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

How AI-SPM Works

AI security posture management covers AI cybersecurity risks with the following processes: 

• AI discovery and inventory: AI-SPM scans environments, e.g., Amazon Bedrock, Azure AI Foundry, and Google Vertex AI, to generate a full inventory of all AI models and associated resources, data sources, and data pipelines involved in training, fine-tuning, deployed within the cloud environment(s). AI-SPM then correlates signals across data classification and discovery, data access paths and potential exposure of sensitive data to AI, identifying potential vulnerabilities and misconfiguration to help users rapidly uncover hidden AI risks.
• Risk management: AI-SPM helps to identify, prioritize and remediate risk (through identifying and classifying sensitive or regulated data, such as personally identifiable information (PII), for example) and compliance violations that could lead to data exfiltration or unauthorized access to AI models and resources. It also uses threat intelligence to detect malicious invocation of AI models and potential misuse of AI resources. Alerts are generated when high priority risk or violation is detected along with security recommendations for rapid response.
• Compliance and security posture management: AI-SPM ensures secure configuration of AI models, including data protection, access controls, and more. It provides comprehensive visibility into AI and data compliance posture, automatically mapping security posture against regulations like GDPR or HIPAA as well as AI-specific standards like NIST AI RMF 600-1 to prioritize compliance violation and minimize the risk of legal liabilities.

Why Is AI-SPM Important?

As AI systems are increasingly being integrated into critical business functions, such as decision-making, automation, and customer interaction, securing these systems has become a top priority. Cloud service providers now provide GenAI-as-a-Service offerings, e.g., Amazon Bedrock, Azure AI Services, and Google Vertex AI. These GenAI Services can further accelerate GenAI adoption. AI systems, which encompass machine learning models, large language models (LLMs), automated decision systems, present unique vulnerabilities, and attack surfaces. 

More and more organizations are integrating their corporate data sets into their AI applications, in many cases exposing sensitive data. With rapid adoption organizations are also facing unique AI-specific threats targeting the AI ecosystem. Key attack vectors include:

• Data poisoning: Malicious actors inject harmful data into training datasets, causing models to adopt biased or compromised behaviors.
• Adversarial attacks: Subtle manipulations in input data mislead AI systems, resulting in inaccurate predictions or decisions with potentially severe outcomes.
• Model extraction: Attackers steal proprietary models by probing outputs to reconstruct internal parameters, leading to intellectual property theft or misuse.

AI-SPM is the solution to these challenges. By anticipating vulnerabilities and securing AI models from design to deployment, AI-SPM mitigates risks and ensures AI development prioritizes security and resilience across the entire lifecycle.

On top of these challenges, teams must remain vigilant in the face of evolving AI and data related compliance requirements, which mandate responsible data handling and model governance. Audits, regulatory frameworks, and industry standards continue to broaden in scope, reflecting the world’s growing dependency on AI-powered solutions. As AI programs become integral to how companies deliver services, it falls on decision-makers to ensure that robust defensive measures are embedded into daily operations. This level of vigilance untangles the complexities of innovation and safeguards the organization’s reputation over the long haul.

AI-SPM differs from traditional security approaches that requires a deep understanding of both AI technologies and the unique risks AI adoption pose. AI-SPM involves a comprehensive evaluation of all components within the AI ecosystem, including machine learning models, training data, APIs, and the infrastructure supporting the AI deployment. This holistic view allows organizations to detect weaknesses that may be exploited by attackers, such as data poisoning, model evasion, or manipulation.

What Risks Does AI Introduce?

Despite the robust advantages AI brings to security posture management, it also creates new areas for risk management teams to be aware of.

• Lack of AI landscape visibility: Security teams often lack insight into all active AI tools and services, making it difficult to identify shadow AI deployments and manage potential risks.
• Shadow AI: Security teams struggle to track which AI models are deployed, whether they are officially approved, properly maintained, and meet current security standards.
• Data governance: Organizations frequently face challenges in monitoring and restricting which sensitive data is shared with external and internal AI services, increasing the risk of leaks.
• Misconfiguration errors: Inadequate oversight in configuring AI services can result in accidental exposure of sensitive information or unauthorized access, increasing the attack surface.
• Compliance violations and legal penalties: Improper AI data handling or deployments can lead to breaches in regulatory mandates such as GDPR and HIPAA, resulting in costly fines and reputational damage.
• Operational risks: AI systems can malfunction or produce unexpected outcomes, potentially disrupting business operations.

Core Features of AI Security Posture Management (AI-SPM)

There are several distinctive elements that set AI-driven security posture management apart, each enhancing a company’s capability to fight off digital adversaries:

• AI landscape visibility: Gain full visibility into AI landscape
• AI discovery and inventory: Automatically discover and inventory AI models with activity, data lineage, and security issues
• AI data security: Classify all the data stored in AI projects, as well as data used to fine-tune AI models to prevent accidental sensitive data usage or exposure
• AI lineage: Understand how AI models interact with data and visualize how sensitive data flows across AI pipelines
• AI risk management: Understand, prioritize, and remediate risks associated with AI data stores such as misconfiguration, excessive permissions, exposure
• AI data access: Enforce granular access policies to restrict unauthorized AI access, prevent model misuse, and ensure secure LLM interactions
• AI governance and compliance: Enforce policies and best practices that align with industry standards and regulations such as GDPR, HIPAA, and NIST's AI Risk Management Framework

AI-SPM vs. DSPM vs. CSPM

AI-SPM ensures the safe and responsible use of AI technologies that process and analyze the data, while data security posture management (DSPM) provides a foundation for data protection, ensuring its confidentiality, integrity, and availability. Cloud security posture management (CSPM) safeguards cloud environments by continuously monitoring configurations and enforcing security best practices to prevent vulnerabilities.

Integrating all three enables organizations to safeguard their AI systems, data assets, and cloud environments, minimizing risks and ensuring data compliance with relevant regulations. Below is a table comparing them all:

Use Cases for AI-SPM

AI-driven processes now touch nearly every industry, unlocking new possibilities for data analytics, automation, and personalized customer experiences. However, ensuring the security and reliability of these AI solutions demands a proactive stance in protecting data, models, and infrastructure. AI-SPM solutions help in this respect by:

• Minimizing exposure points: AI-SPM continuously maps and monitors all access points, privileges, and integrations within AI systems, reducing the potential entryways for attackers and limiting the overall attack surface.
• Securing AI model life cycles: Security posture management identifies vulnerabilities in development environments and deployment pipelines for machine learning models.
• Enforcing data privacy safeguards: Sensitive information—ranging from customer and financial data to proprietary research—stays fully monitored and protected, whether at rest or in motion.
• Providing robust incident response: AI-SPM prioritizes security alerts, enabling faster reactions to potential threats and minimizing the damage from intrusions.

Best Practices for AI Security Posture Management (AI-SPM)

Implementing AI-SPM effectively can feel daunting, yet certain core principles make the journey clearer. It begins with deliberate planning, open discussions about potential challenges, and a commitment to holistic security practices, such as:

• Comprehensive risk assessments: Conduct in-depth evaluations of AI workflows and data pipelines to determine where risk is highest.
• Policy-driven access controls: Establish least-privilege protocols that govern which stakeholders can modify or even view sensitive models and datasets.
• Continuous monitoring: Use automated tools and security dashboards to observe real-time activity, spotting suspicious behaviors early.
• Regular model testing: Validate machine learning outputs through dynamic testing, ensuring adversarial tactics can be detected and mitigated.
• A transparent governance framework: Maintain clear responsibilities across cross-functional teams, enabling swift and coordinated incident response when anomalies arise.

AI and Zero Trust Architecture: Enhancing Security Posture

The foundation of artificial intelligence tools lies in data, and the dual approach of DSPM and AI-SPM helps protect both. Under a zero trust architecture, no device, user, or service is presumed trustworthy; every step of access involves context-based verification. An AI model housing sensitive information benefits from this stance by examining each data request as potentially harmful until proven otherwise, closing the figurative “open window” that threat actors would otherwise exploit.

AI-SPM is one capability of a DSPM solution, expanding on data-centric defenses by applying security controls at the modeling layer as well. Zero trust concepts embed deeply into AI-SPM to ensure that applications and microservices communicate securely, no matter how many new endpoints are added. The result is safer collaboration among data scientists, analysts, and IT teams—individuals who handle significant, often real-time information to drive critical business insights.

Organizations that adopt zero trust principles for their AI environments place as much emphasis on model integrity as they do on data confidentiality. Protecting the logic of these models—and the information they derive—is crucial for consistency in decision-making processes. When an enterprise crafts a zero trust strategy anchored by AI-SPM, it positions itself to manage operations at scale while avoiding the pitfalls that arise when data, infrastructure, and business directives fall out of sync.

Zscaler AI Security Posture Management

Zscaler AI-SPM protects against AI-specific risks, including data exposure, misuse, and model governance, with a focus on securing generative AI (GenAI) workloads in the public cloud. 

As a part of the Zscaler AI Data Security platform and integrated with our existing data security posture management (DSPM) solution, AI-SPM provides end-to-end visibility into AI models , sensitive inference data, model deployments, and risk correlation. By monitoring model configuration, data flows, and system interactions, it identifies security and compliance risks traditional tools often miss, with:

• AI landscape visibility: Discover and maintain an inventory of all AI models being used across their cloud environments, along with the associated cloud resources, data sources, and data pipelines involved in training, fine-tuning, or grounding these models.
• Data security: Identify data sources used for training AI models to discover and classify sensitive or regulated data—such as personally identifiable information (PII) that might be exposed through the outputs, logs, or interactions of contaminated models. 
• Risk management: Evaluate AI deployments for vulnerabilities, misconfigurations, and risky permissions, mapping connections and providing remediation to reduce attack paths, data breaches, and operational or reputational damage.
• Governance and compliance: Automate policy enforcement, best practices, and audit trails for AI deployments to ensure regulatory compliance (e.g., GDPR, NIST), minimizing legal risk and aiding in regulatory adherence.

See Zscaler AI Security Posture Management in action—request a demo to discover how we secure your AI models, data, and deployments in the cloud.