining normal access is what allows you to detect abuse without drowning in false alarms. Baselining is not about perfection; it is about establishing enough context that an alert can be evaluated quickly. Normal access can be defined by which identities typically touch a dataset, what operations they perform, what times of day they operate, and what volume of objects or bytes is typical for them. It can also include which network sources are expected, especially for administrative actions and high-sensitivity datasets. Without a baseline, every large transfer looks alarming and every rare permission change looks catastrophic, which teaches teams to ignore alerts. With a baseline, you can ask whether the observed event is outside the envelope of expected behavior in multiple dimensions, such as volume and identity and time. Multi-dimensional baselines are powerful because they reduce reliance on any single threshold. Even if volume is high, it may be normal for a backup job, but it becomes suspicious if it is high volume from an identity that never runs backups, from a new location, at an unusual hour.

One of the highest-value correlations is between identity permission changes and subsequent storage access, because attackers often need to expand capability before they can move data. Identity and Access Management (I A M) changes can include adding a principal to a powerful group, attaching a broader role, creating new access keys, or altering trust relationships that allow new execution contexts. After that first definition, I A M changes should be treated as leading indicators when they are unexpected, especially if they occur close in time to abnormal storage activity. The correlation story is often clear: an identity gains new rights, then immediately performs listings and reads on datasets it could not access before. This correlation is also helpful for distinguishing mistakes from attacks. A planned change should have a ticket, a known requester, and an expected outcome, while an attacker-driven change often happens without the normal change control signals and is followed by rapid use of the new permissions. When you instrument this correlation, you can catch incidents at the privilege expansion step, which is earlier than catching them at the data theft step.

Alert prioritization is how you ensure analysts spend time on the signals that most reliably indicate high impact. Public exposure alerts should be high priority because they can instantly turn private data into internet-accessible data, and they often have enterprise-wide implications. Key policy edits, especially changes that broaden key usage or reduce constraints, should also be prioritized because they can undermine encryption and make stolen objects immediately usable. Mass access signals, including spikes in reads, listings, or downloads, are another priority because they are common exfiltration indicators and often have clear volume signatures. Permission changes on storage resources themselves are also high priority, particularly when they broaden principals, remove conditions, or enable anonymous access. A disciplined priority model is not just about severity in theory. It is about the likelihood that the alert represents real harm and the speed at which harm can occur. If everything is priority one, nothing is, so the goal is to assign urgency to the alerts that compress the time window between detection and irreversible impact.

The memory anchor for making storage abuse detection reliable is: identity, action, object, volume, and time. Identity tells you who is acting and whether that actor is expected for this dataset. Action tells you what they are doing and whether it is consistent with a legitimate workflow. Object tells you what data is being touched, and whether the targets are sensitive or unusually broad. Volume tells you how much activity is occurring, which is a strong indicator for exfiltration, mass deletion, or bulk tampering. Time tells you when it happened and how the activity is distributed, because attackers and broken automations often have distinctive time patterns. This anchor works because it forces you to look at multiple dimensions instead of jumping to conclusions based on a single event. It also gives you a consistent way to describe incidents to stakeholders, because you can summarize the story in the same five dimensions every time. When your team shares that mental model, triage gets faster and less emotionally driven.

A quick mini-review of top storage abuse patterns helps because you want recognition, not reinvention, during real investigations. A burst of listings followed by a burst of reads implies reconnaissance followed by collection, which is often a precursor to exfiltration. A sudden increase in deletes, especially across many prefixes or datasets, implies destructive intent, ransomware-like behavior, or an automation failure with broad permissions. Permission broadening followed by immediate access implies privilege escalation and exploitation of new capabilities. Cross-boundary copies imply data movement into an ungoverned space, which can be exfiltration even if the data never touches the public internet. Repeated access failures followed by success can imply a probing attacker learning which permissions exist or a misconfigured script being adjusted until it works. Unusual key usage patterns paired with abnormal reads can imply an attempt to ensure stolen data is decryptable. These patterns are not proofs on their own, but they are strong hypotheses that guide the next questions and containment actions.

When an investigation starts, you want the first questions to be consistent and high-leverage so you do not waste time on low-value speculation. The first question is which identity performed the suspicious action and whether that identity’s behavior matches its normal role, including whether the authentication context and source location are expected. The second question is what data was affected, specifically which objects or datasets were touched, and whether the scope is narrow or broad relative to normal behavior. The third question is whether there were any recent permission or policy changes that would explain or enable the activity, including I A M changes, storage policy changes, or key policy changes. These questions align directly with the memory anchor and force you to connect identity, action, object, volume, and time into a coherent story quickly. They also help you decide whether immediate containment is necessary before you fully understand the scope. If the answers indicate high impact and ongoing activity, you contain first and investigate second, because time is part of the risk.

To conclude, take one day of storage and identity logs and summarize what normal behavior actually looks like, because that exercise is what makes baselining real. You want to be able to describe which identities typically access each critical dataset, what operations they perform, what the usual volume is, and what time windows are common. You also want to note which types of events are rare, such as deletes on sensitive datasets or policy changes outside approved change windows, because rarity is often the seed of high-signal detection. As you summarize, look for gaps in coverage, such as datasets with no read logs or policy changes that are not recorded, because those gaps are where abuse can hide. Treat the summary as a living reference, not a one-time report, because normal behavior changes with projects and seasons. The decision rule is simple: if you cannot describe normal access clearly for a dataset, you cannot reliably detect abnormal access for that dataset, so baseline it before you trust your alerts.