AI Security Posture Management: Safeguarding AI Systems in Cybersecurity

AI security posture management encapsulates a holistic strategy to safeguard the security and reliability of artificial intelligence and machine learning systems. This multifaceted approach encompasses ongoing surveillance, evaluation, and enhancement of the security stance concerning AI models, data, and infrastructure. Within AI-SPM lies the critical tasks of pinpointing and rectifying vulnerabilities, misconfigurations, and plausible threats linked to AI utilisation, alongside guaranteeing adherence to pertinent data privacy and security mandates. Within cybersecurity environments where artificial intelligence (AI) holds significant importance, AI security posture management (AI-SPM) emerges as a crucial element. The presence of AI systems, including machine learning models, large language models (LLMs), and automated decision systems, introduces distinct vulnerabilities and potential attack vectors. AI SPM tackles these challenges by offering tools for monitoring, evaluating, and mitigating the risks linked to AI elements within technological frameworks. Legislation oriented towards AI enforces stringent regulations concerning AI and customer data utilisation within AI applications, demanding enhanced governance capacities beyond the norm in most organisations. AI security posture management (AI-SPM) scrutinises the data origins utilised for training and establishing AI models to pinpoint and categorise sensitive or regulated data, including customers’ personally identifiable information (PII), that could potentially be disclosed through the results, records, or engagements of compromised models. AI-SPM consistently monitors user interactions, cues, and inputs to AI models (such as large language models) to uncover misuse, excessive prompts, unauthorised access attempts, or unusual activities related to the models. It reviews the outcomes and records of AI models to pinpoint possible cases of sensitive data exposure. AI-SPM empowers organisations to detect weaknesses and misconfigurations within the AI supply chain that could result in data breaches or unauthorised access to AI models and resources. This advanced technology meticulously outlines the entire AI supply chain, encompassing source data, reference data, libraries, APIs, and pipelines driving each model. Subsequently, it conducts an in-depth analysis of this supply chain to pinpoint any incorrect encryption, logging, authentication, or authorisation configurations. As regulations on AI utilisation and customer data, such as GDPR and NIST’s Artificial Intelligence Risk Management framework, continue to expand, AI-SPM plays a crucial role in assisting organisations in policy enforcement, audit trail upkeep, which involves tracking model lineage, approvals, and risk acceptance criteria, and in attaining compliance by linking human and machine identities with access to sensitive data or AI models. The absence of an AI inventory can result in shadow AI models, non-compliance issues, and data breaches facilitated by AI applications. AI-SPM enables organisations to identify and manage a repository of all AI models utilised within their cloud setups, including the relevant cloud resources, data origins, and data pathways utilised in training, optimising, or deploying these models. When urgent security events or policy breaches are identified within data or the AI infrastructure, AI-SPM supports quick response processes. It grants insight into the situation and key stakeholders involved in addressing and resolving the identified risks or misconfigurations promptly. Incorporating AISPM as a foundational element within the MLSecOps framework marks a pivotal move towards ensuring AI technologies’ secure, compliant, and ethical advancement. By embracing AISPM methodologies with the backing of the Protect AI platform, organisations can confidently manage the intricacies associated with AI and ML technologies.