Improving Due Diligence Using Cloud Audit Tools

Dear IT Auditors, ITGC in Cloud-Native Teams Many organizations have embraced cloud platforms like AWS and Azure, but very few know how to audit IT General Controls (ITGCs) in a cloud-native environment. Traditional ITGC testing relied on on-premises systems, familiar roles, and predictable evidence. Cloud-native teams change the rules. When developers can spin up resources in minutes and infrastructure is managed as code, how do you validate that controls exist and work without slowing the business down? That’s where modern IT audit practices come in. 📌 Access Management: Instead of static AD groups, cloud environments use identity and access management (IAM) policies. You need to review policies, roles, and entitlements at scale. Focus on least privilege, segregation of duties, and rotation of credentials. 📌 Change Management: Cloud-native teams use pipelines like GitHub Actions, GitLab CI, or Azure DevOps. Your role is to confirm that code changes to infrastructure or applications follow peer review, approval, and automated testing. Ask: Can the organization trace who made changes and when? 📌 Operations Controls: Logs, alerts, and monitoring are built into cloud platforms. The test isn’t whether logs exist—it’s whether logs are retained, reviewed, and tied to incident response. Look at CloudTrail in AWS or Activity Logs in Azure and test for completeness and retention. 📌 Evidence Collection: Screenshots aren’t enough. Cloud platforms produce system-generated evidence like JSON files, configuration exports, and automated compliance scans. As an auditor, you should guide teams to provide structured evidence that regulators and executives trust. 📌 Collaboration with DevOps: The biggest shift is cultural. IT auditors can’t audit cloud-native teams with a checklist designed for 2005. You need to understand the language of developers, containers, and automation, then translate it into assurance terms. Collaboration builds trust, and trust drives better controls. Cloud adoption is accelerating. The question for auditors is simple: are you testing ITGCs the old way, or are you building assurance into the way cloud teams actually work? #ITAudit #CloudAudit #ITGC #AWS #Azure #DevOps #Assurance #RiskManagement #CyberSecurityAudit #GRC #InternalAudit

I finally understand why AI due diligence and audit tools keep failing, and Ali Buhaji (Partner at KPMG) and I just fixed it in an evening. The problem isn't the AI. It's that everyone's using ONE model like it's an oracle, trying to replace human judgment. So we ran an experiment: three models, small business acquisition (restaurant), same 62 documents, same 42-point checklist, zero communication between them. Then we watched them fight. Here's what broke my brain: they disagree on EXACTLY the things experienced deal lawyers disagree on. - Is a chef with no non-compete a dealbreaker or just standard restaurant practice? - Is 45% supplier concentration a red flag or a long-term relationship? - Does a lease with no renewal option kill your valuation or just reshape your timeline? That disagreement IS the signal. That's the thing nobody's building toward. So we turned the conflict into a routing system: 1. All three agree → CLEAR. Junior verifies. Move faster. 2. Models split → REVIEW. Senior associate digs in. This is your money zone. 3. All three screaming → ESCALATE. Partner time. No shortcuts. The logic: unanimous AI confidence means low-variance risk. You don't need expensive human hours there. But contested flags? That's where judgment lives, and that's exactly where you want your senior people focused. We didn't pitch this. We built it. Open source. Swap your own risk matrix, your own models, your own data room. 🔗 Live demo: https://lnkd.in/eBedqpPE 📦 Source: https://lnkd.in/exuFZssX Curious… when you're doing preliminary deal review right now, where does the human escalation decision actually get made? Is it documented or just vibes? #duediligence #legaltech #openSource

This is the disconnect everyone in GRC deals with.  Controls operate continuously inside modern systems. Verification does not.   Verification waits for the audit calendar. That latency isn’t a tooling issue as much as an architectural gap in how evidence is produced. And this gives us a backward-looking administrative burden rather than a forward-looking risk management practice. Enterprises already have telemetry that describes control behavior. Cloud APIs, SaaS platforms, identity providers, logs, and CMDB sources produce signals about access, configuration, deployment, and change. The data exists in real time. But compliance workflows consumes it in long intervals. Raw system data isn’t useful to auditors. It must be translated into evidence for a specific control and a specific framework. That translation layer is missing in most environments. When it’s missing, compliance becomes a reconstruction exercise. This translation cannot be a 'black box.' Security logic must be transparent, allowing teams to codify custom rules for both cloud-native and legacy on-prem systems. But once that translation is codified, evidence can be generated continuously. Exceptions surface earlier, while the context for remediation still exists. Audit preparation becomes review instead of archaeology. GRC teams spend less time collecting artifacts and more time interpreting control performance. It becomes possible to answer board questions with current evidence instead of stale snapshots. This translation layer shouldn't be another silo. We have enough of those already. It needs to be an extensible engine that enriches the existing system of record – the GRC platforms enterprises have already invested in – turning these static GRC platforms into dynamic command centers.  .  True assurance must also bridge the human gap. That is, turn episodic manual checks into collaborative workflows that capture evidence in the flow of work. This ensures that even non-technical controls maintain the same tempo as the system's telemetry. The outcome is compliance aligned to the tempo of systems.  Assurance becomes a standing condition rather than an episodic event. The business gains continuous confidence from how operations already function, instead of layering assurance on after the fact. 

Thirty percent of code commits rejected before hardware exists is not a quality metric. It is a governance architecture. Volvo Cars is running cloud-based electronics digital twins that validate every code change against the full vehicle before a single prototype is built. Roughly 30 percent of those commits fail early virtual testing. Integration conflicts, timing issues, system-level failures that would have surfaced months later in hardware validation are caught in hours. Most people see that number as a development efficiency gain. The deeper implication is regulatory. ISO 26262 requires bidirectional traceability from requirements to validation evidence. Traditional hardware-dependent validation generates that evidence late, often in the final months of development. Reconstructing the traceability chain after the fact is expensive, error-prone, and increasingly insufficient for what regulators now expect. UNECE R155 and R156 have shifted homologation from test reports to audit evidence. Regulators are no longer asking whether the vehicle passed a lab test. They are asking how the manufacturer governs cybersecurity and software change across the vehicle's entire lifecycle. The question is not what the vehicle does. It is whether you can demonstrate continuous governance over what it does. Cloud-based virtual validation changes the evidence model entirely. Every rejected commit becomes auditable documentation. Every integration failure caught before hardware becomes traceable proof that the governance system is working. The validation process itself generates the compliance evidence that regulators increasingly demand. Governance is no longer a documentation exercise performed after development. It becomes embedded in the development process itself. The infrastructure behind Volvo's approach includes Synopsys virtual ECUs, AWS cloud infrastructure, QNX virtualized operating systems, and RemotiveLabs physical system simulations. That ecosystem produces validation evidence continuously, with every commit linked to test results, every failure logged and traceable. The companies treating virtual validation as a speed optimization are missing the structural shift. This is a governance architecture that produces compliance evidence as a byproduct of development. The 30 percent rejection rate means 30 percent of potential compliance gaps are identified and documented before they become audit findings. Is your validation process generating the audit trail your regulators will ask for next year? ⸻ 👉 Enjoy insights like this? ✅ Follow me here on LinkedIn https://lnkd.in/gP6p5xxT 🔗 Subscribe for more weekly deep dives https://lnkd.in/gs7RVVvn All opinions are my own and do not reflect those of my employer. ⸻ #AutomotiveCloudWatch #SoftwareDefinedVehicle #DigitalTwin

🔍 𝐀𝐮𝐝𝐢𝐭-𝐑𝐞𝐚𝐝𝐲 𝐂𝐥𝐨𝐮𝐝: 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐂𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐭 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞𝐬 𝐟𝐫𝐨𝐦 𝐃𝐚𝐲 𝐎𝐧𝐞 As cloud environments grow more complex, the gap between innovation and compliance widens. Here's why building audit-ready cloud architectures should be your top priority: 🏗️ 𝐊𝐞𝐲 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 𝐏𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞𝐬: - Infrastructure as Code (IaC) with built-in compliance checks - Automated audit trails across all cloud resources - Real-time compliance monitoring and drift detection - Standardized tagging strategy for resource tracking - Least-privilege access by default 💡 𝐏𝐫𝐨 𝐓𝐢𝐩𝐬 𝐟𝐫𝐨𝐦 𝐭𝐡𝐞 𝐓𝐫𝐞𝐧𝐜𝐡𝐞𝐬: 1. Version control your compliance policies like code 2. Implement automated remediation for common violations 3. Use cloud-native audit tools (AWS Config, Azure Policy, GCP Security Command) 4. Document everything - your future self will thank you 🛠️ E𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥 𝐓𝐨𝐨𝐥𝐬 𝐢𝐧 𝐘𝐨𝐮𝐫 𝐀𝐫𝐬𝐞𝐧𝐚𝐥: - Terraform/CloudFormation for IaC - Open Policy Agent (OPA) for policy enforcement - Cloud-native CSPM solutions - Git-based audit history - Automated compliance testing in CI/CD 🎯 𝐑𝐞𝐬𝐮𝐥𝐭𝐬 𝐖𝐞'𝐫𝐞 𝐒𝐞𝐞𝐢𝐧𝐠: - 75% reduction in audit preparation time - Near real-time compliance reporting - Significantly fewer audit findings - Faster security clearance for new deployments 𝐑𝐞𝐦𝐞𝐦𝐛𝐞𝐫: Compliance isn't a checkbox; it's an architectural requirement. Build it in from the start, automate everything possible, and make it part of your engineering culture. 🎯 𝐈𝐬 𝐘𝐨𝐮𝐫 𝐂𝐥𝐨𝐮𝐝 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 𝐀𝐮𝐝𝐢𝐭-𝐑𝐞𝐚𝐝𝐲? Tired of last-minute audit scrambles? Our clients were too. We helped them achieve: ✅ 70% faster audit preparations ✅ Zero critical compliance findings ✅ Automated compliance monitoring ✅ Real-time violation alerts Don't wait for auditors to find gaps in your cloud infrastructure. https://lnkd.in/e2mWD_3e

This is how compliance in most organizations works: someone manually checks a list of controls, fills out a doc and submits it as a piece of evidence. This isn’t sustainable in the cloud; things change daily and evidence docs are already outdated by the time anyone reads it. I figured out a better way to approach this and built an AI-powered GRC tool that checks whether your AWS environment actually meets the security controls required by SOC 2, ISO 27001, PCI DSS and GDPR. Not in theory but live in your account, providing visibility into controls that are effectively implemented, allowing you to prioritize time on areas that require improvement. How it works: Connect your AWS account credentials. The scanner checks technical controls across your environment and classifies each one into four states; Enforced (fully configured and working), Running (partially in place), Stopped (was active, now disabled), Not Enforced (does not exist). Every result maps directly to the framework controls your auditors will ask about. SOC 2 CC references, ISO 27001 Annex A controls, PCI DSS requirements & GDPR articles. GRC engineers can no longer afford to be framework interpreters but builders, deploying AI agents and automated systems that help GRC workflows. Like Ethan Troy said "Selling a Trust Center and Dashboard will quickly become obsolete. Good luck GRC Tools." Your credentials are used only during the scan and never stored. Built with React, Nodejs and the AWS SDK. Check it out here: https://lnkd.in/eG5Cb5n5 If you are a GRC professional, cloud security engineer, or anyone who has ever sat in an audit and wished you had better evidence, this was built for you. Share and repost to help a growing team move faster.