Automating Evidence Mapping for Audit Testing

Manual evidence collection is a relic of point-in-time audits. Continuous monitoring flips the script: The system sends us evidence. - Use AWS Config, Security Hub, or GCP SCC to emit JSON findings continuously. - Land everything in an S3 “evidence lake” with stamped hashes. - Every failed control triggers a Slack alert and writes a record auditors can inspect. - Quarterly audit? The data is already there. No heroic screenshot sprints required. If your evidence isn’t collected by code while you sleep, is it really “continuous”monitoring? Automating evidence frees humans to interpret risk instead of hunting files. This is exactly where smart GRC engineers add value. #GRCEngineering

Last week, I shared how we automated 175+ SOX tests in 90 days. It generated a lot of “how are you actually doing this?” conversations - especially from teams trying to do the same. TLDR: We’re saving human hours without offloading decision-making to the models. By automating the work that doesn’t require judgment, we’re raising the bar on the work that does. Most SOX testing was an execution vs. judgment problem — and that’s what we targeted. A few questions kept coming up: 1. What’s automated vs. human? The model does the heavy lifting: - parses evidence - applies test criteria - drafts workpapers - tickmarks It also produces a proposed conclusion. The human: - reviews the evidence - challenges the reasoning - decides if it actually holds 👉 We don’t offload judgment — only execution Auditors move from executing tasks → tackling work that actually requires expertise and solving higher order problems 2. What controls work best (and why)? Fastest wins: - ITGCs - key reports - transactional controls Why? They’re more: - rule-based - evidence-driven - repeatable More complex controls take more upfront context. We don’t view that as a limitation — it’s sequencing. Once the context is built, it compounds every cycle. We expect 90%+ of controls to be tested this way over time. 3. What changes with external audit? The standard doesn't. They still reperform. What changes: - the machine catching things humans missed - more consistent documentation - workpapers delivered earlier Net: lower execution risk, not higher 4. Why not just use ChatGPT or Claude CoWork? Because this isn’t a one-time prompt. It has to work: - repeatedly - at scale (hundreds of controls) - near-right every time (or manual rework kills the ROI) It also has to: - learn from and retain context specific to our environment - tie every conclusion back to evidence - produce clearly traceable outputs If you can’t repeat it, trust it, and prove it, it doesn’t work for audit. General AI is flexible. Audit requires: 👉 consistency 👉 deep context 👉 provability That’s the gap at audit-grade standards.

🚨 New Open-Source Project Drop: 58 PowerShell Security Functions — Built from Real Incidents ⚡ After years of responding to breaches that could’ve been prevented with basic visibility, I decided to stop complaining and start building. Too many teams face the same challenges: ❌ Enterprise tools priced between $50K–$200K/year ❌ SMBs locked out of critical capabilities ❌ Security analysts drowning in 20+ disconnected tools ❌ Compliance audits eating weeks of manual effort So, I consolidated years of enterprise security experience into one free, open-source PowerShell module — a single toolkit designed for defenders who need speed, visibility, and automation. 🧠 What’s Inside (58 Functions, One Module): 🎯 Threat Detection & Hunting • APT indicator mapping (MITRE ATT&CK aligned) • Living-off-the-land technique detection • Data exfiltration and lateral movement monitoring 🔐 Active Directory Security • Kerberoasting and Golden/Silver Ticket detection • AdminSDHolder & privilege escalation audit • Domain misconfiguration and stale admin checks 📊 Compliance Automation • CIS Benchmark validation (95% automated) • NIST 800-53 & PCI-DSS control checks • Evidence collection and audit-ready reports 🔍 Advanced Analysis & Forensics • Memory and process injection analysis • Registry persistence hunting • Event log correlation and attack timeline reconstruction • Network anomaly detection ⚡ Impact So Far: ✅ Reduced audit time from 3 days → 2 hours ✅ Detected persistence that 6-figure EDRs missed ✅ Automated 90% of compliance evidence collection ✅ Saved clients $50K+ in annual license costs 🖥️ How to Start: # Install from PowerShell Gallery Install-Module WindowsSecurityAudit -Force # Run full system scan Invoke-SecurityAssessment -Verbose # Generate report Export-SecurityReport -Format HTML -Path C:\Reports 🎯 Ideal For: ✔ SOC & Blue Team Analysts ✔ IT & SysAdmins ✔ Security Consultants ✔ MSPs ✔ Students learning real-world security 🧩 Tech Stack: Pure PowerShell (5.1+) Zero dependencies Windows 10/11/Server 2016+ 14 specialized modules Battle-tested in enterprise environments 🔗 GitHub: https://lnkd.in/diWGeq2j ⭐ If it helps you, give it a star — it’ll help others find it too. 💬 Question: What’s the one detection or automation capability you’d love to see added next? (My v2.0 focus: container security scanning 🔒) #CyberSecurity #PowerShell #ThreatHunting #BlueTeam #IncidentResponse #WindowsSecurity #DFIR #OpenSource #SecurityAutomation #SOC #InfoSec #DevSecOps #SIEM #SecurityEngineering

Orchestrating AI Agents in Internal Audit: A Game Plan Alas a lot of Internal Audit teams are “using AI” the way they once used Excel macros: helpful, but basically one tool doing one thing. In 2026 - the year of AI agents - the approach needs to be one of orchestration. Orchestration is different. It’s when you’ve got a small team of AI agents, each with a clear job, handing work to each other and leaving a trail that wont get laughed out of the room. So what is the "game plan" - in some respects it is easier than you think (base level is if I can work it out so can you). 1) Stop starting with “use cases” - start with your audit workflow Write your audit work out as a simple chain: assessment, scoping, evidence, testing, judgement, reporting, follow-up Now you’ve got a map. Agents plug into stages. That’s orchestration. 2) Build a squad, not a superhero One mega-agent sounds awesome (and is what is occasionally being sold!) … until it confidently makes something up and you can’t tell where it came from. Instead, think “team roles”: - Scout Agent: spots risk signals (incidents, KRIs, change logs) - Scope Agent: drafts scope + criteria options (and what’s out) - Evidence Agent: requests data, checks completeness, logs lineage - Test Agent: proposes tests, runs scripts where allowed - Sceptic Agent: tries to break the conclusion (bias checks, alternate explanations) - Writer Agent: drafts the report from evidence objects, not vibes Humans stay in charge of the stuff that matters: scope calls, severity judgement, stakeholder heat, final sign-off. 3) Put the guardrails where audits actually die Most audits don’t fail because people can’t write. They fail because: evidence is thin, assumptions go unchallenged, conclusions are too big for the data. So: - quality gates at handoffs (“no evidence, no next step”) - provenance on every claim (what dataset, what date, what method) - separation of duties (the agent that tests shouldn’t be the one that concludes) - full audit trail (prompts, outputs, versions, datasets) 4) Run it like a “control room” You need a simple operating model: - a queue of work items by audit/risk theme - playbooks per audit type - exception rules (when agents disagree, escalate) - metrics: cycle time, rework, false positives, coverage 5) Scale the boring way (which is the smart way) Pick one audit area (payroll, AP, cyber access, procurement) and build the full chain end-to-end. Then clone the playbook. If your orchestrated agents can’t clearly show why they believe what they believe, you haven’t automated assurance - you’ve automated confidence. Confidence is not evidence. (The attached NotebookLM generated from the above is a pretty decent playbook to start!)

The utilisation of AI in cybersecurity is rendering traditional GRC approaches obsolete. This development is positive. For years, GRC was treated like a paperwork tax. Spreadsheets. Annual audits. Box-ticking exercises that lag reality by months. That model is already dead. If your GRC still wakes up only during audit season, you’re lying to yourself about risk. What actually changes? Four pillars. 1️⃣ Automated Policy Mapping – the control truth engine Manual cross-mapping controls across ISO, NIST, GDPR, PCI, and SOC2 is slow and error-prone. - AI reads the regulation itself. Not keywords. Intent. - It compares new requirements against your internal controls and tells you exactly what’s covered, what’s weak, and what’s missing. - It even drafts the missing clause. Result: comply once, report everywhere. No army of consultants needed. 2️⃣ Proactive Risk Scoring – gut feeling is gone High/Medium/Low based on opinion is useless. AI scores risk using live signals: – exposed assets – failed controls – active exploits – sector-specific attack patterns It doesn’t just say “critical vulnerability.” It says: this system holds regulated data and attackers are already abusing this flaw. That’s decision-grade information, not noise. 3️⃣ Dynamic Compliance – audit season is over Compliance becomes continuous, not episodic. - APIs connect directly into cloud, IAM, CI/CD, ticketing, and logs. - Controls are checked constantly. - Someone disables encryption? Drift detected instantly. - Evidence? Collected automatically, time-stamped, and stored without human screenshots or excuses. Your audit readiness is visible every second. When it drops, you know why. 4️⃣ LLMs – the intelligence layer humans actually use. Raw GRC data is useless unless someone can act on it. LLMs: – translate regulations into executive-level summaries – answer direct questions like “Are we ready for SOC 2 next month?” – explain failures and give fix instructions engineers can execute – draft policies that actually match how the company operates This is where GRC stops being a tool and becomes a system. The feedback loop is the real shift Policy mapping defines what “good” means. Dynamic compliance checks if you’re doing it. Risk scoring flags where “good” is no longer enough. LLMs explain all of it to leadership in plain language. That’s not bureaucracy. That’s control. If your AI story in cybersecurity doesn’t include AI GRC, you’re defending faster systems with slower thinking. And that's where breaches happen. #AIGRC #CyberSecurity #AIinCyber #Governance #RiskManagement #Compliance #CISO #SOC2 #ISO27001 #NIST #CloudSecurity #BoardReporting #DigitalRisk #GRC #ISO #SOC

🚀 How to Automate SOX Testing With RPA (Robotic Process Automation) SOX testing doesn’t have to feel like a quarterly fire drill. With RPA, you can automate evidence collection, control testing, and documentation — freeing your IT, Finance, and Audit teams to focus on analysis, not admin work. Here’s how forward-thinking audit and risk teams are doing it 👇 1️⃣ Map and Prioritize Controls Identify repetitive, rule-based SOX tests — like access reviews, change management, and key report validations — that can be automated first. 2️⃣ Design “Audit-Proof” Bots Document every bot like a control: purpose, inputs/outputs, logs, and approvals. Treat bot logic changes as in-scope for SOX. 3️⃣ Build Securely Use vaults for credentials, enforce least privilege, and integrate bots into your GRC or evidence repository. 4️⃣ Test and Validate Compare bot outputs to human results (UAT). Capture logs, screenshots, and timestamps for every run. 5️⃣ Monitor and Improve Set quarterly “Bot Health Reviews” to track exceptions, false positives, and ROI. ⚙️ Common RPA Use Cases for SOX User Access Reviews — auto-pull users, compare to HR, generate exceptions Change Management — match commits to approvals and deployments Key Report Testing — re-execute reports and hash results Backups/Job Monitoring — verify completion and collect evidence ⚠️ Key Challenges Data quality issues → fix upstream, validate populations Credential sprawl → dedicated bot IDs + vaulting Change control gaps → ticket every update Auditor reliance → document bot design + test scripts ✅ Outcome: Organizations are cutting SOX testing time by 50–70%, reducing human error, and providing auditors with complete, timestamped evidence bundles every quarter. 💡 Pro tip: Start small — automate 3–5 high-ROI controls first, measure results, and scale. #SOXCompliance #InternalAudit #RPA #TechRisk #Automation #AuditInnovation #CISO #GRC #ITAudit #DigitalTransformation

The first time I heard "GRC engineering" I thought it was just spreadsheets with a better job title. Then I lived through managing evidence collection across multiple concurrent audits with a small team and a tight timeline. Different frameworks, overlapping controls, redundant requests — all pulling from the same systems but packaged separately. That's when it clicked. The problem wasn't the people. It was the architecture. Most companies add frameworks year after year without unifying the controls underneath them. Every new framework adds cost, audit fatigue, and engineering friction. Eventually the compliance burden starts working against the business instead of enabling it. When compliance is engineered well, it becomes a growth enabler. New customer requirements get met faster. Sales cycles don't stall on security questionnaires. Adding the next framework is a mapping exercise, not a six-month project. Your security posture actually improves as a byproduct of the compliance work, because continuous evidence collection surfaces control failures before auditors do. If you're feeling the pain and wondering where to start: 1. Unify your controls — map overlapping requirements across frameworks into a single control set your teams operate against. 2. Build your collection framework — automate evidence gathering using what you already have. Python, CI/CD pipelines, your data warehouse. You don't need a six-figure platform. 3. Build process and monitoring around it — track control effectiveness over time, surface exceptions proactively, and turn your evidence into metrics that tell you something useful. I wrote a longer piece on how we're approaching this, including what role AI is starting to play in accelerating the work. Full post on my blog: https://lnkd.in/gpJADukW

What Role Is AI Playing in Automating Test Evidence and Validation Documentation Today? After working with 1000s of CSV professionals, 90% ask me the SAME question about AI (and it's not what you think). Not 'What AI tools should I use?' - but 'How do I use AI without getting shut down by regulators?' I think it is the right Q to ask. Because I feel finally, regulators stopped being vague- FDA and EMA are literally handing us the AI compliance playbook. Lets review together: 1) FDA's CSA guidance (2025) reframes validation as risk-based assurance - test what matters, reduce paperwork. AI becomes the evidence generator - analyzing logs and audit trails for "objective evidence." 2) EU's Draft Annex 22 emphasizes continuous monitoring, human oversight, and data integrity. AI + Automation = Smarter Evidence Your new workflow could look like this: Capture logs & data → Process via AI → Generate structured evidence → Human review & sign-off → Audit-ready package What's Happening NOW: 1. Automated Evidence Generation AI captures logs, screenshots, execution data → transforms into audit-ready records 2. Smart Risk Assessment AI assigns risk levels and testing rigor - mirroring CSA's "high vs not-high process risk" logic 3. Continuous Assurance Forget one-time validation. AI monitors validated state in real-time 4. Digital Records as Primary Evidence Regulators are clear: digital audit trails are preferable to screenshots Here's a practical Checklist for Your Team: ✅ Define Context of Use for each AI model ✅ Preserve end-to-end provenance with timestamps ✅ Maintain human-in-the-loop gates ✅ Log drift and metrics continuously ✅ Keep explainability artifacts ✅ Validate AI tools themselves first Question: What's your biggest challenge with AI in validation today? -- Lets learn AI together. Follow Sreejith Kanhirangadan for more insights.

Dear IT Auditors, Embedding Continuous Auditing with Data Analytics Traditional audit methods rely on periodic sampling. This approach leaves large blind spots and delays the detection of critical control failures. In 2025, IT auditors need to embed continuous auditing powered by data analytics. This shift transforms audit from a backward-looking review into a proactive source of assurance. 📌 Define what continuous auditing means Continuous auditing is not running controls more often. It is the automated collection, analysis, and reporting of control evidence at defined intervals or in real time. For example, instead of sampling 50 user accounts quarterly, you monitor every provisioning and deprovisioning event daily through automated scripts. 📌 Prioritize high-value areas first You do not need to automate everything on day one. Focus on areas where manual testing is costly or where risk exposure is highest. Examples include privileged access reviews, segregation of duties, and financial transaction monitoring. These domains have high impact and data-rich environments that lend themselves to automation. 📌 Use analytics to increase coverage Sampling only 5 to 10 percent of transactions is not enough in high-risk environments. With analytics, you test the entire population. This not only improves assurance but also builds credibility with executives. When you show that your audit covered 100 percent of access requests, your insights carry more weight. 📌 Build repeatable workflows Continuous auditing is most effective when processes are standardized. Use scripts, dashboards, and alerting tools that can run repeatedly with minimal manual effort. For example, integrate logs into a data warehouse and set thresholds for exceptions. When thresholds are breached, alerts feed directly to the audit team for review. 📌 Partner with IT and security teams Auditors cannot embed continuous auditing alone. Partner with IT operations, cybersecurity, and compliance teams to access data pipelines, logging systems, and APIs. Collaboration ensures that analytics scripts have reliable inputs and that findings feed into remediation processes. 📌 Measure and communicate results The ultimate value of continuous auditing comes from timely insights. Define metrics such as number of exceptions detected, average time to remediation, and percent of population tested. Present these results to leadership in dashboards or concise trend charts. Show how your methods reduce risk faster than traditional audits. The future of IT audit will belong to teams that can harness analytics. Continuous auditing enables broader coverage, faster detection, and more relevant insights. Instead of waiting for year-end reports, executives can see real-time assurance. This positions IT auditors as critical partners in enterprise risk management. #ITAudit #AuditInnovation #ContinuousAuditing #DataAnalytics #CyberVerge #CybersecurityAudit #InternalAudit #RiskManagement #CloudAudit

"Trust me, we ran the security scan" isn't a compliance strategy. 🕵️♂️ NIST 800-53 Control SA-11 (Developer Security Testing) doesn't just ask if you have security testing tools. It asks if you can prove they were run against specific artifacts, with specific results, and that flaws were actually fixed. Traditionally, evidence for this is a mess of mutable CI logs, screenshots, and Jira tickets—none of which hold up to serious scrutiny. Welcome back to the Automating Compliance Controls Series. Today, we’re showing how the TestifySec AI Compliance Platform transforms ephemeral security scans into immutable, cryptographic evidence. 🚫 The Problem: CI Logs Are Not Evidence A log file says a test ran. It doesn't cryptographically prove what code was tested, when it happened, or if the results were altered after the fact. ✅ The Solution: Attestation-Based Evidence We don't just run scans; we witness them. The TestifySec platform wraps your existing security tools (Snyk, Trivy, Pytest, etc.) to create cryptographically signed attestations for every execution. 1. Capture the Evidence (The "Witness" Step) Instead of just running a scan, we wrap it to capture inputs (source code hash), outputs (report hash), and identity. 👉 The Command: witness run --step sast-scan -- snyk test --json This generates a signed document proving exactly what version of the scanner ran on exactly which commit. 2. Map to SA-11 Requirements The platform automatically maps these attestations to specific SA-11 control enhancements: ✅ SA-11(1) Static Analysis: Verified by signed SAST attestation. ✅ SA-11(8) Dynamic Analysis: Verified by signed DAST attestation. ✅ SA-11.d Flaw Remediation: Cryptographic proof that the CVE found in build v1 was absent in build v2. 3. Enforce at Deployment If the required attestations don't exist for an artifact, deployment is automatically blocked. 🎯 The Result You stop handing auditors mutable logs and start handing them immutable proof. You satisfy the base control plus key enhancements without a single spreadsheet. Follow TestifySec for the next installment of the Automating Compliance Controls Series.