As cloud computing evolves, its security challenges are growing more intricate and diverse. As a result, there is a growing need for a comprehensive approach to managing and improving security posture to stay ahead of evolving threats. You may be familiar with Security Posture Management (SPM), a strategic approach to strengthening an organization’s security by continuously assessing, monitoring, and improving its security controls, policies, and preparedness to mitigate risks.
However, as organizations increasingly use artificial intelligence and machine learning (AI/ML) technologies across their operations, such as business, sales, and e-commerce, traditional SPM methods may not adequately address the unique security challenges posed by these advanced systems. Our Currents 2023 survey of technology professionals found that 37% of respondents plan to increase spend on cybersecurity in the next fiscal year. Notably, among those planning to boost their cybersecurity budget, 34% specifically cited the emergence of generative AI as a driving factor, highlighting growing concerns about new threats in this rapidly evolving space. This has led to the development of AI Security Posture Management (AI-SPM)—a specialized approach focused on managing the security risks associated with AI-powered applications, infrastructure, and data. Read on to explore AI-SPM, its benefits, how it differs from Cloud Security Posture Management (CSPM) and Data Security Posture Management (DSPM), and key factors to consider when choosing the right AI-SPM tool.
Summary
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AI-SPM is a proactive approach to securing AI systems that involves continuous assessment, risk mitigation, and protection of AI models, data, and infrastructure.
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Understanding the distinctions between AI-SPM, Cloud Security Posture Management (CSPM), and Data Security Posture Management (DSPM) is important for comprehensive protection across AI, cloud, and data environments.
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While selecting the right AI-SPM tool, assess AI security needs, prioritize automated risk assessment, ensure lifecycle coverage, and consider integration and scalability.
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What is AI-SPM?
AI-SPM is the ongoing process of maintaining and improving an organization’s security stance regarding its AI systems. It involves proactively identifying, mitigating, and managing potential security risks and vulnerabilities associated with deploying and using AI technologies.
This includes assessing the security of AI models, data, and infrastructure and implementing appropriate security controls and monitoring mechanisms to ensure the continued protection of an organization’s critical assets and sensitive information.
Benefits of AI-SPM
Effective AI Security Posture Management can provide numerous benefits to organizations, helping them improve their overall security posture and mitigate the unique risks posed by AI technologies. AI systems can introduce new vulnerabilities and attack vectors (a way for attackers to enter a network or system) that traditional security measures may not adequately address. Demonstrating a strong commitment to AI security builds greater trust among your stakeholders, including customers, regulators, and the public. This can be particularly valuable in industries such as healthcare, financial services, and public sector organizations where AI’s responsible and transparent use is a key concern.
Improved risk identification and mitigation
You can proactively identify potential security vulnerabilities within your AI systems, such as model flaws, data poisoning risks, or adversarial attacks. This allows you to implement appropriate countermeasures and security controls to reduce the likelihood and impact of such threats, strengthening the overall resilience of your AI-powered applications.
Increased compliance and regulatory adherence
By maintaining a storing AI security posture, you can ensure compliance with relevant industry regulations and data privacy laws. This is particularly important in sectors like finance, healthcare, or government, where the improper handling of sensitive data or the misuse of AI can lead to significant legal and financial penalties.
Reduced operational disruptions
Effective AI Security Posture Management can help you prevent or quickly respond to security incidents involving your AI systems, minimizing the potential for service disruptions, data breaches, or reputational damage. This can ultimately lead to improved operational continuity and improved customer trust.
AI-SPM vs CSPM vs DSPM
Understanding the differences and relationships between AI-SPM, Cloud Security Posture Management (CSPM), and Data Security Posture Management (DSPM) is important for cloud-based businesses. As organizations increasingly rely on cloud infrastructure and AI-powered applications that handle/store sensitive data, managing the security posture across these domains is essential for mitigating risks and ensuring comprehensive protection.
| Parameter | AI-SPM | CSPM | DSPM |
|---|---|---|---|
| Focus | Securing AI systems, models, and related infrastructure | Monitoring and managing the security configuration of cloud resources | Protecting the security of data throughout its lifecycle |
| Key concerns | Model vulnerabilities, adversarial attacks, data poisoning, AI system misuse | Misconfigurations, excessive permissions, lack of encryption, insecure network settings | Data privacy, unauthorized access, data breaches, data leaks, compliance with data regulations |
| Scope | Encompasses the entire AI lifecycle, from development to deployment | Focuses on the cloud infrastructure, including IaaS, PaaS, and SaaS offerings | Covers data security across on-premises, cloud, and hybrid environments |
| Example use cases | Detecting and mitigating adversarial attacks on a machine learning model used for fraud detection in a financial services application | Identifying misconfigured S3 buckets with public access in a cloud-based object storage solution | Classifying and securing sensitive customer information, such as financial records or medical data, stored across on-premises and cloud-based data repositories |
How to choose the right AI-SPM tool
Choosing the right AI-SPM tool helps identify and mitigate vulnerabilities, protect against attacks, and ensure AI’s secure and responsible use across your business. By investing in the right AI-SPM tool, you can help secure your AI systems, maintain compliance, and unlock the full potential of your AI-powered initiatives. Several AI-SPM tools are available on the market, including offerings from Prisma Cloud (by Palo Alto networks), Wiz, and Orca Security, each with unique features and capabilities. Here is a quick checklist to help you select your AI-SPM tool:
1. Assess your AI security and privacy needs
Start by evaluating your current AI architecture and identifying the specific security and privacy challenges you must address. Consider factors such as the complexity of your AI systems, the sensitivity of the data you’re working with, and the regulatory requirements you need to comply with. This will help you determine the key features and capabilities you should look for in an AI-SPM tool.
2. Prioritize automated risk assessment
Look for an AI-SPM tool that continuously and automatically assesses your AI systems’ security and privacy risks. This should include capabilities like model vulnerability scanning, adversarial attack simulation, and data poisoning detection. Automating these risk assessment tasks can help you stay ahead of emerging threats and vulnerabilities.
3. Ensure comprehensive lifecycle coverage
Ensure your AI-SPM tool supports the entire AI lifecycle—from development to deployment—addressing security and privacy at every stage. Prioritize tools with explainability features to understand decision-making processes, identify biases, and enhance transparency and trust in your AI applications.
4. Consider integration and scalability
Look for an AI-SPM tool that integrates with your existing cloud security practices and IT infrastructure, helping you centralize and simplify your AI security and privacy management efforts. Additionally, ensure that the tool can scale to accommodate the growth and evolution of your AI ecosystem.
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