LONDON (IT BOLTWISE) – The integration of artificial intelligence into cybersecurity is fundamentally changing the way security professionals work. Instead of relying on traditional methods, professionals are learning how to use AI tools to increase their efficiency and avoid blind spots. This development requires new skills and a rethink in the industry.
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The story of Paul Bunyan, the legendary lumberjack who competed against a steam-powered saw and lost, is a metaphor for the challenges facing security professionals today. Artificial intelligence (AI) is our modern steam-powered saw. It is faster in many areas, unfamiliar in others and challenges many long-standing habits. The instinct to protect the known rather than explore the possibilities of the new tool could put us on the wrong side of a change already underway.
AI is now integrated into almost every security product we use. From endpoint protection platforms to mail filtering systems to SIEMs and vulnerability scanners, some form of “intelligent” decision-making is being promoted everywhere. The challenge is that most of this intelligence is hidden behind a curtain. Security professionals should therefore develop or adapt their own AI-powered workflows to compensate for blind spots and maintain control over the logic that shapes their environment.
A large part of security work is translational. Anyone who has written complex JQ filters, SQL queries, or regular expressions to extract information from logs knows how much time this translation step can take. AI can do a lot of this translation work. For example, I have developed small tools that use AI on the frontend side and a query language on the backend. Instead of writing the query myself, I can ask what I want in plain German and the AI will generate the correct syntax to extract it.
To use AI effectively, security professionals must develop new skills. Much of today’s AI work is done in Python, which has traditionally been a barrier for many security professionals. AI changes this dynamic. You can express your intent in plain German and the model generates most of the code. The model goes a long way, but closing the remaining gap requires judgment and technical competence. With this foundation, AI becomes a force multiplier, making it possible to develop targeted tools to analyze internal data and automate routine tasks.
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