Article 3: Overcoming Challenges in Implementing AI Risk Controls

Executive Summary

Identifying AI risks is only the beginning – implementing effective risk management is the real challenge. This article addresses practical hurdles in deploying AI risk controls, especially for generative AI systems. Key obstacles include measuring AI risks, adapting to rapid AI evolution, integrating risk processes into agile development, and overcoming organizational resistance. Drawing from NIST's framework and industry experiences, I explore these challenges and strategies to overcome them. How can teams handle the lack of clear metrics for AI behavior? What about addressing "unknown unknowns" in generative AI? How do you secure buy-in for risk mitigation? I highlight examples from organizations that have tackled these issues and provide actionable steps to move AI risk controls from theory to effective practice.

Introduction: From Framework to Front Line

NIST's AI Risk Management Framework (AI RMF 1.0) offers a comprehensive blueprint, but adoption isn't instantaneous. Implementing ideals like "safe, effective, and fair" AI requires integrating them into workflows across teams. Generative AI adds complexity with its rapid evolution and unpredictable outputs. Meanwhile, regulatory expectations grow, and customer wariness increases. While 93% of organizations recognize generative AI risks, only 9% feel prepared – a gap we must close.

This article examines the hurdles between good intentions and effective practice: measurement challenges, alignment with enterprise risk structures, resource constraints, technology change, and internal resistance.

For each obstacle, I'll discuss solutions being tested, drawing from NIST's ARIA program and industry case studies. Whether you're an AI lawyer, compliance officer, product leader, or auditor, understanding these challenges will help distinguish between superficial compliance and robust risk management.

Hurdle 1: The Measurement Dilemma

Unlike traditional risk areas with established metrics, AI risk is harder to quantify. How do you measure "hallucination risk" or bias in a language model? These issues are context-dependent, making single metrics insufficient.

NIST's ARIA program is developing new evaluation methods to quantify trustworthy AI behavior. Meanwhile, teams can adopt metrics from research:

• Hallucination/Error Rate: Measure incorrect responses against ground truth
• Toxicity Scores: Track harmful content in outputs across test prompts
• Bias Metrics: Compare outputs when varying demographic identifiers
• Robustness Metrics: Evaluate consistency across similar inputs

Practical approaches include:

• Creating risk thresholds (e.g., "If hallucination rate > X%, risk is High")
• Using incident tracking as a reactive metric
• Starting with imperfect but useful measurements for priority risks

The key is avoiding "we can't measure perfectly" as an excuse for not measuring at all.

Hurdle 2: Rapid Evolution and "Risk Drift"

AI systems evolve quickly, causing "risk drift" – risks morph or new ones emerge as capabilities change. Adding tool use to a text model, for instance, introduces entirely new risks beyond existing controls.

Solutions include:

• Formalizing AI model change management processes
• Scheduling quarterly risk reviews or assessments before major releases
• Monitoring industry developments for newly discovered vulnerabilities
• Employing "Red Team as a Service" for periodic external testing
• Documenting changes to trace potential issues

Treat AI risk management as a living process by integrating re-evaluation into changes, staying informed on research, using external audits, and maintaining documentation.

Hurdle 3: Integration into Agile Development

Fitting risk management into rapid development cycles without causing delays is challenging. Risk management can be perceived as bureaucratic and incompatible with agile methods.

Effective strategies include:

• Embedding risk checks into existing workflows (stories, done criteria, CI tests)
• Providing lightweight tools and templates for quick assessment
• Automating risk tests in development pipelines
• Using incremental rollouts to limit risk exposure
• Assigning clear responsibility within teams

Some companies designate "AI risk champions" within teams, similar to security champions in AppSec, ensuring risk considerations aren't overlooked.

Hurdle 4: Resource and Expertise Constraints

Not all organizations have AI ethicists or model risk specialists. Teams may identify risks but lack expertise to implement appropriate mitigations.

Approaches to address expertise gaps:

• Leveraging external guidelines and open-source resources
• Investing in upskilling current staff on AI risk topics
• Prioritizing highest-risk areas when resources are limited
• Engaging consultants for critical reviews
• Sharing lessons learned within the community

The NIST framework is designed to be scalable, allowing smaller organizations to adopt core principles in a lighter fashion. Start with what you have – some risk management is better than none.

Hurdle 5: Cultural and Organizational Resistance

Human factors can undermine risk management efforts. Skepticism ("Is this necessary?"), fear ("Will it slow us down?"), and confusion ("Who's responsible?") can prevent progress.

Strategies for cultural alignment:

• Securing leadership buy-in through policy and prioritization
• Framing risk management as an enabler of sustainable innovation
• Encouraging transparent reporting of issues through blameless post-mortems
• Clarifying roles through governance committees
• Balancing risk control with innovation

NIST's framework is risk-based, not zero-risk, allowing for calculated risks when justified.

Insights on Overcoming Hurdles

From NIST workshops and industry experience:

• Adopting common definitions reduces internal debates
• Integrating risk framework with existing quality processes aids adoption
• Multi-tier testing catches issues standard development misses
• Starting with partial measures and maturing over time is acceptable
• Normalized risk processes eventually become expected parts of development

Actionable Steps: Define, Detect, Analyze, Decide, Act

1. Define: Establish processes, roles, and success metrics; clarify risk tolerance
2. Detect: Monitor process effectiveness and emerging risks through audits and metrics
3. Analyze: Examine both failures and successes to understand what works
4. Decide: Adjust approach based on analysis, making explicit trade-offs
5. Act: Implement decisions and communicate changes

Consider creating an "AI Risk Task Force" for quarterly program reviews using this method.

Conclusion

Implementing AI risk controls is challenging but achievable. By recognizing common hurdles, we can address them proactively. As more organizations implement risk management, tools will improve, benchmarks will emerge, and expertise will grow.

Frame these efforts as investments in resilience and trust – like building good brakes on a fast car, enabling safer innovation. Companies that effectively manage AI risks will innovate with confidence.

Leverage the community and frameworks like NIST ARIA. Every hurdle cleared advances responsible AI deployment. With persistence and evolving standards, we can ensure AI risk controls truly operate on the front lines of innovation.

DISCLAIMER: The content is for educational purposes and is not or intended to be legal advice or to establish an attorney-client relationship. No content represents advice provided to past or current clients.

Article 3 will explore what Industry and NIST workshops revealed about AI Risk Management.