AI Security Posture Management (AI-SPM) Explained

Dana Raveh - September 10, 2024

<strong>AI Security Posture Management (AI-SPM)</strong> Explained
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What is AI security posture management (AI-SPM)?

AI offers immense opportunities and is reshaping today’s business landscape. With 64% of organizations anticipating that AI models will boost productivity and increase customer relations, AI is enabling companies to operate with greater agility and speed.1 But despite the productivity gains that AI can bring, as it integrates into an organization’s critical operations, maintaining a strong security posture is crucial to protect against evolving threats. This is where AI-SPM comes in.

AI-SPM is a strategic approach to safeguarding AI services and data by continuously monitoring, assessing, and enhancing their security posture against the evolving attack surface of AI systems. It involves identifying and fixing vulnerabilities across the entire AI model life cycle, from AI systems in containers to the runtime infrastructure where models are trained and deployed. By managing these vulnerabilities, ensuring compliance, and protecting against evolving threats, AI-SPM helps your organization maintain the integrity of AI-enabled operations, keeping deployments secure, resilient, and aligned with regulatory standards.

AI-SPM in DevSecOps

As AI becomes increasingly integrated into the software development life cycle (SDLC), it brings both opportunities and challenges. AI models are now essential components of modern applications, automating decision-making and enhancing functionality. However, their integration into the SDLC introduces new security risks.

AI-SPM integrates with your DevSecOps practices, ensuring that AI systems are secure throughout the development life cycle. This approach extends traditional security measures to include machine learning (ML) models, often referred to as MLSecOps, covering everything from code and data handling to model deployment. By embedding AI-SPM within DevSecOps, you can fortify the security of your organization’s AI initiatives, from development to production.

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It’s imperative that organizations consider key risks of using generative AI technology. Learn about five key considerations to weigh when evaluating generative AI for your team. Blog: 5 Questions to Use GenAI Responsibly

What risks does AI introduce?

AI-SPM helps your organization manage and safeguard against the risks that AI systems introduce, including:

Privacy and Data Security Risks

AI systems process vast amounts of data, as they require large datasets to learn and make accurate predictions or decisions. This makes them attractive targets for cybercriminals. A breach of such data can result in significant privacy violations, financial losses, and damage to an organization’s reputation. Because AI systems are integrated into many aspects of daily operations, safeguarding this data is crucial to preventing unauthorized access and protecting individual and corporate privacy.

Fraud and Identity Risks

AI technologies have the potential to create convincing fake identities and fraudulent content, such as deepfakes — realistic but fabricated images or videos. These technologies can deceive individuals and institutions by presenting false information as genuine, leading to serious risks like financial scams and identity theft.

Data Poisoning and Misinformation

The training data for AI models must maintain high quality to ensure the system’s accuracy and reliability. When bad actors tamper with the training set — a process known as data poisoning — they can introduce biased or malicious data that skews the model’s outputs. This manipulation compromises the AI’s performance, leading to incorrect or harmful results. This compromised data can spread misinformation and undermine the trustworthiness of AI systems, impacting decisions and actions based on flawed insights.

AI-Enabled Cyberattacks

AI can be leveraged to enhance the sophistication of cyberattacks by automating and optimizing attack techniques. For example, AI can identify vulnerabilities more quickly and develop tailored attacks that adapt to security measures. These advanced AI-enabled cyberattacks can evade traditional defenses and increase the speed and scale of breaches, making them more challenging to detect and mitigate. This elevated level of threat poses significant risks to organizational security and resilience.

Key components of AI-SPM

AI Inventory Management

AI-SPM tracks and catalogs all AI services, AI resources, and their components within an organization. Without effective inventory management, organizations risk losing visibility over their AI assets, potentially leaving shadow AI models unprotected and unmanaged. By discovering and maintaining an inventory of each AI model in use across your organization, AI-SPM maintains thorough oversight and ensures compliance with security standards.

Runtime Detection

Continuous observation of AI models in real time allows AI-SPM to detect unusual or potentially harmful activities like misuse, prompt overloading, unauthorized access attempts, and other abnormal activity. This model performance and behavior tracking enables early detection of security threats and operational issues so that you can ensure the security and reliability of your AI systems.

Attack Path Analysis

AI-SPM maps potential routes an attacker might exploit within an AI system. Identifying weak points and possible threats throughout the infrastructure, AI-SPM helps you understand where attacks could occur. This analysis can help support your strategy to prevent and mitigate attacks, strengthening your overall defenses and enhancing resilience.

Built-In Configuration

AI-SPM integrates security settings and policies directly into AI systems and their infrastructure. By embedding security settings and policies from the start, it automatically enforces best practices as you deploy. This helps prevent misconfigurations and keeps your AI models safer and more secure.

Benefits of implementing AI-SPM

Enhances Security

AI-SPM ensures that AI systems are secure by continuously monitoring their behavior and environment, detecting anomalies, unauthorized access, and potential breaches in real time. By providing deep visibility and detecting misconfigurations, AI-SPM minimizes the chances of costly breaches that could disrupt operations and damage your brand’s reputation.

Enables Regulatory Compliance

AI-SPM helps ensure your AI deployments comply with stringent security and privacy regulations (such as the GDPR), reducing the risk of fines and legal challenges and instilling confidence in stakeholders and customers. It assists in demonstrating due diligence in managing AI-related risks, which is crucial for regulatory adherence.

Improves Operational Efficiency

AI-SPM empowers organizations to efficiently and proactively identify potential threats before they can cause damage. It streamlines AI security management by automating the detection and remediation of risks, allowing your security team to focus on higher-priority tasks and reducing the overall cost of security operations.

Accelerates Innovation

With AI-SPM securing your AI infrastructure, you can confidently accelerate AI adoption and innovation, knowing that security is integrated at every step, from development to deployment. This allows your team to focus on driving new ideas and technologies while minimizing security concerns, ultimately leading to faster advancements and a stronger competitive position.

Allows You to Gain a Competitive Advantage

By implementing AI-SPM, you position your organization as a leader in secure AI practices. This proactive approach helps build trust with clients and partners by demonstrating a strong commitment to safeguarding sensitive data and maintaining high security standards. Having a robust AI-SPM strategy gives you a competitive edge to stand out in a crowded market and confidently navigate new opportunities.

The Schunk Group

Read this customer story and learn how The Schunk Group, an international high-tech company, protects its IT Infrastructure with cloud-native CrowdStrike Security.

Read Customer Story

How AI-SPM compares

By examining data security posture management (DSPM), cloud security posture management (CSPM), and application security posture management (ASPM), we can understand their distinct roles and see how AI-SPM uniquely enhances and completes a robust cybersecurity framework.

  • DSPM acts as a watchdog over where your data lives, how it’s secured, and who accesses it. DSPM helps organizations manage their data across the cloud, monitor for risks, enforce security policies, and ensure regulatory compliance.
  • CSPM provides visibility into misconfigurations and potential compliance violations for cloud environments, addressing security at the platform and infrastructure level.
  • ASPM is the holistic process of evaluating, managing, and enhancing the security stance of an organization’s custom applications. It ensures applications adhere to security standards, resist cyber threats, and remain compliant.

AI-SPM delivers specialized security capabilities that tackle the unique threats AI models and assets face. Though existing security solutions provide valuable protection, they often lack the comprehensive approach needed to secure AI environments. AI-SPM fills this critical gap by extending visibility into AI-specific risks, such as securing training data, protecting cloud-based AI models, and addressing vulnerabilities throughout the AI life cycle.

The true value comes with AI-SPM integrated into a cloud-native application protection platform (CNAPP). Though AI-SPM alone addresses the unique security challenges AI models face, its integration with a CNAPP creates a unified security framework that covers the entire cloud-native environment. This ensures AI assets are protected alongside cloud infrastructure, applications, and data, providing comprehensive, end-to-end security.

CrowdStrike’s approach

Enhancing the AI-native CrowdStrike Falcon® platform, AI-SPM provides comprehensive visibility into every AI model within your environment, including those that might be hidden as shadow AI.

The Falcon platform’s AI-SPM capabilities offer more than just visibility. By leveraging advanced agentless technology, the solution detects misconfigurations in leading AI services like OpenAI and Amazon Bedrock, ensuring your AI infrastructure is both secure and optimally configured. In addition to protecting your AI assets, AI-SPM also helps you maintain compliance with stringent security and privacy regulations, offering peace of mind that your operations are secure and compliant.

CrowdStrike’s commitment to AI security extends across the entire AI life cycle — from development to deployment — beginning with a shift left approach to securing AI development environments. By scanning container images before deployment, AI-SPM can identify AI models and detect any potential poisoning attempts, ensuring that AI development is secure from the outset and built on a solid foundation.

For customers running AI models within NVIDIA NIM containers,the Falcon platform’s AI-SPM’s runtime protection enables real-time monitoring of AI model behavior, allowing for swift detection and response to emerging threats. This proactive defense is crucial for maintaining the security and resilience of your AI assets in an increasingly complex threat landscape.

With CrowdStrike Falcon® Cloud Security, you’re not just gaining AI-SPM capabilities — you’re employing a complete, unified solution that protects every aspect of AI, ensuring your AI initiatives are safeguarded at every stage.

1 Forbes. How Businesses Are Using Artificial Intelligence In 2024.

GET TO KNOW THE AUTHOR

Dana Raveh is a Director of Product Marketing for Data and Cloud Security at CrowdStrike. Before joining CrowdStrike, Dana led marketing teams in cybersecurity startups, including Seemplicity Security and Flow Security (acquired by Crowdstrike), where she served as the VP of marketing. Dana also had various product management and product marketing roles in a number of global organizations, such as Checkmarx. She holds a PhD in cognitive neuroscience from University College London.