What is AI risk management?
AI risk management is a set of tools and practices for assessing and securing artificial intelligence environments. It's a sub-field of AI governance focused on creating a structured approach. Because of the non-deterministic, fast-evolving, and deep-tech nature of AI, effective AI risk management and SecOps requires more than just reactive measures.
The aim is to identify and address potential risks and vulnerabilities in AI systems before they become threats to the organization. It demands an agile AI security framework that outlines a consistent set of decisions and processes for AI risk management.
AI systems are increasingly becoming embedded in enterprise IT, with GenAI leading the transformation of internal processes and customer experiences across organizations of all sizes and industries. But with great opportunity comes great risk: How do you unleash AI’s full potential without opening Pandora’s box?
While some of the main benefits of AI risk management processes are ensuring regulatory compliance and minimizing financial risks, an AI risk management framework is not just about ticking boxes. A solid AI risk management framework helps fortify security, improve decision-making, and boost operational resilience.
All these benefits translate to a quicker rollout of an AI proof of concept (PoC) and increased trust and transparency both internally and externally. The bottom line? Successful AI risk management both safeguards your AI investments and accelerates them.
This guide aims to fast-track you through the essentials of AI risk management, with a focus on defining a robust AI risk management framework. Let’s get started.
GenAI Security Best Practices [Cheat Sheet]
Discover the 7 essential strategies for securing your generative AI applications with our comprehensive GenAI Security Best Practices Cheat Sheet.
Download Cheat Sheet
What does AI risk management protect your organization from?
Every AI system is different and comes with its own unique set of characteristics and risks. Keeping in mind that AI risks typically belong to more than one category, we can generally categorize AI risks into four types:
Data risk: Data vulnerabilities threaten to compromise the integrity and confidentiality of your enterprise data. If compromised, data can be exfiltrated, leaked, or even manipulated. A notorious example was discovered by the Wiz Research team, who found that Microsoft researchers accidentally exposed 38 TB of sensitive data due to a misconfigured AI model.
Model risk: Here, the AI model itself is the target. Hackers might employ adversarial attacks or model inversion to manipulate a model’s behavior, potentially altering or extracting sensitive information. For instance, Wiz Research identified an architecture vulnerability in Hugging Face that allows attackers to manipulate hosted models, which could result in remote code execution or a loss of model integrity.
Operational risk: This risk threatens your ability to keep your AI systems running smoothly. Attacks like DoS or supply chain threats can cripple your AI operations. For instance, in May 2024, Wiz Research discovered vulnerabilities in SAP AI Core endangering customers’ cloud environments and private AI artifacts, which could potentially spread across different services and applications.
Ethical/compliance risk: AI risk can have far-reaching ethical and legal implications. These non-functional risks include biases, lack of explainability, and even hallucinations in AI outputs. (The CVSS 10 prompt injection flaw discovered in an LLM-to-SQL library reveals just how dangerous prompt injections are.)
AI risk management addresses these interconnected risks and negative impacts so that organizations can rely on robust frontline defense against the expanded attack surface of AI, the rapidly evolving nature of AI-related threats, and the inherent unpredictability of these models.
Essential AI Security Best Practices
To manage risks associated with AI, organizations need a strategic and well-coordinated security approach that extends traditional cybersecurity measures to the unique needs of AI.
En savoir plus
Top 6 AI risk management frameworks
Your organization’s AI risk management framework is the blueprint for securing all your AI systems. Since there’s no one-size-fits-all template, you can look at existing AI risk management frameworks and tailor their AI security processes to your specific requirements.
Remember, defining an AI risk management framework is an iterative process that evolves with your AI deployments and general advancements in AI.
Here are six leading AI risk management frameworks to consider when defining your own:
1. NIST AI RMF: This framework breaks down AI security into four core functions—Govern, Map, Measure, and Manage. It’s versatile and applicable across various industries, making it a solid starting point for most organizations.
Best for:
Defining the roles and responsibilities within your AI risk management team.
Mapping roles to specific risk management tasks.
2. ISO/IEC 23894:2023: This international standard promotes consistent and transparent AI practices throughout the AI lifecycle. It’s particularly useful for organizations operating worldwide who are looking to improve cross-border AI operations.
Best for:
Aligning your AI risk management practices with global and regional regulatory requirements.
Achieving compliance across markets.
3. MITRE’s Sensible Regulatory Framework for AI Security: MITRE offers a deep technical view of AI risks, focusing on specific attack tactics and proposing AI regulations to mitigate these threats. It’s a go-to for all organizations that want to understand the threat landscape and its regulatory implications, making the MITRE framework especially relevant for heavily regulated industries like finance and healthcare.
Best for:
Using MITRE's detailed threat models to enhance your threat detection capabilities.
Incorporating regulatory recommendations to ensure your framework is compliant with emerging regulations.
4. Google’s Secure AI Framework: Google’s standard outlines six core elements for AI safety, focusing on concepts like secure development, data protection, and threat detection. This framework is particularly useful for organizations already leveraging Google Cloud services, but its principles can be broadly applied.
Best for:
Making sure your AI pipelines follow secure development practices.
Enhancing overall risk management capabilities.
5. McKinsey’s AI Security Approach: McKinsey provides a more business-centric framework, focusing on identifying and prioritizing AI risks. This framework is ideal for organizations looking for peace of mind that their AI risk management is not only technically but also strategically sound.
Best for:
Aligning your framework with your organization's overall risk tolerance and business priorities.
Ensuring data privacy and AI model transparency are present.
6. Wiz’s PEACH Framework: Built around tenant isolation, the PEACH framework is designed to keep different AI workloads secure and separate. It’s especially relevant if your AI operations are cloud based and require robust segmentation of resources.
Best for:
Modeling and improving tenant isolation from the get-go.
Managing the attack surface exposed by user interfaces.
Top 4 Security Risks of Gen AI, According to Gartner
Gartner offers insights and recommendations for security and product leaders to gain a competitive edge by addressing the key transformation opportunities presented by these risks.
Download Guide
How does AI-SPM enable AI risk management?
AI security posture management (AI-SPM) is how you put your AI risk management framework into action. When choosing an AI-SPM solution, look for these three foundational capabilities:
Visibility through an AI-BOM (AI bill of materials): You can only secure what you’re aware of. AI-SPM tools provide a detailed AI-BOM so you’re always up-to-date with what components need protection in your AI systems. An agentless AI-BOM system that automatically detects new or changed AI deployments can effectively overcome the threat of shadow AI.
Proactive risk mitigation with context: AI-SPM tools don’t just alert you to risks, they help prioritize them according to your organization’s risk tolerance. By providing context, these tools enable you to focus on the most critical risks first. An AI-SPM can help your teams categorize and mitigate risk efficiently, with industry standards introduced by design.
Threat detection in AI pipelines: Early detection is key to mitigating threats before they spiral out of control. AI-SPM tools continuously monitor your AI pipelines, catching issues as they arise. Integration with the extended ecosystem of SecOps tools unlocks the ability to alert early and set automated containment actions against threats.
Wiz AI-SPM stands out as the first CNAPP to provide advanced AI SecOps functionalities within a centralized solution that bridges the gap between data scientists and SecOps teams. Through user-friendly dashboards and analyses, Wiz AI-SPM makes it easier to secure your AI deployment一leveraging tailored integrations for Amazon SageMaker, Vertex AI, and OpenAI一without slowing down innovation.
Accelerate AI Innovation, Securely
Learn why CISOs at the fastest growing companies choose Wiz to secure their organization's AI infrastructure.
