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In an era of rapid technological change, cyber risk remains one of the foremost concerns for organisations. Traditional point-in-time security assessments—such as annual penetration tests or quarterly vulnerability scans—fail to keep pace with the dynamic threat landscape, leaving enterprises exposed to novel attack vectors. Continuous Threat and Exposure Management (CTEM) has emerged as a holistic framework that consolidates multiple security disciplines into an ongoing lifecycle, enabling organisations to detect, prioritise and remediate risks in real time.
Artificial Intelligence has transformed from a theoretical construct into a practical powerhouse driving real-world applications across sectors. At the forefront of this revolution lies a new taxonomy of intelligent systems: AI Agents, Agentic AI, and the emergent concept of Agentic Retrieval-Augmented Generation (Agentic RAG). For data scientists—tasked with building intelligent systems, driving innovation, and ensuring scalable impact—it is crucial to differentiate and understand these evolving paradigms.
To explore how Agentic Retrieval-Augmented Generation (RAG) revolutionises vulnerability assessment and management through autonomous decision-making, context-aware retrieval, and intelligent automation — with a strong focus on ROI, business impact, and proactive risk mitigation.
Continuous Threat Exposure Management (CTEM) is not a tool or platform but a strategic programme that integrates threat intelligence, attack surface management, vulnerability prioritisation, and validation techniques like red teaming or breach simulation.
Unlike traditional AI, agentic systems have goal-oriented autonomy, situational awareness, and collaborative decision-making traits. Think of them as proactive digital analysts embedded in your CTEM loop.