Tips for Balancing Risk and Opportunity in Safety Practices

AI poses serious risks when used the wrong way. Our present situation with the emergence of AI reminds me of the early years of my engineering career. Graphing calculators and engineering software were introduced and some thought it was the beginning of the end of quality engineering. In reality, these tools have been a net positive, but only once we put them in capable hands and in a proper workflow. Fast forward 20 years and AI is here in safety, and its here to stay. But, how do we use it well and avoid the traps? I see four potential scenarios: - Effective and Efficient: A knowledgeable person who knows how to use AI to accelerate, enhance, and review their work. - Effective but Inefficient: A knowledgeable and skilled person who does not use AI. - Ineffective and Inefficient: An ignorant or unskilled person who doesn’t use AI. - Dangerous: An ignorant or unskilled person using AI to rapidly produce bad output The risk of the “dangerous” category is very real. That’s why our team is equally focused on two things: (1) enhancing the fidelity of the AI and (2) ensuring the AI is used effectively. --- Here is an example of a good and bad use of ChatSafetyAI: ✅ DO: Use ChatSafetyAI to check your high-energy control assessments (HECA) to see if you missed anything. ❌ DONT: Use ChatSafetyAI to do your HECA for you. Proper workflow: Integrate the ChatSafetyAI API after an initial assessment to provide feedback and recommendations. This additive function helps the assessors to “fill in the gaps” with more intelligence. This workflow leverages both human and artificial intelligence, assuming effort is placed in the initial assessment. Our council, comprised of the licensees of ChatSafetyAI, is working on this. Consider joining us. I would love to hear your ideas on the effective use of AI for safety. 

A New Path for Agile AI Governance To avoid the rigid pitfalls of past IT Enterprise Architecture governance, AI governance must be built for speed and business alignment. These principles create a framework that enables, rather than hinders, transformation: 1. Federated & Flexible Model: Replace central bottlenecks with a federated model. A small central team defines high-level principles, while business units handle implementation. This empowers teams closest to the data, ensuring both agility and accountability. 2. Embedded Governance: Integrate controls directly into the AI development lifecycle. This "governance-by-design" approach uses automated tools and clear guidelines for ethics and bias from the project's start, shifting from a final roadblock to a continuous process. 3. Risk-Based & Adaptive Approach: Tailor governance to the application's risk level. High-risk AI systems receive rigorous review, while low-risk applications are streamlined. This framework must be adaptive, evolving with new AI technologies and regulations. 4. Proactive Security Guardrails: Go beyond traditional security by implementing specific guardrails for unique AI vulnerabilities like model poisoning, data extraction attacks, and adversarial inputs. This involves securing the entire AI/ML pipeline—from data ingestion and training environments to deployment and continuous monitoring for anomalous behavior. 5. Collaborative Culture: Break down silos with cross-functional teams from legal, data science, engineering, and business units. AI ethics boards and continuous education foster shared ownership and responsible practices. 6. Focus on Business Value: Measure success by business outcomes, not just technical compliance. Demonstrating how good governance improves revenue, efficiency, and customer satisfaction is crucial for securing executive support. The Way Forward: Balancing Control & Innovation Effective AI governance balances robust control with rapid innovation. By learning from the past, enterprises can design a resilient framework with the right guardrails, empowering teams to harness AI's full potential and keep pace with business. How does your Enterprise handle AI governance?

AI policy is no longer optional. Rolling out blind doesn't work. Structure beats chaos every time. Old strategy: deploy first, ask questions later. New strategy: policy-first, risk-aware rollout. That's how safe AI succeeds today: 1 - Executive buy-in drives everything forward. Present ROI cases and competitor wins. Funding flows when leadership believes. 2 - Cross-functional teams own the outcome. Diverse expertise prevents blind spots. Regular alignment keeps progress moving. 3 - Governance frameworks guide every decision. GDPR, HIPAA, EU AI Act compliance built in. Standards prevent costly mistakes later. 4 - Risk assessments catch bias early. Audit fairness before deployment. Dashboards track what matters most. AI policy levels the playing field for every organization. It brings trust, compliance, and innovation together. AI isn't risky. Ungoverned AI is.