The global cost of cybercrime reaches $10.5 trillion in 2025 — if measured as a nation, it would rank third behind only America and China. Every two seconds, another ransomware attack strikes while your security teams scramble to respond.

Machine learning changes the game entirely. Where humans see noise, AI detects subtle patterns across 750 billion daily security events that reveal attacks in progress.

The AI advantage in threat detection

Zero-day exploits bypass signature-based defences because they’ve never been seen before. AI watches for behavioural anomalies instead — unusual data flows, strange access patterns, or unexpected system calls that betray malicious activity.

Gartner’s December 2024 research shows AI cuts false positives by 30%. Fewer meaningless alerts means your analysts investigate real threats instead of chasing ghosts.

From reactive to predictive security

Dark web forums buzz with chatter about your industry. Geopolitical tensions spike in regions where you operate, and threat actors share new exploit techniques on underground channels.

AI correlates these external signals with your internal vulnerabilities to predict likely attack vectors. Secureworks’ Taegis platform demonstrates this capability, analysing 51 petabytes of security data to anticipate threats before they materialise.

The human-AI partnership

Your best analysts bring intuition and strategic thinking that no algorithm can replicate. AI handles the grunt work — sifting through logs, correlating alerts, and drafting initial incident reports.

Secureworks found this division of labour cuts detection time by over 50%, meaning those threats that once lurked for hours get spotted in minutes and limit potential damage to your organisation.

Measuring AI’s impact

The Ponemon Institute tracked breach costs hitting $4.88 million per incident last year. Preventing just one attack pays for your entire AI implementation several times over.

Operational gains multiply these savings. Secureworks’ security teams experienced a 50% workload reduction after deploying their AI-powered Prioritization Engine.

Building your AI in AI in cybersecurity strategy

Define specific problems AI should solve first. Perhaps you need faster threat detection, automated incident response, or better vulnerability prioritisation — pick one and prove value before expanding.

Select platforms combining multiple AI approaches. Secureworks Taegis uses both generative AI for natural language explanations and traditional machine learning for pattern recognition.

The road ahead

Criminals already weaponise AI for phishing and polymorphic malware, so standing still means falling behind in an accelerating arms race where algorithms battle algorithms.

Tomorrow’s breaches won’t wait for committee approvals or budget cycles. The organisations thriving in 2030 will be those that have embraced AI in cybersecurity transformation today