EditorialThe AI Revolution, Part 6: Top AI Risk Management Strategies for Enterprise Leaders
As AI continues to evolve, the spotlight needs to shift from capabilities to consequences. This final article in our AI Revolution series explores the critical risks surrounding generative AI and agentic AI — including hallucinations, security vulnerabilities, privacy breaches and broader governance concerns.
Unlike analytical AI, which matured through years of structured data modeling and predictable machine learning pipelines, today’s GenAI and agentic AI face entirely new classes of business risks. These risks aren’t just technical — they directly impact adoption, trust and operational success.
What’s clear from our research is that agentic AI inherits all the vulnerabilities of the GenAI models it builds upon — which often gets magnified by increased autonomy and complexity. Although these technologies are capable of incredible things, they also can introduce opaque decision-making, provide misinformation and reduce human oversight. As organizations rush to deploy AI-driven agents, overlooking these risks isn't just naive — it’s reckless. Addressing these challenges head-on is the only path to sustainable, responsible AI deployment.
Before proceeding, I would like to thank Brian Lett and Michael Moran for their thoughtful review and insightful comments in this article.
Editor's Note: Want to learn more? Check out Part 1, Part 2, Part 3, Part 4 and Part 5 of our AI Revolution series.
Top AI Risks Organizations Face in 2025
Diagram provided by Dresner Advisory Services
In our research, one concern that stands far above the rest is data security and privacy. A majority of survey participants — 55% — identified this as a critical risk associated with generative AI and agentic AI. It is the only category to receive such a designation from more than half of respondents, signaling that for most organizations, protecting sensitive information remains the foremost barrier to AI adoption and scaling.
This level of concern is notably higher — by 16% — than the next-most-cited critical issue: legal and regulatory compliance, which 39% of respondents considered as critical. This gap shows how deeply security and privacy intertwine with business trust, liability and long-term risk exposure. While evolving legal frameworks will help shape compliance boundaries, many enterprises feel less prepared to manage the immediacy and technical complexity of securing data in AI-driven environments.
Rounding out the top six concerns are the quality and accuracy of AI-generated responses, ethical and bias-related issues, the total cost of ownership and internal trust and skepticism. Each of these areas of risk potentially introduces operational friction and jeopardizes reputational risk. For instance, poor response accuracy can quickly erode confidence in AI outputs, while ethical blind spots or biased models may expose businesses to public backlash and lost credibility.
The complete list of issues identified by respondents reflects a broad and growing awareness that deploying GenAI and agentic AI is not just a technical challenge — it also includes significant governance, leadership and management challenges. Organizations must proactively assess not just what these systems can do, but also what could go wrong and how these potential failures would manifest themselves in real business contexts. As we move further into the AI era, this risk awareness will be essential in separating hype from lasting value, along with data and analytics governance, which creates the policies, controls, standards, decisions, rights/accountability, processes, procedures and technologies that organizations need to maximize the value and minimize the risk created by AI.
AI Risk Management Framework: Best Practices
Overcoming the risks associated with GenAI and agentic AI requires more than technical fixes — it demands a comprehensive, disciplined approach to risk triage and trust governance. While we’ve previously covered how organizations can address data quality and integration challenges, this article focuses on the remaining, often more complex, categories of risk.
Classify Risks Into a Clear Taxonomy
As the McKinsey authors of "Rewired" recommend, organizations should embed AI-specific risk management into their broader enterprise risk framework. This begins with identifying and classifying digital risks — covering models, data assets and AI solutions — into a clear taxonomy. From there, teams can score these risks based on their potential impact. This methodical approach helps leaders avoid both blind optimism and initial resistance to new technology, creating a foundation for responsible innovation.
Develop Clear Trust Policies
Central to this effort is the development of robust trust policies. These must go beyond traditional privacy statements, encompassing how AI systems use personal data, what guardrails govern their behavior and how fairness, explainability and bias are monitored. Concrete standards — like AI transparency thresholds, real-time model monitoring, fairness audits and persistent privacy protections — should be embedded into policy and practice alike. Automating as much of this compliance as possible is essential. By translating trust policies into code through MLOps pipelines, organizations can enforce rules and reduce human error at scale.
Create Data Protection and Governance Controls
A specific technical consideration lies in the way large language models (LLMs) create and store "data chunks." These chunks must inherit the data protection and governance rules of their sources, especially when they contain sensitive or proprietary information. Persisting these controls in data catalogs or governance platforms ensures that even as AI systems learn and adapt, the original privacy and compliance rules stay intact. This is the only scalable way to manage risks tied to data security, regulatory exposure and ethical misuse.
Keep Humans in the Loop
Finally, concerns about quality, accuracy and unintended consequences must not be underestimated. These issues are best addressed by ensuring high-quality inputs and maintaining human oversight — particularly with agentic systems that take autonomous actions. The goal of GenAI is not to eliminate people but to augment them. Organizations must embrace this mindset shift, recognizing that human judgment remains essential for validating AI outputs, refining behavior and ensuring alignment with core business values. In this way, the future of AI becomes not just powerful, but trustworthy.
Embed Trust Into the Architecture of AI
The path forward with generative and agentic AI isn’t just about advancing capabilities — it’s about managing consequences with clarity, discipline and foresight. As this series has shown, AI’s greatest promise comes hand-in-hand with its greatest risks. From data privacy and security to legal exposure, ethical pitfalls and trust deficits, the challenges are real — but they are also navigable.
Organizations that succeed won’t be the ones with the flashiest models, but those with the strongest governance, clearest policies and most thoughtful integration of human oversight. This means embedding trust into the architecture of AI — automating risk controls, enforcing policy as code and never losing sight of the human judgment that must guide machine intelligence. The future of AI will be shaped not just by what it can do, but by how responsibly we deploy it. It’s time to lead with intention.
Don't miss Part 1, Part 2, Part 3, Part 4 and Part 5 of our AI Revolution series.