Building A Compliance Framework For Tech Startups

When people say they’re “starting #GRC from scratch,” what they really mean is, “I’ve been told we need risk and compliance, but I don’t know where to begin and I don’t want to mess it up.” There’s no shortage of people yelling about frameworks, certifications, dashboards, and expensive tooling. But if you’re at a small company or just got tapped to build out GRC for the first time, you don’t need a framework on day one. You need clarity. Before you worry about #ISO 27001, #NIST 800-53, or #SOX compliance, take a step back and look at what GRC is really trying to do: help the business run securely, legally, and with less risk. Start with what’s risky. Ask: What could go wrong here that would hurt the company? It could be a breach, a data leak, a contract violation, or even a missed customer deadline because something wasn’t documented. Write those things down. That’s the beginning of your risk register. Don’t overthink it. You don’t need a platform or a perfect scoring model. A spreadsheet is just fine. Then ask what’s already being done. Are people locking laptops? Do you have documented onboarding steps? Is someone reviewing access to key systems? That’s your informal control set. It exists, even if no one has called it that. Start by documenting what’s real, not what should be. Figure out what’s required. Do you handle personal data? Are you a vendor for a regulated company? Do your customers expect reports or audits? This tells you whether something like #SOC 2, #GDPR, or #CCPA should be on your radar. You don’t need to implement the whole thing, just know what the expectations are. That helps you prioritize. Make the first thing repeatable. Pick one thing, maybe user access reviews or incident documentation and make it consistent. Write a simple checklist. Note who’s responsible. That’s your first GRC process. Once something is working well, you can grow from there. But starting small, with real value and consistency, builds the trust that you’ll need when it’s time to roll out something bigger. The best GRC programs aren’t flashy. They’re dependable. You don’t need to buy anything to begin. You don’t need a CSF score. You need to start with the work that protects people, helps the business avoid risk, and supports clear decisions. Everything else comes later.

During seed round due diligence, we found a red flag: the startup didn’t have rights to the dataset used to train its LLM and hadn’t set up a privacy policy for data collection or use. AI startups need to establish certain legal and operational frameworks to ensure they have and maintain the rights to the data they collect and use, especially for training their AI models. Here are the key elements for compliance: 1. Privacy Policy: A comprehensive privacy policy that clearly outlines data collection, usage, retention, and sharing practices. 2. Terms of Service/User Agreement: Agreements that users accept which should include clauses about data ownership, licensing, and how the data will be used. 3. Data Collection Consents: Explicit consents from users for the collection and use of their data, often obtained through clear opt-in mechanisms. 4. Data Processing Agreements (DPAs): If using third-party services or processors, DPAs are necessary to define the responsibilities and scope of data usage. 5. Intellectual Property Rights: Ensure that the startup has clear intellectual property rights over the collected data, through licenses, user agreements, or other legal means. 6. Compliance with Regulations: Adherence to relevant data protection regulations such as GDPR, CCPA, or HIPAA, which may dictate specific requirements for data rights and user privacy. 7. Data Anonymization and Security: Implementing data anonymization where necessary and ensuring robust security measures to protect data integrity and confidentiality. 8. Record Keeping: Maintain detailed records of data consents, privacy notices, and data usage to demonstrate compliance with laws and regulations. 9. Data Audits: Regular audits to ensure that data collection and usage align with stated policies and legal obligations. 10. Employee Training and Policies: Training for employees on data protection best practices and establishing internal policies for handling data. By having these elements in place, AI startups can help ensure they have the legal rights to use the data for training their AI models and can mitigate risks associated with data privacy and ownership. #startupfounder #aistartup #dataownership

AI regulation is no longer theoretical. The EU AI Act is a law. And compliance isn’t just a legal concern but it’s an organizational challenge. The new white paper from appliedAI, AI Act Governance: Best Practices for Implementing the EU AI Act, shows how companies can move from policy confusion to execution clarity, even before final standards arrive in 2026. The core idea: Don’t wait. Start building compliance infrastructure now. Three realities are driving urgency: → Final standards (CEN-CENELEC) won’t land until early 2026 → High-risk system requirements go into force by August 2026 → Most enterprises lack cross-functional processes to meet AI Act obligations today Enter the AI Act Governance Pyramid. The appliedAI framework breaks down compliance into three layers: 1. Orchestration: Define policy, align legal and business functions, own regulatory strategy 2. Integration: Embed controls and templates into your MLOps stack 3. Execution: Build AI systems with technical evidence and audit-ready documentation This structure doesn’t just support legal compliance. It gives product, infra, and ML teams a shared language to manage AI risk in production environments. Key insights from the paper: → Maps every major AI Act article to real engineering workflows → Aligns obligations with ISO/IEC standards including 42001, 38507, 24027, and others → Includes implementation examples for data governance, transparency, human oversight, and post-market monitoring → Proposes best practices for general purpose AI models and high-risk applications, even without final guidance This whitepaper is less about policy and more about operations. It’s a blueprint for how to scale responsible AI at the system level across legal, infra, and dev. The deeper shift. Most AI governance efforts today live in docs, not systems. The EU AI Act flips that. You now need: • Templates that live in MLOps pipelines • Quality gates that align with Articles 8–27 • Observability for compliance reporting • Playbooks for fine-tuning or modifying GPAI models The whitepaper makes one thing clear: AI governance is moving from theory to infrastructure. From policy PDFs to CICD pipelines. From legal language to version-controlled enforcement. The companies that win won’t be those with the biggest compliance teams. They’ll be the ones who treat governance as code and deploy it accordingly. #AIAct #AIGovernance #ResponsibleAI #MLops #AICompliance #ISO42001 #AIInfrastructure #EUAIAct