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GenAI in Cybersecurity: Great Opportunity, New Risks and the Operational Roadmap

Published:

30/09/2025
GenAI in Cybersecurity: Great Opportunity, New Risks and the Operational Roadmap

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    Why is GenAI prominent in Cybersecurity?

    Cybersecurity is one of the most important early applications of GenAI: 72% of pioneering organizations have already implemented or are experimenting with GenAI for this purpose (Omdia's Early Adopters survey). This figure is reconfirmed by industry sources for 2024-2025.

    These are the early and fast-tracked use cases being deployed by businesses, as aggregated by Canalys.

     

    Specific Risks & Challenges of GenAI in Security

    • Reliability & output bias: depend on training data and usage context; a standardized AI risk governance framework is needed to "design for trust" from the start.

    • New threats to the model itself: prompt injection, insecure output handling, training data poisoning, DoS against the model, supply chain, etc.

    • Warning of real-world attack trends: the UK's National Cyber Security Centre (NCSC) forecasts that AI will make phishing/scams more sophisticated, increasing the volume and impact of attacks in the next 1-2 years.

    • General threat landscape: ransomware, attacks on availability, and supply chain attacks remain the top threats according to the ENISA Threat Landscape 2023-2024, forming the foundation for integrating GenAI into a multilayered defense strategy.

     

    Roadmap for Safely "Putting GenAI into Production"

    1. Identify use cases with impact & sufficiently clean data: Start with SOC assistant, incident summarization, IOC enrichment.

    2. Protect data & privacy: Data partitioning, encryption, access control based on the Principle of Least Privilege (PoLP); do not send sensitive data externally without a Data Processing Agreement (DPA).

    3. Continuous monitoring & auditing: Observe drift/output quality, log prompt/tool usage, periodically assess bias/accuracy.

    4. Train the SOC workforce: Skills in prompt read-back, output triage, AI-enhanced attack simulation; update Incident Response (IR) playbooks.

    5. Prepare for AI-upgraded attack scenarios: Multi-channel phishing, deepfake voice/video, social engineering at scale.

    6. Measure ROI & scale in phases: Use metrics like MTTR (Mean Time To Respond), investigation time, volume of alerts processed, classification accuracy, etc., as criteria for deciding budget expansion.

     

    Conclusion

    GenAI is not just a supporting technology, but a strategic leverage point for the SOC and enterprise defense capabilities over the next 5-10 years. However, to overcome the "POC to production gap", organizations need a systematic risk governance framework, technical controls, continuous operation, parallel, targeted investment and human training.

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