Navigating Compliance Issues In Software Development

Compliance of AI Systems — Julius Schöning & Niklas Kruse, Osnabrück University (AI Act & XAI Best Practices) Quotations 📚 “The world doesn’t need more AI—it needs more trustworthy AI.” 📚 “You can’t retrofit compliance after development—it must be embedded from step one.” 📚 “An AI system trained on biased or illegal data is untrustworthy by default.” 📚 “The more decentralized the AI system, the harder it becomes to verify compliance at the edge.” 📚 “Simulation and regulation must evolve in sync—one without the other leads to failure or delay.” Key Points 📚 AI Compliance is Lifecycle-Embedded: From data collection to deployment, compliance needs to be integrated across all six AI pipeline steps. 📚 XAI Is a Trust Engine: Explainability methods (ex-ante, ex-nunc, ex-post) enable accountability and legal defensibility—especially under the EU AI Act. 📚 Data Is a Legal Liability: Datasets must be verified for fairness, bias, and legality—flawed data can render entire systems noncompliant. 📚 Edge AI Is a Special Risk Zone: Low compute + local data = high exposure to attacks and audit complexity. 📚 Expert Systems > LLMs for Legal Checklists: Traditional logic-based systems are better suited to track fast-changing laws and avoid AI Act high-risk status. Headlines 📚 “Compliance by Design: Why Trustworthy AI Starts Before the First Line of Code” 📚 “Data Is the Achilles’ Heel of AI—Train on It, Own the Risk” Action Items 📚 Build a Compliance-by-Design Playbook: Start with the six AI pipeline stages—identify legal touchpoints early (data, training, deployment). 📚 Implement Automated Dataset Audits: Use tools to scan for bias, copyright risks, and legal violations pre-training. 📚 Adopt Explainability Standards: Formalize XAI practices across teams—clarify how decisions are made and visualized at each step. 📚 Deploy Legal Expert Systems: Integrate non-autonomous expert logic systems (not LLMs) for real-time regulatory alignment. 📚 Prepare for High-Risk Use Case Reviews: Assess whether your AI applications fall under AI Act Article 6 or Annex III triggers (e.g., employee task allocation). Risks 📚 Post-Hoc Legal Reviews: Discovering legal flaws after training may require complete redevelopment—especially for high-risk applications. 📚 LLM Legal Tools Under Reg Watch: Generative legal advisors may themselves become regulated as “high-risk” under the AI Act. 📚 Noncompliant Dataset Structure: Data tied to biometric rights or discriminatory outcomes may violate Art. 10 of the AI Act. 📚 Blind Deployment on Edge Devices: Without a clear Operational Design Domain (ODD), edge AI systems face reliability and legal ambiguity. 📚 One-Size-Fits-All Explainability: Stakeholders (regulators vs. users) need tailored proofs of trustworthiness—tech alone can’t solve it. #AICompliance #XAI #EUAIAct #TrustworthyAI #RegulatoryStrategy #AIProductLeadership #ResponsibleAI #EdgeAI #DataGovernance

✳ Integrating AI, Privacy, and Information Security Governance ✳ Your approach to implementation should: 1. Define Your Strategic Context Begin by mapping out the internal and external factors impacting AI ethics, security, and privacy. Identify key regulations, stakeholder concerns, and organizational risks (ISO42001, Clause 4; ISO27001, Clause 4; ISO27701, Clause 5.2.1). Your goal should be to create unified objectives that address AI’s ethical impacts while maintaining data protection and privacy. 2. Establish a Multi-Faceted Policy Structure Policies need to reflect ethical AI use, secure data handling, and privacy safeguards. Ensure that policies clarify responsibilities for AI ethics, data security, and privacy management (ISO42001, Clause 5.2; ISO27001, Clause 5.2; ISO27701, Clause 5.3.2). Your top management must lead this effort, setting a clear tone that prioritizes both compliance and integrity across all systems (ISO42001, Clause 5.1; ISO27001, Clause 5.1; ISO27701, Clause 5.3.1). 3. Create an Integrated Risk Assessment Process Risk assessments should cover AI-specific threats (e.g., bias), security vulnerabilities (e.g., breaches), and privacy risks (e.g., PII exposure) simultaneously (ISO42001, Clause 6.1.2; ISO27001, Clause 6.1; ISO27701, Clause 5.4.1.2). By addressing these risks together, you can ensure a more comprehensive risk management plan that aligns with organizational priorities. 4. Develop Unified Controls and Documentation Documentation and controls must cover AI lifecycle management, data security, and privacy protection. Procedures must address ethical concerns and compliance requirements (ISO42001, Clause 7.5; ISO27001, Clause 7.5; ISO27701, Clause 5.5.5). Ensure that controls overlap, such as limiting access to AI systems to authorized users only, ensuring both security and ethical transparency (ISO27001, Annex A.9; ISO42001, Clause 8.1; ISO27701, Clause 5.6.3). 5. Coordinate Integrated Audits and Reviews Plan audits that evaluate compliance with AI ethics, data protection, and privacy principles together (ISO42001, Clause 9.2; ISO27001, Clause 9.2; ISO27701, Clause 5.7.2). During management reviews, analyze the performance of all integrated systems and identify improvements (ISO42001, Clause 9.3; ISO27001, Clause 9.3; ISO27701, Clause 5.7.3). 6. Leverage Technology to Support Integration Use GRC tools to manage risks across AI, information security, and privacy. Integrate AI for anomaly detection, breach prevention, and privacy safeguards (ISO42001, Clause 8.1; ISO27001, Annex A.14; ISO27701, Clause 5.6). 7. Foster an Organizational Culture of Ethics, Security, and Privacy Training programs must address ethical AI use, secure data handling, and privacy rights simultaneously (ISO42001, Clause 7.3; ISO27001, Clause 7.2; ISO27701, Clause 5.5.3). Encourage a mindset where employees actively integrate ethics, security, and privacy into their roles (ISO27701, Clause 5.5.4).

If I had the ability to create a new buzzword for the DoD it would be path-to-production! 😏 Why? Because you can't ship software continuously otherwise. "Path to production refers to the journey or process that a project or product undergoes from its initial development stages to its final deployment or release for actual use by end users. It encompasses all the steps and milestones involved in bringing a concept or idea to a functional and operational state." This is standard in the technology industry but not standard across govtech. This includes all your underlying infrastructure, PaaS, and pipeline--and not just a typical pipeline---a secure release pipeline. And specifically within the federal government, especially the DoD, you need to meet all security and compliance requirements (addressing NIST RMF) before you can get working software in front of your end users. By that definition--very few organizations have a true path-to-production that has achieved Ongoing Authorization. Apply RMF in parallel with DevOps throughout the entire software development lifecycle, continuously! It's possible. We've done it. I've seen it. I was a product manager that had to prioritize security stories in my backlog just as much as feature work. We had tooling and automation built in our pipeline to scan and check against vulnerabilities and compliance. I led an entire software portfolio of teams and had to set expectations both internally and externally that security and compliance is just as important as capability development. Yes, it's important to get software to your end users quickly. It's also important to make sure you do so in a secure and sustainable way. But you can do it in parallel, continuously! You don't have to tradeoff one for the other. So if you can't figure out how the hell to ship software in the government, ask yourself--do you have a valid path to production with Ongoing Authorization? You can learn more all about this here! https://playbook.rise8.us/ #continuousdelivery #pathtoproduction #softwaredevelopment #cicd

DevSecOps promised us automation, speed, and security woven directly into development, but we’re starting to see a clear limit, especially in terms of visibility and traceability. These properties are critical to automating compliance controls on developer actions. With "Shift Left" and DevSecOps, developers now have the ability to change production with every commit—potentially hundreds of times a day. Meanwhile, compliance teams can, at best, manage monthly reporting in most organizations. This disconnect creates a real gap. Compliance simply can’t keep up with the speed of development, which means reporting and accountability are consistently out of sync. Real-time or near-real-time visibility into developer actions is essential if compliance is ever going to keep pace. To get there, compliance needs to be directly integrated with CI/CD pipelines, tracing every action back to policy controls, so that each change is recorded and assessed in real-time. Only when compliance can achieve the same velocity as DevSecOps will we truly be able to hold the line on risk and security in these agile, high-frequency release environments.