In this post, Kim Grönberg discusses the fundamentals of AI Security Posture Management (AI-SPM), how it compares with traditional CSPM solutions, its use cases, and why organizations should consider adopting it, especially as Microsoft prepares to release AI-SPM features.

AI Security Posture Management (AI-SPM): What Is It All About and Considerations for It

Author: Kim Grönberg
Published: 05.11.2024

AI Security Posture Management (AI-SPM) is a new toolset providing visibility and control over the security and posture of generative AI (GenAI) services. As the adoption of AI in organizations accelerates, managing associated security risks becomes increasingly critical. This blog outlines what AI-SPM is, how it compares to conventional cloud posture tools, its risk scenarios and use cases, and key recommendations for implementation.

What Is AI Security Posture Management (AI-SPM)?

AI-SPM gives organizations the means to understand, monitor, and protect the security posture of their AI stacks. The term “AI-SPM” gained traction in the past year as vendors, including Microsoft, WIZ, and Palo Alto, began extending their existing Cloud Security Posture Management (CSPM) platforms toward GenAI services. Through AI-SPM, companies can:

  • Inventory their AI stack
  • Identify and assess vulnerabilities
  • Map potential attack paths
  • Mitigate and react to security risks

Difference Between AI-SPM and CSPM and Their Collaboration

While both AI-SPM and CSPM share the goal of security posture management, their focus and specializations differ:

  • CSPM centers on cloud infrastructure security, emphasizing identification and remediation of infrastructure misconfigurations and enforcing compliance standards.
  • AI-SPM focuses on AI-specific elements: safeguarding the posture of AI models, datasets, and associated infrastructure. It delves deeper into vulnerabilities peculiar to AI systems and ensures AI-layer compliance and regulatory adherence.

Modern CSPM tools are starting to add AI-SPM modules, enabling organizations to monitor both their cloud infrastructure and AI subsystems from a consolidated dashboard. This minimizes monitoring gaps as organizations deploy more AI-based workloads.

Risk Scenarios and AI-SPM Use Cases

AI-SPM addresses several risk angles unique to AI environments, including:

  • Access Control Monitoring: Ensuring only authorized entities can access AI models and data, and preventing exposure of sensitive keys (like API keys) in public spaces.
  • Model Integrity Assurance: Detecting and preventing tampering of AI models by monitoring version changes, assuring only authorized updates are applied.
  • Plugin and Extension Security: Assessing and monitoring extensions used with AI systems for insecure dependencies.
  • Output Handling: Implementing output sanitization to block injection or other output-based attacks.
  • Training Data Protection: Identifying and mitigating “poisoned” or compromised training datasets through validation and anomaly detection.

When to Consider Adopting AI-SPM

Organizations should evaluate AI-SPM solutions when:

  • They lack a clear view of existing AI services and risk “Shadow AI” usage.
  • Numerous pilot AI projects are underway with uncertain security status.
  • New AI services are being introduced, requiring structured oversight and protection.
  • There is a need for ongoing, automated monitoring and hygiene of AI service security.

While AI-SPM adds important monitoring and mitigation capabilities, organizations should also adopt a security-first culture across AI lifecycle—from design and deployment, through to monitoring and response.

Microsoft’s Approach: Microsoft currently offers AI-SPM as a preview feature, integrated as an add-on to Microsoft Defender for Cloud. A general availability release is expected by the end of 2024.

Need Help?

If you are facing challenges implementing AI security or AI-SPM, consulting specialists (such as the author’s team) can assist with strategy, implementation, and ongoing operations.

About the Author
Kim Grönberg is the Head of Data & Cloud Security at Zure, with 8 years’ experience solving multi-cloud security challenges across industries such as finance and manufacturing. Kim currently focuses on the security of Data & AI services.

For further related content, visit the Zure Blog, which covers topics such as integrating Security Copilot into enterprise security architectures, data security posture management, and security within DevOps workflows.

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