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

AI Security Posture Management: An overview

AI Security Posture Management (AI-SPM) includes tools, practices, and processes designed to secure AI-related resources. AI-SPM is a broad category, and the exact types of workloads that it helps protect can vary depending on which AI solutions a business uses and how it manages them. For example, AI-SPM would look different at an organization that builds and trains its own AI models, as opposed to one that solely uses third-party AI solutions.

That said, common examples of the types of risks that AI-SPM helps to mitigate include:

• Prompt injection vulnerabilities in AI models.
• Insecure training data, which could expose sensitive information to third parties or, worse, allow them to manipulate training data in ways that impact model behavior.
• Lack of proper access controls in machine learning (ML) pipelines, meaning the set of tools used to design, build, test, and deploy AI models. This is another flaw that could lead to the leakage of sensitive data or the injection of malicious code or training data.
• “Shadow AI,” meaning AI tools or services that employees use without their company’s knowledge or permission.

The AI-SPM concept builds on the same principles as practices like Cloud Security Posture Management (CSPM), which focuses on managing risks related to cloud environments. In a similar fashion, AI-SPM addresses AI-related risks.

Why is AI-SPM important?

AI workloads and resources introduce novel risks that other types of security solutions don’t fully address. For example, while vulnerability scanning can detect vulnerabilities in traditional applications, vulnerability scanners are not designed to identify vulnerabilities in AI models, such as flaws that enable prompt injection attacks. Likewise, while businesses can deploy various tools to help secure CI/CD pipelines, these solutions don’t typically cover ML pipelines, which consist of different workflows and tools than CI/CD pipelines.

The lack of coverage of AI risks by other tools – combined with widespread adoption of AI solutions, which 77 percent of companies are now either exploring or actively using – means that having a set of security solutions dedicated to protecting AI resources has become critical. Without AI-SPM, businesses are in a poor position to ensure that AI solutions don’t become the weakest link in their overall security posture.

AI-SPM features and capabilities

The main features and capabilities of AI-SPM tools include:

• AI inventory management: AI-SPM solutions can inventory the AI applications and services that are in use within a business. This feature provides visibility into how organizations are using AI, and it helps to identify instances of “shadow AI” deployments that the IT department would otherwise not know about.
• AI security assessment: By analyzing the security controls and policies that are in place to secure AI models and training data. AI-SPM helps businesses identify misconfigurations that could expose critical AI resources to access by malicious parties.
• Model testing and monitoring: AI model testing and monitoring capabilities to detect weaknesses like whether a model is susceptible to leaking sensitive business data.
• ML pipeline monitoring: Monitoring of ML pipelines  – meaning the workflows AI developers use to design and build models – helps identify anomalous access patterns, which could signal a breach of AI resources by threat actors.

AI-SPM vs. MLSecOps

AI-SPM complements, but is distinct from, MLSecOps.

MLSecOps refers to the integration of security into machine learning processes. In essence, it does for ML workflows what DevSecOps does for software development workflows: It makes security a primary consideration throughout all steps of the process.

In contrast, the goal of AI-SPM is to identify risks that arise from the way an organization builds or uses AI technologies. AI-SPM may help to identify risks within ML workflows that a business should address by enhancing its MLSecOps practices, which is how A-ISPM can complement MLSecOps. But AI-SPM is not the same thing as actually embedding security practices into ML workflows.

AI-SPM vs. CSPM and DSPM

AISPM is also distinct from both Cloud Security Posture Management (CSPM) and Data Security Posture Management (DSPM).

As noted above, CSPM focuses on managing security risks associated with the cloud, such as insecure Identity and Access Management (IAM) configurations. The purpose of CSPM is to mitigate security risks of all types within cloud services and cloud-based workloads. CSPM may help to secure AI in cases where AI applications and services run in the cloud, but the overlap between CSPM and AI-SPM ends there.

Likewise, the goal of DSPM is to identify and protect sensitive data resources, including but not limited to those used by AI apps and services. Since data is central to most ML pipelines and AI workloads, DSPM capabilities that protect sensitive data can complement AI-SPM security features. But DSPM addresses a distinct type of risk.

Securing AI with Aqua

As a code-to-cloud cybersecurity platform, Aqua provides key capabilities – for building and using AI securely – including secure LLM development, AI security risk mitigation based on the OWASP Top 10 risks for LLM apps, and advanced runtime protection for AI workloads that detects and responds to threats in real time. 

Learn more about Aqua’s AI application features.