AI compliance demands a fundamentally new mindset. Many organizations fall into one of two limiting perspectives: either viewing compliance primarily through the lens of corporate compliance, focusing on training and audits, or treating it as a purely technical challenge within the domain of cybersecurity. Both approaches, while valuable, ultimately miss the mark. Neither alone is sufficient to ensure AI delivers real benefits in a safe and responsible manner.
When it comes to AI, the stakes are exceptionally high, with both significant risks and opportunities emerging at unprecedented speeds. This environment demands real-time AI governance, supported by programs, systems, and processes that work in harmony.
Traditional approaches to building compliance programs – which often focus on developing individual components in isolation with the hope of future integration – are inadequate. While such approaches might address basic obligations, they fail to create the integrated, responsive systems needed for effective safe and responsible AI.
When it comes to AI, what we need instead are compliance programs that function as a system from day one and capable of evolving over time.
The Lean Startup Approach
This is where the Lean Startup methodology (developed by Eric Ries and adapted by Lean Compliance) proves invaluable, as it aligns naturally with how AI itself is being developed. This approach is what compliance must also follow to reduce friction and keep up with the speed of AI risk.
The core principle is maintaining an operational compliance program with essential capabilities working together (a Minimal Viable Program or MVP) that can be continuously improved through learning and iteration.
Think of it like transportation technology: you might start with a scooter, progress to a bicycle, then to a car, and beyond. At each stage, you have a functional system that delivers the core value proposition of transportation, rather than a collection of disconnected parts that might someday become a vehicle. This approach mirrors how technology itself is developed and represents how compliance must evolve to keep pace with AI advancement.
Applying Lean Startup to AI Compliance in Practice
The Lean Startup approach for AI compliance focuses on three key principles:
Build-Measure-Learn: Create a minimal viable program that can be quickly implemented and tested. Gather data on its performance and effectiveness and use these insights to make informed improvements.
Validated Learning: With AI regulations being actively drafted and enacted globally, organizations can't wait for complete regulatory clarity. Instead, they must implement practical compliance measures and learn from their application in real-world scenarios. This hands-on experience helps organizations understand how to operationalize regulatory requirements effectively, identify potential gaps or challenges, and develop practical solutions before regulations are fully enforced. This learning becomes invaluable input for both improving internal compliance programs and engaging constructively with regulators as they refine their approaches.
Compliance Accounting: Establish clear metrics for measuring the success of your compliance program, focusing on meaningful outcomes rather than just traditional compliance checkboxes.
In practice, this might mean starting with a basic set of AI compliance capabilities, then iteratively advancing monitoring tools, governance structures, and audit capabilities based on real-world experience and feedback. The key is maintaining a functional system at every stage while continuously improving its capabilities and sophistication over time.
This approach ensures that organizations can begin managing AI risks immediately while building toward more capable compliance programs. It's a pragmatic and rapid response to the challenge of governing evolving technology, allowing companies to stay on mission, between the lines, and ahead of risk.
Lean Compliance has adapted the Learn Startup Approach to support implementation of compliance programs across all obligations: safety, security, sustainability, quality, and so on. This approach ensures compliance programs are operational - able to deliver the outcomes of compliance.