Protecting AI Infrastructure | SentinelOne AI Security Posture Management (AI-SPM) Overview

Widespread adoption of AI introduces unique security considerations. Misconfigurations of AI infrastructure and insecure APIs can create weaknesses that cybercriminals will exploit. In this blog post, We take an in-depth look at how SentinelOne’s agentless AI Security Posture Management (AI-SPM) solution addresses these challenges by delivering AI inventory automation, misconfiguration detection, and path analysis. attack tools to help organizations effectively secure their AI workloads..

More and more organizations are deploying generative AI (GenAI) models on public clouds like AWS due to their on-demand scalability, specialized infrastructure like high-performance GPUs and TPUs, and cloud management platforms. AI such as Amazon SageMaker, Amazon Bedrock, Azure OpenAI and Google Vertex AI.

This trend is driving rapid growth in global investment in artificial intelligence (AI), with IDC’s Global AI and Generative AI Spending Guide spending on AI-enabled applications, infrastructure and services is expected to more than double by 2028, reaching $632 billion at a compound annual growth rate (CAGR) of 29%. This increase will represent nearly 40% of overall public cloud spending over the next three years.

Protecting against evolving AI threats

As our reliance on AI technology increases, it becomes increasingly attractive to cybercriminals. Malicious actors are looking for new ways to exploit misconfigured AI infrastructure, taking advantage of security vulnerabilities to manipulate models or steal sensitive data. This evolving threat requires organizations to proactively protect their AI systems against new and existing risks.

One of the most common AI-related security threats is data theft. For example, If an AI developer creates an Amazon Bedrock training job to train a machine learning model but fails to attach it to a virtual private cloud (VPC), this misconfiguration could expose the job to the Internet.. A case like this could allow adversaries to intercept or access sensitive training data, potentially compromising personally identifiable information (PII) or confidential business data. Additionally, insecure API endpoints for AI models may allow malicious actors to directly interact with the models, which could lead to misuse of the models.

How SentinelOne protects AI workloads

SentinelOne’s AI-SPM solution, available as part of Cloud native security (CNS), helps address the evolving risks associated with GenAI. AI-SPM was designed from the ground up to protect your AI models and pipelines deployed on managed AI services such as Amazon SageMaker, Amazon Bedrock, Azure OpenAI, and Google Vertex AI. SentinelOne’s AI-SPM produces three main results.

Automated AI Infrastructure Inventory

AI-SPM discovers AI services such as machine learning (ML) models, training and processing tasks, and pipelines. For example, if your organization uses Amazon SageMaker to manage your AI/ML pipeline, you will have end-to-end visibility into SageMaker notebook instances, SageMaker endpoints, and models deployed in that pipeline.

AI-Native Configuration Error Detection

AI-SPM’s built-in security rules provide insight into misconfigurations in AI services such as AWS SageMaker, Bedrock, Azure OpenAI, and Google Vertex AI.. For example, if an Amazon SageMaker notebook instance is configured with direct Internet access, AI-SPM generates an exposure and recommends actions to remediate it. Additionally, support for frameworks such as European AI law And NIST AI Risk Management Framework helps CNS customers ensure AI workloads comply with AI security standards.

Actively address potential issues with attack paths

By visualizing attack paths related to AI workloads, you can see Exactly how an adversary could traverse your environment and potentially move laterally to access resources.

Conclusion

As global investments in AI continue to increase, promising on-demand scalability and specialized AI infrastructures, organizations will focus on managing the unique security challenges that arise. Misconfigured AI systems, such as exposed endpoints or improper access controls, are low-hanging fruit that malicious actors seek to exploit, potentially leading to model manipulation or data compromise . Proactive security measures like SentinelOne’s AI-SPM are becoming essential to protect critical business data and ensure the integrity of AI workloads in the face of rapidly evolving AI-based threats..

Eager to learn more about SentinelOne cloud security abilities? Visit us at Kiosk #1672 see you next time AWS re:Invent conference and see SentinelOne AI-SPM in action. Enjoy hands-on demos with product experts, our Mortal vs. Mortal Challenge. Machine, big gifts and the chance to stock up on the latest SentinelOne goodies.