"The AI-centric security product demo looked impressive. The vendor spoke confidently about autonomous detection, self-learning defences, and AI-driven remediation. Charts moved in real time, alerts resolved themselves, and threats seemed to vanish before human analysts even noticed them."
"One of the first realities CISOs must accept is that AI in cyber security isn't new. Machine learning (ML) has powered spam filters, anomaly detection, user behaviour analysis, and fraud systems for over a decade. What is new is the arrival of large language models (LLMs) and more accessible AI tooling that vendors are rapidly layering onto existing products. This shift has changed how security teams interact with data - summaries instead of raw logs, conversational interfaces instead of query languages, and automated recommendations instead of static dashboards."
"One lesson that keeps resurfacing is that architecture beats features. An AI bolted onto a weak security foundation won't save you. If identity is broken, data governance is unclear, or network visibility is fragmented, AI simply operates on bad inputs and produces unreliable outputs. CISOs also must understand that AI doesn't replace fundamentals, it amplifies them."
AI-driven demos can appear impressive, showcasing autonomous detection, self-learning defenses, and automated remediation. Machine learning has already powered many security systems for years, while large language models and accessible AI tooling are changing how teams interact with security data. These changes favor summaries, conversational interfaces, and automated recommendations over raw logs and manual queries. AI can add real value, but it can also create an illusion of intelligence when fundamentals are weak. Strong architecture, clear identity controls, solid data governance, and full network visibility are essential because AI amplifies existing capabilities rather than replacing them.
Read at ComputerWeekly.com
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