PREDICTIVE intelligence
AI will enable bad actors to do what they have always done , but faster .
stronger forms of multi-factor authentication and privileged access management . These measures can help mitigate risks associated with social engineering and wire fraud , which are likely to increase as attackers utilise AI for more sophisticated tactics .
Prediction 3 : In the next five years , AI-driven cybersecurity will enhance operational efficiency for defenders , but the human element will remain crucial in interpreting data and making decisions
Over the next five years , we can expect significant improvements in operational and capital efficiency for defenders , as AI continues to automate routine tasks and streamline processes . This will free security practitioners to focus on more complex challenges , particularly those involving ‘ irreducible uncertainty ’ – situations where the risk cannot be fully understood through empirical data .
As the deterministic aspects of cybersecurity are automated , the role of experts will increasingly shift toward decision-making in uncertain scenarios . AI will aid in modeling these risks , but the effectiveness of these models will heavily depend on the expertise and assumptions of the security professionals using them . This means that while AI will enhance analytical capabilities , the human element will remain critical in interpreting data and making informed choices among plausible alternatives . Security professionals will continue to play a vital role in navigating complexities and uncertainties , underscoring the importance of their expertise in the evolving landscape of AI-driven cybersecurity .
Prediction 4 : Automation and orchestration will grow in importance in 2025 to centralise risk telemetry across cloud , endpoints and IoT devices
Landing all your risk telemetry into one place will become common . Many organisations are already aggregating IT , OT and cloud-native risk data into security data lakes , including asset state and changes over time , along with threat and vulnerability intelligence . Note that telemetry consumption is not the same as risk measurement . At a minimum , assets must be normalised , and scores must be rationalised . From there , automation will enable organisations to measure operational efficiency in controlling attack surfaces and implement ‘ policy-as-code ’ using AI copilots . AIdriven tools will drive down risk in both a capital and operationally efficient manner .
Prediction 5 : Cyber-risk quantification ( CRQ ) will be a core organisational practice for most CISOs in the next five years
Measuring risk is a core capability , not a product . As cybersecurity maturity grows , the integration of financial metrics with technical security data will become critical . The industry calls this ‘ cyber-risk quantification ’ ( CRQ ), but I call it cybersecurity risk management . You can ’ t extract quantitative measurement from the broader domain of
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