Agentic AI is emerging as a double-edged sword in cybersecurity, automating both attack and defence strategies. Unlike traditional AI, agentic AI systems operate autonomously, making context-aware decisions and adapting to evolving threats in real-time. These systems can independently manage complex tasks, learn from past outcomes, and dynamically adjust strategies based on sensory input and external factors.
In cybersecurity, agentic AI enhances threat detection, proactively manages vulnerabilities, and automates responses, improving an organisation's security posture. It can identify subtle signals of advanced persistent threats (APTs) by correlating information across networks over time. However, this technology also presents risks, as malicious actors can leverage agentic AI to automate attacks, potentially overwhelming traditional defence mechanisms.
To mitigate these risks, organisations must adopt a dual approach: defending both with and against agentic AI. This includes implementing robust testing and runtime controls to ensure AI agents behave safely and predictably. As agentic AI redefines the cybersecurity landscape, enterprises must rethink their security strategies to address the challenges and opportunities presented by this technology.
