Episode 93 — Spotlight: Event Logging (AU-2)
Welcome to Episode 93, Spotlight — Event Logging, also known as Control AU-2. Logging is the memory of a system, the record of what really happened. Without it, investigations depend on guesswork and security becomes blind. Event logging ensures that significant activities—both normal and suspicious—are captured in structured form so they can be analyzed, correlated, and acted upon. The goal is not to log everything but to log what matters, with enough context to understand cause and impact. When designed properly, event logging transforms raw data into institutional awareness. It tells the organization’s story in real time, one entry at a time.
Every log entry should capture four essentials: the actor, the action, the target, and the outcome. The actor is who or what performed the event. The action describes what was attempted. The target is the object affected, such as a file, database, or account. The outcome states whether it succeeded or failed. Together, these elements tell a complete story in miniature. For instance, “User X deleted file Y from server Z, success” is enough to reconstruct intent and consequence. Omitting any element breaks the chain of accountability. When each event describes who did what to which resource and with what result, clarity emerges automatically.
Coverage must span systems, identities, and networks, not just the security tools themselves. Application servers, endpoints, cloud platforms, firewalls, and identity providers all generate signals that fill different parts of the puzzle. If one domain goes unlogged, adversaries will find and exploit that blind spot. A complete strategy lists every system that produces or should produce logs, defines their retention and transport paths, and verifies that data arrives as expected. Breadth without depth creates noise; depth without breadth creates holes. Balanced coverage ensures that events from one system can explain or confirm those from another.
Logging both successes and failures provides perspective. Success logs show what users and systems normally do, helping define baselines and detect subtle deviations. Failure logs highlight attempted but denied actions, signaling potential attacks or misconfigurations. For example, repeated failed logins may indicate brute force, while repeated successful ones at odd hours may suggest account misuse. Recording both sides ensures that monitoring does not overlook quiet patterns that reveal risk. Omitting success data saves storage but loses context. A full picture of behavior comes from contrast—what went right and what almost went wrong.
While completeness is vital, avoid storing sensitive content unless absolutely necessary. Logs should capture metadata—who accessed a record and when—not the full contents of that record. Accidentally logging personal data, passwords, or encryption keys creates privacy and compliance liabilities. Redaction or tokenization tools can strip sensitive fields before storage. For instance, record that a payroll file was opened, not its salary details. Logging should protect the organization, not create new exposures. Write policies defining what must never appear in logs and enforce them technically where possible. Security evidence should never double as sensitive data storage.
Before logs enter storage, validate their schema and format at the point of ingestion. Consistent structure allows automation to parse and correlate entries accurately. Schema validation checks field presence, data type, and timestamp formatting. Reject or flag malformed logs rather than letting them pollute the repository. A simple scenario illustrates this value: if one system sends date fields in local time and another in UTC without tagging, timelines will break. Validation standardizes language across systems, turning logging from chaos into coherence. Good data in leads to good analysis out.
Every log source needs documented owners, reviewers, and review cadences. Ownership ensures accountability for configuration, retention, and accuracy. Reviewers verify that logs remain active, complete, and relevant. Cadence means scheduled checks—daily for high-value systems, monthly for supporting ones. For example, a log owner in network operations may verify that firewall logs continue streaming to the SIEM without gaps, while a security analyst reviews authentication logs weekly for anomalies. Assigning names and intervals converts responsibility into routine. Logs without owners drift into neglect; owners keep the memory alive and trustworthy.
Evidence for this control includes source inventories, configuration settings, and representative samples of captured logs. An evidence package might list all active log-producing systems, their event categories, and a few anonymized examples showing required fields present. Samples confirm that the control operates as described. They also demonstrate coverage breadth and formatting quality. Auditors and internal reviewers use these artifacts to verify both implementation and consistency. Producing such evidence should be effortless if the program is organized—simply export what already exists. If evidence gathering feels like a hunt, logging discipline needs improvement.
Exceptions and temporary gaps must be recorded with transparency. Sometimes logging stops during upgrades, storage migrations, or vendor transitions. Document these events, explain why, list affected systems, and show what compensating measures—like enhanced network monitoring—were applied. Include expected restoration dates and confirmation once coverage resumes. Unacknowledged silence is worse than known absence. When gaps are visible, risk can be managed; when hidden, risk becomes denial. Logging programs mature when they treat downtime with the same formality as uptime, preserving trust in the record even when the record briefly pauses.
Metrics keep logging aligned to objectives. Track coverage percentage, data freshness, ingestion errors, and integrity checks. Coverage shows what fraction of systems log correctly; freshness measures delay from event to central record; integrity confirms no tampering. A simple dashboard might display ninety-eight percent coverage, five-minute freshness, and zero checksum failures this week. Trends reveal drift and justify resource adjustments. Metrics make conversation factual: are we seeing all we should, soon enough, and intact? When measured, logging evolves from background function to strategic asset, guiding investments where visibility matters most.
