Scaling AI While Maintaining Compliance

𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐢𝐬𝐧’𝐭 𝐚 𝐠𝐚𝐭𝐞 𝐢𝐭’𝐬 𝐭𝐡𝐞 𝐩𝐫𝐨𝐝𝐮𝐜𝐭 𝐭𝐡𝐚𝐭 𝐦𝐚𝐤𝐞𝐬 𝐀𝐈 𝐬𝐜𝐚𝐥𝐞. Most teams bolt governance on. Then wonder why scaling stalls. The shift: 𝐃𝐞𝐬𝐢𝐠𝐧 𝐠𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐚𝐬 𝐟𝐞𝐚𝐭𝐮𝐫𝐞𝐬 𝐜𝐮𝐬𝐭𝐨𝐦𝐞𝐫𝐬 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞. → Policies as Code Hard-code data boundaries, approvals, and retention. No PDFs on SharePoint. → Evaluation Harnesses Test safety, bias, drift, and instruction-following before release continuously. → Observability Trace every decision: inputs, tools, and model versions audits in hours, not weeks. Change Management Bake in gates, rollout plans, and feature flags. 𝐂𝐚𝐬𝐞 𝐢𝐧 𝐩𝐨𝐢𝐧𝐭: A bank deployed onboarding agents under regulatory scrutiny. ↳ Policies-as-code enforced KYC + disclosures automatically. ↳ Eval harness caught risky prompts pre-production. ↳ Deployment time dropped 60%. ↳ Incidents trended toward zero. Result? Governance wasn’t friction it became the feature buyers trusted most. Ready to turn governance from blocker into competitive advantage? ♻️ Repost to your LinkedIn empower your network & follow Timothy Goebel for expert insights #GenerativeAI #EnterpriseAI #AIProductManagement #LLMAgents #ResponsibleAI

In 2025, deploying GenAI without architecture is like shipping code without CI/CD pipelines. Most companies rush to build AI solutions and create chaos. They deploy bots, copilots, and experiments with no tracking. No controls. No standards. Smart teams build GenAI like infrastructure. They follow a proven four-layer architecture that McKinsey recommends with enterprise clients. Layer 1: Control Portal Track every AI solution from proof of concept to production. Know who owns what. Monitor lifecycle stages. Stop shadow AI before it creates compliance nightmares. Layer 2: Solution Automation Build CI/CD pipelines for AI deployments. Add stage gates for ethics reviews, cost controls, and performance benchmarks. Automate testing before solutions reach users. Layer 3: Shared AI Services Create reusable prompt libraries. Build feedback loops that improve model performance. Maintain LLM audit trails. Deploy hallucination detection that actually works. Layer 4: Governance Framework Skip the policy documents. Build real controls for security, privacy, and cost management. Automate compliance checks. Make governance invisible to developers but bulletproof for auditors. This architecture connects to your existing systems. It works with OpenAI and your internal models. It plugs into Salesforce, Workday and both structured and unstructured data sources. The result? AI that scales without breaking. Solutions that pass compliance reviews. Costs that stay predictable as you grow. Which layer is your biggest gap right now: control, automation, services, or governance?

In my work with organizations rolling out AI and generative AI solutions, one concern I hear repeatedly from leaders, and the c-suite is how to get a clear, centralized “AI Risk Center” to track AI safety, large language model's accuracy, citation, attribution, performance and compliance etc. Operational leaders want automated governance reports—model cards, impact assessments, dashboards—so they can maintain trust with boards, customers, and regulators. Business stakeholders also need an operational risk view: one place to see AI risk and value across all units, so they know where to prioritize governance. One of such framework is MITRE’s ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) Matrix. This framework extends MITRE ATT&CK principles to AI, Generative AI, and machine learning, giving us a structured way to identify, monitor, and mitigate threats specific to large language models. ATLAS addresses a range of vulnerabilities—prompt injection, data leakage, malicious code generation, and more—by mapping them to proven defensive techniques. It’s part of the broader AI safety ecosystem we rely on for robust risk management. On a practical level, I recommend pairing the ATLAS approach with comprehensive guardrails - such as: • AI Firewall & LLM Scanner to block jailbreak attempts, moderate content, and detect data leaks (optionally integrating with security posture management systems). • RAG Security for retrieval-augmented generation, ensuring knowledge bases are isolated and validated before LLM interaction. • Advanced Detection Methods—Statistical Outlier Detection, Consistency Checks, and Entity Verification—to catch data poisoning attacks early. • Align Scores to grade hallucinations and keep the model within acceptable bounds. • Agent Framework Hardening so that AI agents operate within clearly defined permissions. Given the rapid arrival of AI-focused legislation—like the EU AI Act, now defunct  Executive Order 14110 of October 30, 2023 (Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence) AI Act, and global standards (e.g., ISO/IEC 42001)—we face a “policy soup” that demands transparent, auditable processes. My biggest takeaway from the 2024 Credo AI Summit was that responsible AI governance isn’t just about technical controls: it’s about aligning with rapidly evolving global regulations and industry best practices to demonstrate “what good looks like.” Call to Action: For leaders implementing AI and generative AI solutions, start by mapping your AI workflows against MITRE’s ATLAS Matrix. Mapping the progression of the attack kill chain from left to right - combine that insight with strong guardrails, real-time scanning, and automated reporting to stay ahead of attacks, comply with emerging standards, and build trust across your organization. It’s a practical, proven way to secure your entire GenAI ecosystem—and a critical investment for any enterprise embracing AI.

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?

Govern to Grow: Scaling AI the Right Way    Speed or safety? In the financial sector’s AI journey, that’s a false choice. I’ve seen this trade-off surface time and again with clients over the past few years. The truth is simple: you need both.   Here is one business Use Case & a Success Story. Imagine a loan lending team eager to harness AI agents to speed up loan approvals. Their goal? Eliminate delays caused by the manual review of bank statements. But there’s another side to the story. The risk and compliance teams are understandably cautious. With tightening Model Risk Management (MRM) guidelines and growing regulatory scrutiny around AI, commercial banks are facing a critical challenge: How can we accelerate innovation without compromising control?   Here’s how we have partnered with Dataiku to help our clients answer this very question!   The lending team used modular AI agents built with Dataiku’s Agent tools to design a fast, consistent verification process: 1. Ingestion Agents securely downloaded statements 2. Preprocessing Agents extracted key variables 3. Normalization Agents standardized data for analysis 4. Verification Agent made eligibility decisions and triggered downstream actions   The results? - Loan decisions in under 24 hours - <30 min for statement verification - 95%+ data accuracy - 5x more applications processed daily   The real breakthrough came when the compliance team leveraged our solution powered by Dataiku’s Govern Node to achieve full-spectrum governance validation. The framework aligned seamlessly with five key risk domains: strategic, operational, compliance, reputational, and financial, ensuring robust oversight without slowing innovation.   What stood out was the structure: 1. Executive Summary of model purpose, stakeholders, deployment status 2. Technical Screen showing usage restrictions, dependencies, and data lineage 3. Governance Dashboard tracking validation dates, issue logs, monitoring frequency, and action plans   What used to feel like a tug-of-war between innovation and oversight became a shared system that supported both. Not just finance, across sectors, we’re seeing this shift: governance is no longer a roadblock to innovation, it’s an enabler. Would love to hear your experiences. Florian Douetteau Elizabeth (Taye) Mohler (she/her) Will Nowak Brian Power Jonny Orton