Audit Trail Documentation

Dear IT Auditors, Documenting Audit Evidence: What’s Enough? Every audit rests on one foundation, evidence. Without it, our conclusions are nothing more than claims. But here’s the challenge: what qualifies as sufficient and appropriate evidence? I’ve seen both extremes: 1. An audit file filled with screenshots and assumptions. 2. Another buried in 300 pages of irrelevant data that add no real value. Neither inspires confidence. The right approach sits in the middle; structured, relevant, and defensible. 📌 Tie evidence directly to the control objective If the evidence doesn’t map back to what you’re testing, it’s noise. 📌 Use reliable and independent sources Data should come from the system of record or a trusted source, not manually altered extracts. 📌 Cover the entire testing period A single transaction or a point-in-time snapshot won’t always cut it. Think coverage and completeness. 📌 Ensure clarity and re-performability Another auditor should be able to review the same evidence and reach the same conclusion without your explanations. 📌 Check the metadata Who generated the report? When was it pulled? From where? Metadata often makes the difference between weak and defensible evidence. 📌 Go beyond screenshots Screenshots can support, but they rarely stand alone. System logs, exports, and automated reports are far stronger. 📌 Maintain version-controlled documentation Evidence should be stored in a centralized, secure repository where updates and history are traceable. Here’s the truth: your findings are only as strong as the evidence behind them. And if you’re ever explaining your audit to regulators, leadership, or even a legal team, you’ll want more than a folder full of screenshots. Build your evidence file as if it will be questioned, because one day, it almost certainly will. #AuditEvidence #ITAudit #InternalAudit #ControlTesting #GRC #CyberAudit #AuditStandards #ISAssurance #CyberVerge #CyberYard

Your AI pipeline is only as strong as the paper trail behind it Picture this: a critical model makes a bad call, regulators ask for the “why,” and your team has nothing but Slack threads and half-finished docs. That is the accountability gap the Alan Turing Institute’s new workbook targets. Why it grabbed my attention • Answerability means every design choice links to a name, a date, and a reason. No finger pointing later • Auditability demands a living log from data pull to decommission that a non-technical reviewer can follow in plain language • Anticipatory action beats damage control. Governance happens during sprint planning, not after the press release How to put this into play 1. Spin up a Process Based Governance log on day one. Treat it like version-controlled code 2. Map roles to each governance step, then test the chain. Can you trace a model output back to the feature engineer who added the variable 3. Schedule quarterly “red team audits” where someone outside the build squad tries to break the traceability. Gaps become backlog items The payoff Clear accountability strengthens stakeholder trust, slashes regulatory risk, and frees engineers to focus on better models rather than post hoc excuses. If your AI program cannot answer, “Who owns this decision and how did we get here” you are not governing. You are winging it. Time to upgrade. When the next model misfires, will your team have an audit trail or an alibi?

#GRC It’s how little of the job is actually about finding the risk and how much of it is about tracking what people decide to do with it. One of my early projects involved reviewing a system where access wasn’t being removed when employees left. I flagged it, explained the impact, walked through the risk. Everyone nodded. And then… nothing changed. A few weeks later, during a walkthrough, someone asked, “Was this risk ever reviewed or accepted?” That’s when it clicked to me. It wasn’t enough that I’d raised the concern. I hadn’t captured who made the decision to leave it as-is, or why. There was no clear record of what was said, or when it was decided. Now, I always document those moments. Not just the risk, but the conversation around it; who was involved, what they agreed on, and what context shaped that choice. Not to point fingers. Just to keep a history. So if that risk resurfaces, we’re not scrambling to remember what happened or why. For anyone learning GRC .. spotting a gap is just one step. The actual work is in following it through; making sure it’s not just noted, but owned, discussed, and either acted on or intentionally accepted. And keeping that trail matters more than you think. Here’s a few of my recommendations: 1. Risk Acceptance vs Risk Mitigation (Article by TechTarget) Breaks down how risks are either accepted or acted on, and why documenting the decision matters. https://lnkd.in/g82uYRk6 2. Hyperproof Risk Ownership and Documentation Best Practices A plain-language overview of how GRC teams manage risk conversations, decision logs, and assignments. https://lnkd.in/gzWZUBah 3. GRC Fundamentals Training by ISACA (Free & Paid Options) Includes lessons on risk management, documentation, and audit readiness. https://lnkd.in/gDPyqv24 4. The Importance of an Audit Trail (OneTrust Resource) Covers why clear documentation is your strongest evidence in any control or risk review. https://lnkd.in/gfB5EE5k

Most people treat 21 #CFR Part 11 like background noise. But here’s what no one told me: Your #career can stall if you don’t understand it. And your #site can get audited if you ignore it. ↳ 21 CFR Part 11 isn't about tools. It's about trust. When I started in #clinical research, I thought #Part 11 was just “for the tech team.” Wrong. It touches everything — from source documentation to patient safety. Here’s how I learned the hard way: A #monitor flagged missing #audit trails during a site visit. We had #electronic records… but no timestamps. No record of who signed what. No validation of changes. It looked sloppy. It was sloppy. We didn’t just lose credibility — we lost the trial. ↳ That’s when I got serious about learning Part 11. Let me break it down like I wish someone had for me: ✅ #Electronic signatures = digital proof you approved a record (Page 5) ✅ #Audit trail = the secure, timestamped log of all activity (Page 6) ✅ #Validation = you must test the system before trusting it (Page 7) ✅ #Record retention = you must be able to retrieve data for years (Page 8) ✅ #Scope = Part 11 applies to regulated records, not your drafts (Page 9) And here’s what most people miss: ➡️ Even freshers need to get this. If you don't understand integrity, traceability, and e-signatures — you’re just "clicking buttons." But the ones who think like data managers? They lead trials, not just follow instructions. ↳ You don’t need to code. You just need to care. Ask yourself: If an FDA auditor shows up tomorrow… Can you prove who changed what, when, and why? If not — your compliance is just hope in disguise. Follow Rudy Malle for more real-world breakdowns like this. I don’t sugarcoat. I simplify. #clinicalresearch #gcp #part11 #dataintegrity #careerdevelopment ↳ Let’s build careers that actually protect patients.

Five Data‑Integrity Bombs Hidden in Your Audit Trail (That FDA Investigators Actually Flag) When I was with the FDA, I didn't need to catch someone deleting data red-handed. Often, all it took was following the digital breadcrumbs. And surprisingly, those "breadcrumbs" were frequently right there in the audit trail, completely overlooked and unreviewed. Here are five metadata flags that have led to actual FDA findings during audits I've either conducted or reviewed: 1. Reprocessing Without Justification: I once saw an analyst reprocess a chromatogram three times before reporting a "passing" result. The audit trail clearly showed the same user and system, but there was no note-to-file or deviation recorded. This led to a 483 finding for a lack of controls to detect or prevent data manipulation. 2. System Time Discrepancy: During a lab inspection, we noticed the system time was off by nine minutes compared to the actual clock. That small difference explained why peak integration times weren't lining up. The result? A 483 finding for failing to ensure time-synchronized systems for traceability. 3. Using ‘Sample Sets’ as a Draft Mode: One lab was using "sample sets" as trial runs before recording what they considered "official" data. The audit trail, however, told the whole story. This resulted in a 483 finding for intentionally bypassing original data capture, which undermines data integrity. 4. Missing Audit Trail for Critical Instruments: While the HPLC audit trail was perfectly intact, the balance used for weighing had no audit trail enabled at all. This led to a 483 finding due to incomplete control over data acquisition systems. 5. Audit Trail Disabled by Admin Rights: This is a huge red flag: an IT admin with unrestricted access had disabled the audit trail function three weeks before an inspection, claiming it was "for system maintenance." This resulted in a 483 for failing to ensure complete and accurate records. If you're in Quality or Lab Management, ask yourself: When was the last time you reviewed your audit trail with the same critical eye an FDA Investigator would? I dig into these points even further in my Data Integrity PowerPoint training, showing you exactly what to look for in systems like Empower, LIMS, and standalone balances. You can find it at https://lnkd.in/e9vwyahP.

A good auditor will assess your processes, systems, and data with this type of mental checklist. How would your system hold up? Attributable: Can I see who did it without detective work? (Name, role, e-signature, timestamp, audit trail.) Legible: Would a stranger understand it six months from now? Contemporaneous: Was it recorded as work happened, not at day-end? Original: Is this the true source (or a controlled, verified copy)? Accurate: Calculations auto-checked; instruments calibrated; no “friendly rounding.” Documentation reflect the actual work performed. Complete: Raw data, changes, voids, exclusions, and repeating data included. Nothing disappears. Consistent: Same units, formats, versioning, and time zones across systems. Enduring: Stored in a controlled system that survives audits, turnover, and IT issues. Available: Can QA retrieve it in <2 minutes during an audit? ALCOA+.

Ever had a user quietly change a note in your Power App… and poof, the original’s gone? 😬 In apps where accountability matters, that’s a big problem. In my latest video, I show you how to use Patch to append rich text notes—so instead of overwriting, you always add to the history. Every note gets: ✅ The user’s name ✅ A timestamp ✅ Rich text formatting for easy reading No sneaky edits, no lost info—just a clean, reliable audit trail. Perfect for ticket systems, approval workflows, audit logs, and more. 🎥 Watch it here → https://lnkd.in/gEht3Sju Part 2 is coming soon, where we’ll turn those notes into PDFs and store them in SharePoint for even more auditing power! #PowerApps #PowerPlatform #Microsoft365

Ever wondered how regulated industries prove their data hasn't been tampered with? The answer lies in audit trails - the digital DNA that tracks every single change to your critical data. In clinical trials and GxP environments, they're not just helpful - they're mandatory. Here's the reality: Data integrity violations are costing companies millions in regulatory penalties, and one deleted record or undocumented change can derail years of research. But audit trails serve as your invisible shield - these automated guardians don't just track changes, they tell the complete story of your data's journey. Whether you're in clinical operations, quality assurance, regulatory affairs, or IT - understanding audit trails can be the difference between a successful FDA inspection and a compliance nightmare. 📖 Read the article below to discover: - What exactly constitutes an audit trail and why it's crucial - The 5 key types of audit trails you need to know - How they align with ALCOA+ principles for data integrity - Real-world applications in clinical systems (eTMFs, CTMS, EDCs) - Essential compliance requirements to avoid regulatory observations If you work with regulated data, mastering audit trails is essential for both compliance and career advancement. What do you think are the biggest challenges in managing and maintaining effective audit trails? Share your experiences in the comments below! 👇