Drawing on key insights from the paper “AI Risk Atlas: Taxonomy and Tools for Navigating AI Risks,” it’s clear the industry faces a crucial challenge. The authors provide a comprehensive framework for understanding, classifying, and mitigating the risks tied to today’s most advanced AI. But while tools and taxonomies are maturing, most enterprises are dangerously behind in how they manage these new and rapidly evolving threats.

The AI Risk Atlas offers a powerful framework for categorizing and managing the unique risks associated with artificial intelligence, but it’s important to recognize that it’s not the only system available. Other frameworks—such as the NIST AI Risk Management Framework, various ISO standards on AI governance, and models developed by leading cloud providers—also offer valuable guidance for understanding AI-related threats and structuring appropriate safeguards. Each has its own focus, strengths, and scope, whether it’s general principles, industry-specific guidelines, or practical checklists for compliance.

In this discussion, we will focus on the Atlas framework to develop a habit of using outside expertise and proven strategies when dealing with the complexities of AI in the cloud. The Atlas is especially useful for its organized taxonomy of risks and its practical, open source tools that help organizations create a clear and comprehensive approach to AI cloud security. By engaging deeply with such frameworks, enterprises can avoid starting from scratch and instead tap into the collective knowledge of the broader security and AI communities, making progress toward safer and more efficient AI.

We’re not paying attention

Too many organizations are treating AI like just another IT add-on, failing to recognize that AI—especially generative models and agentic technologies—has opened the door to attack vectors that simply didn’t exist five years ago. The AI Risk Atlas lays out this new threat landscape of adversarial inputs, prompt-based attacks, model extraction, data poisoning, and even risks from relying on automated systems too much or not enough.

Cloud security teams, who have spent years focusing on perimeter controls and access management, are now faced with adversaries who can bypass these measures by exploiting the language-based, context-sensitive behaviors of AI. Prompt injection is a prime example: Attackers manipulate the natural language prompts that drive generative models, causing these systems to generate malicious or harmful outputs. The AI Risk Atlas emphasizes that these vulnerabilities are no longer theoretical—they are being targeted in real-world cloud deployments.

Further complicating matters, the volume and diversity of data used to train modern AI mean there’s a rising risk of data poisoning or membership inference, where attackers reconstruct or expose sensitive information by querying the model. The Atlas emphasizes that the typical cloud-based enterprise is especially vulnerable here, given the interconnectedness of cloud data and the ease with which AI models can unintentionally leak insights about that data.

Most enterprises are not prepared

The AI Risk Atlas makes one thing abundantly clear: Enterprises’ current frameworks for risk assessment and mitigation are not enough. Organizations may have detailed inventories of their cloud assets and compliance routines, but few of these are designed to understand or surface risks unique to AI—much less the compounding risks introduced as AI systems act autonomously.

Moreover, AI governance is often manual, slow, and disconnected from everyday development. The Atlas emphasizes the need for a comprehensive, adaptable risk taxonomy that links technical vulnerabilities (such as adversarial exploitation) with process issues (poor documentation, untested models, unclear ownership, etc.). Without this, most organizations remain reactive, only addressing gaps after an incident.

The Atlas points out that as attackers become more sophisticated at using AI’s own capabilities to probe and exploit weaknesses, defenders are often left scrambling to adapt outdated protocols to threats they don’t fully understand. The explosion of generative AI deployments—sometimes in unsanctioned shadow IT projects—means blind spots abound.

‘Good enough’ risk management won’t cut it

If your organization’s risk management playbook still depends on annual audits or template compliance checks, the AI Risk Atlas warns this will not suffice. AI-driven systems evolve too quickly for checkpoint governance; they demand ongoing, dynamic surveillance. Most enterprises are unprepared to monitor and respond to the subtle risks posed by generative and agentic AI, especially in cloud environments.

Prompt-based attack vectors, for example, seldom appear in traditional security monitoring. However, they can cause everything from accidental data leaks to direct breaches if not proactively monitored. Likewise, nuances such as over-reliance on “black box” model outputs or failing to record enough model documentation can escalate minor issues into major incidents. The Atlas reminds us that technical, organizational, and human factors are now inseparable in AI risk.

Even as automation promises to expand compliance efforts, the Atlas emphasizes that automation can’t fix issues that aren’t clearly defined or properly governed. Many organizations are adopting AI before they’ve set clear risk boundaries, creating opportunities for exploitation that will only grow more serious as agentic systems (autonomous AI capable of taking actions and orchestrating cloud APIs) become more prevalent.

A new approach to risk assessment

Based on the guidance and taxonomy presented in the AI Risk Atlas, here’s how organizations should respond:

• Map assets to novel threats. Actively apply the Atlas’s categories, such as adversarial attacks, prompt injection, and model governance, to all cloud AI assets, not just the official systems.
• Automate wisely but keep oversight. Employ automated tools, including open source Atlas Nexus tools, but back these up with mandated human review, ongoing audits, and independent red-teaming.
• Integrate risk governance across teams. Build cross-functional risk response squads that include engineers, risk officers, and business leaders to ensure organizational alignment on what AI risk actually means.
• Test, attack, and document. Systematically stress test AI models using adversarial techniques and prompt-based attack scenarios. Rigorously document both model behavior and mitigation strategies.
• Educate and iterate. Educate your workforce on AI-specific threats and ensure continuous improvement—not just compliance—using metrics drawn from the Atlas.

This is an urgent call to action. The window to proactively fix these vulnerabilities is closing fast. The AI Risk Atlas isn’t just a taxonomy; it’s a call to arms for enterprises to radically improve their preparation and defenses. As AI becomes integral to cloud operations, organizations must ensure responsible, informed, and agile risk management before today’s emerging threats become tomorrow’s disasters.