AI Security Posture Management (AI-SPM)

Gain visibility and control over models, GenAI and the AI supply chain. Stop new attack vectors before they materialize.
AI Security Posture Management
Adoption of artificial intelligence (AI)

The rapid adoption of AI/ML leads to model corruption, AI model misuse and unwanted data exposure.

Read about our approach to AI security. Read the blog

  • New AI attack vectors

    Implementing new pipelines and infrastructure together with lack of unified visibility make organizations susceptible to new attacks.

  • Dealing with model sprawl issues

    Lack of AI inventory can lead to shadow AI models, compliance violations and data exfiltration through AI-powered applications.

  • Governance is lacking

    New AI-focused legislation mandates strict controls around AI usage and customer data that is fed into AI powered applications.

High-Level Benefits

Protect and control AI infrastructure, usage and data

Maximize the transformative benefits of AI and large language models (LLMs) without putting your organization at risk. Prisma® Cloud AI Security Posture Management (AI-SPM) gives you visibility and control over the three critical components of your AI security — the data you use for training or inference, the integrity of your AI models and access to your deployed models.

  • Detect unsafe or unauthorized model usage.
  • Reduce risk of data exposure from AI systems.
  • Ensure compliance with current and upcoming regulations.

    Protect and control AI infrastructure, usage and data

    Infographic: AI Risk Analysis

    • Training data classification
    • Model access governance
    • Live model inventory
    • AI misuse prevention
    • AI attack path analysis

    Our approach to AI security


    Visibility into AI Application Ecosystem

    Discover all AI applications, models and associated resources. Identify and trace lineage of AI components used in applications.

    • AI App stack discovery

      Discover all AI applications, models and associated resources.

    • AI lineage

      Identify and trace lineage of AI components and data sources used in applications.

    • Model inventory

      Catalog AI models deployed and identify updates.

    Discover, protect and govern AI and data
    AI Model Risk Analysis

    AI Model Risk Analysis

    Identify vulnerabilities in the AI supply chain, and find misconfigured models and related cloud resources that can lead to manipulation, misuse and theft.

    • Prevent model compromise and theft

      By identifying risks of adversaries to create a functional equivalent.

    • Find misconfigurations

      By reducing overprivileged compute instances and models.

    • Avoid insecure plug-in design

      By identifying overprivileged and vulnerable agents/workloads.

    Data Security Across Model Resources

    Manipulating model data can introduce vulnerabilities and biases, expose data, and lead to data privacy violations, compliance and security risks.

    • Classify the AI stack

      Identify where sensitive data exists in training and reference data, libraries, APIs, and data pipelines that power AI models.

    • Monitor sensitive data

      Monitor and govern data exposure, poisoning risk, privacy violations and security breaches.

    • Prioritize vulnerabilities

      In infrastructure that host AI that is accessing sensitive data.

    Real-time detection and response

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    Read how these customers keep their data safer with Prisma Cloud

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    Data Security Tip Sheet, Palo Alto Networks, August 2023; The Total Economic Impact™ Of Palo Alto Networks Prisma Cloud, Forrester Consulting, November 2023.