1. AI-driven phishing becomes indistinguishable from humans
LLMs enable mass-produced, highly personalised phishing emails that accurately mimic tone, context, and writing style. Traditional “spot the bad grammar” tactics stop working.

2. Deepfakes undermine trust at scale
AI-generated voices and video will be used to impersonate executives, staff, and suppliers—authorising payments, issuing instructions, or spreading false information. Verification, not familiarity, becomes critical.

3. Autonomous malware adapts in real time
Malware increasingly uses AI to mutate code, evade detection, and adapt mid-attack. Signature-based antivirus can’t keep up; behaviour-based and adaptive defences become mandatory.

4. Prompt injection and AI-targeted attacks
Attackers will actively target AI systems—tricking agents into leaking data, making unsafe decisions, or executing harmful actions. Securing AI becomes as important as securing identities and endpoints.

5. AI lowers the barrier to entry for cybercrime
With LLMs, attackers no longer need deep technical skill. Phishing, malware, and social engineering can be launched faster, cheaper, and at scale by almost anyone.

6. AI-powered threat detection becomes essential
Security platforms must use machine learning to detect anomalies, correlate weak signals, and identify attacks early. Organisations of all sizes will need AI-driven detection to counter AI-driven threats.

7. AI transforms defensive testing
Autonomous agents can simulate realistic AI-powered attacks, allowing organisations to test defences continuously—not just during annual penetration tests.

8. Autonomous SOCs become the norm
AI agents handle first-line security: alert triage, correlation, and initial containment. Human teams move up the stack, managing response playbooks instead of chasing alerts.

9. LLM assistants amplify security teams
AI assistants draft incident reports, analyse logs, and support faster decision-making. Small teams can operate with the speed and effectiveness of much larger ones.

10. Zero Trust evolves under AI pressure
Trust models must expand beyond users and devices. Every identity, message, and system interaction is verified by default, using behavioural signals, provenance checks, and out-of-band confirmation.