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

The AI security equivalent of Cloud Security Posture Management (CSPM) and Data Security Posture Management (DSPM), AI-SPM offers a framework for monitoring AI ecosystems to identify risks and policy violations. With AI-SPM, businesses can maintain visibility, control, and governance while leveraging AI technologies. This allows to safely accelerate AI adoption without introducing dangerous security gaps.

The Importance of AI-SPM

AI is transforming the business world, enabling companies to automate workflows, optimize operations, and enhance decision-making. Through the power of machine learning and advanced data analysis, businesses can predict trends, personalize customer experiences, and improve efficiency across departments. This helps increase productivity while reducing operational costs.

The AI transformation has accelerated significantly in recent years due to the release of generative AI tools. Businesses now have the ability to create new content, develop ideas, and facilitate innovation at scale to develop the next generation of AI-powered products. The technology is even being deployed in cybersecurity for a range of use cases. 

With many benefits on offer, companies quickly began exploring how to effectively leverage these new technologies and integrate them into their operations. However, the rush to find new business benefits and get ahead of the competition often overlooks the significant security challenges AI systems introduce. 

In addition to the typical risks associated with expanding your attack surface by introducing new technologies (such as misconfigurations, software vulnerabilities, and API threats), AI deployments pose new and unique challenges. 

AI-specific attacks include:

• • Data Poisoning: Attackers introduce corrupted data into the training dataset to influence an AI model’s behavior. For example, an attacker might inject biased or misleading data to cause a model to perform inaccurately or dangerously.
  • Model Extraction: AI models are valuable intellectual property (IP). Attackers can reverse-engineer models by repeatedly querying and probing outputs, thereby extracting the logic and internal parameters. This poses a major security risk for companies that rely on proprietary AI technology.

• Adversarial Attacks: Manipulating AI systems through input data and finding ways to subtly mislead their outputs. Adversarial attacks can significantly impact the performance of an AI model, resulting in incorrect outputs that may cause substantial damage.

A major concern with AI systems is that they often have access to significant amounts of sensitive business data. This includes several GenAI security concerns, such as employees oversharing data. Statistics from Check Point’s 2025 AI Security Report found that 7.5% of GenAI prompts include sensitive or private details, and 1 in 80 prompts expose sensitive data to attackers. 

In the rush to maximize the positive business outcomes of generative AI, businesses are also freely opening up corporate datasets to these tools without considering the data security implications. Additionally, as vendors strive to make their tools as fast and easy to use, they often default to configurations that grant them broad access and excessive permissions. 

Finally, much AI use in the workplace is informal, often by employees without knowledge of the associated security concerns. This so-called shadow AI creates major visibility gaps, as security personnel cannot monitor its use or implement proper security controls to maintain data integrity and compliance.

All these factors combine to make AI systems impossible to manage using traditional security controls. To adequately address these risks, AI-SPM (AI Security Posture Management) is needed to provide comprehensive, AI-specific security.

How Does AI-SPM Work?

AI-SPM (AI Security Posture Management) is a structured framework designed to manage the security, governance, and compliance of AI systems throughout their lifecycle. This includes ensuring all stages of model development and deployment, as well as related data pipelines, are secure and compliant. 

Key aspects of AI-SPM include:

• AI Inventory Management: AI-SPM tools automatically discover all AI assets within an organization, including models, datasets, and inference endpoints. This provides full visibility into the AI ecosystem, including the identification of shadow AI or unauthorized models that may be running within the organization.
• Data Security and Governance: AI-SPM ensures that training datasets are properly classified, in terms of regulations or sensitivity to the organization, and that appropriate safeguards are in place to prevent unauthorized access or misuse. Common security controls utilized include data masking, encryption, and access control.
• Model Security: AI-SPM checks the integrity of models, ensuring that their behavior has not been altered maliciously. This includes checking for unauthorized access to models and verifying the model’s provenance.
• Threat Detection: AI-SPM tools also continuously monitor deployed AI models to detect suspicious behavior such as adversarial inputs. Any signs of data poisoning or model manipulation can be flagged for immediate action.
• Compliance and Auditability: Maintaining detailed logs of all activities across the AI lifecycle, from data collection to model deployment and inference. This provides detailed audit trails that demonstrate compliance with data protection regulations and industry standards.

Benefits of AI-SPM Implementation

These AI-SPM capabilities offer significant benefits to organizations leveraging AI in their operations. This includes the following:

• Enhanced AI Risk Visibility: AI-SPM provides comprehensive visibility into all AI-related assets, enabling security teams to quickly identify any risks or vulnerabilities that could compromise the system’s integrity.
• Enhanced Data and Model Protection: Continuously monitoring AI use for anomalies or suspicious behavior allows you to minimize the risk of costly data breaches, model theft, or malicious manipulation. This is particularly important for organizations with proprietary AI models or sensitive customer data.
• Compliance Readiness: AI-SPM simplifies compliance, ensuring that AI systems meet data privacy, transparency, and accountability requirements. With dynamic data security regulations, AI-SPM helps organizations avoid costly legal penalties and reputational damage.
• Operational Efficiency: Automating security checks, monitoring, and remediation allows organizations to maintain a proactive approach to AI security without overburdening their IT teams. 
• Safer AI Innovation: AI-SPM provides the governance and security framework necessary for organizations to safely innovate with AI. With the proper security measures in place, teams can experiment with new models and applications, confident that they are not putting the organization at risk.

Key Differences Between AI-SPM, DSPM, CSPM, and ASPM

AI-SPM (AI Security Posture Management) is analogous to other security posture management frameworks, including DSPM, CSPM, and ASPM. Each of these provides dedicated tools, processes, and strategies to oversee and safeguard their specific security domain:

• DSPM (Data Security Posture Management): Protects business data across an organization’s environments, ensuring compliance with data security standards and detecting misconfigurations or vulnerabilities. It helps businesses understand where sensitive data resides, how it’s accessed, and whether it’s properly secured against potential breaches or misuse.
• CSPM (Cloud Security Posture Management): Continuously monitors and manages an organization’s security posture in cloud environments. It helps identify and correct misconfigurations, enforce security policies, and ensure compliance with industry regulations, thereby reducing the risk of cloud vulnerabilities.
• ASPM (Application Security Posture Management): Focuses on securing an organization’s application throughout the Software Development Lifecycle (SDLC). ASPM involves mitigating code vulnerabilities and ensuring that applications remain secure against evolving threats, both during development and in production.

AI-SPM complements these frameworks by providing enterprise protections for an organization’s AI ecosystem. For example, extending DSPM protections to AI training data, protecting cloud-based AI workflows, and monitoring for unique AI vulnerabilities during the development of AI applications.

Best Practices When Implementing AI-SPM

Given its complex and broad nature, AI-SPM requires a structured and well-planned implementation to be successful. Best practices that help maximize the benefits of AI-SPM include:

• Set Clear Goals: Before deploying AI-SPM, clearly outline your security objectives and how they relate to your desired business outcomes. It can be tempting to want to go into deployment with the goal of making broad AI security improvements. But with more specific goals, you can better measure the performance of the new framework and its processes. For example, if you have proprietary AI technology and want to protect against specific threats, or you want to focus on data security and streamlining compliance audits.
• Start with a Pilot Program: Begin with a limited pilot AI-SPM program that focuses on a specific use case or environment, such as monitoring a particular AI service or starting with creating your AI inventory. This phased implementation approach enables you to evaluate the effectiveness of the framework and refine it before rolling it out across the entire organization.
• Integrate AI-SPM into DevSecOps Pipelines: Organizations that develop their own AI models or utilize techniques such as supervised fine-tuning to create curated in-house models should embed security and compliance checks into their DevSecOps pipelines. By spotting issues as early as possible in the development cycle, you can mitigate their impact and ensure they are resolved before deployment.
• Collaborate Across Disciplines: AI-SPM involves security teams, AI engineers, and other stakeholders working together to ensure that appropriate security controls are in place. This process is enhanced with effective collaboration channels between the various teams. By promoting collaboration, you can create a more holistic approach to AI-SPM where security is addressed at every stage.

Stay Protected with Check Point’s GenAI Solutions

By adopting AI Security Posture Management, companies can innovate with confidence, ensuring their AI systems are secure and compliant. As part of its Infinity AI security solution, Check Point offers dedicated AI-SPM capabilities to protect AI usage within your organization. This includes:

• GenAI Protect: Comprehensive AI discovery and data security enforcement.
• GenAI Application Protection: Real-time threat detection and response without impacting the user experience.
• GenAI Application Risk Scanner: Red teaming to simulate real-world attacks and test the security posture of generative AI systems.

Learn more about Check Point’s approach to AI-SPM and how it enables rapid AI adoption without the risk by scheduling a demo today.