AI security posture management encapsulates a holistic strategy for protecting the security and reliability of artificial intelligence and machine learning systems.
This multi-faceted approach includes continuous monitoring, assessment, and strengthening of the security stance with respect to AI models, data, and infrastructure. AI-SPM includes the critical task of identifying and remediating vulnerabilities, misconfigurations, and possible threats related to the use of AI, as well as ensuring compliance with relevant data privacy and security requirements.
Explanation of AI-SPM
In a cybersecurity environment where Artificial Intelligence (AI) assumes great importance, AI Security Posture Management (AI-SPM) has emerged as a critical element. The presence of AI systems such as machine learning models, large language models (LLMs), and automated decision systems creates distinct vulnerabilities and potential attack vectors. AI SPM addresses these challenges by providing tools to monitor, assess, and mitigate risks associated with AI elements within a technical framework.
Data Governance
AI legislation is enforcing strict regulations around the use of customer data within AI and AI applications, forcing most organizations to implement governance capabilities beyond the norm. AI Security Posture Management (AI-SPM) scrutinizes the origins of data used to train and build AI models, identifying and classifying sensitive or regulated data, such as customer personally identifiable information (PII), that could potentially be leaked through compromised model results, records, or engagements.
Runtime Detection and Monitoring
AI-SPM continuously monitors user interactions, cues, and inputs to AI models (such as large-scale language models) to detect misuse, excessive prompting, unauthorized access attempts, and anomalous activity related to the models. It reviews AI model results and records to identify possible cases of sensitive data leakage.
crisis management
AI-SPM helps organizations detect weaknesses and misconfigurations in the AI supply chain that could lead to data breaches and unauthorized access to AI models and resources. This advanced technology provides a thorough overview of the entire AI supply chain, including source data, reference data, libraries, APIs, and the pipelines that power each model. It then performs a deep analysis of this supply chain to identify if encryption, logging, authentication, or authorization are misconfigured.
Compliance and Governance
As regulations around AI usage and customer data continue to expand, such as GDPR and NIST's Artificial Intelligence Risk Management Framework, AI-SPM will play a key role in helping organizations achieve compliance by enforcing policies, maintaining audit trails including tracking model lineage, approvals, and risk acceptance criteria, and linking human and machine identity to access to sensitive data and AI models.
Discovery and visibility
Lack of an AI inventory can result in shadow AI models, non-compliance issues, and data breaches due to AI applications. AI-SPM enables organizations to identify and manage the repository of all AI models used within their cloud setup, including the associated cloud resources, data origins, and data pathways used to train, optimize, and deploy these models.
Risk Response and Mitigation
When an urgent security event or policy violation is identified within your data or AI infrastructure, AI-SPM supports a rapid response process, providing insight into the situation and key players to quickly address and resolve the identified risk or misconfiguration.
footnote
Incorporating AISPM as a foundational element within an MLSecOps framework is a crucial step toward ensuring AI technologies are secure, compliant, and ethical. By adopting the AISPM methodology with the support of the Protect AI platform, organizations can confidently manage the complex issues associated with AI and ML technologies.
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