Why 2026 May Herald the Democratisation of Destructive Power

Michael Freeman explores four trends set to shape the industry next year

The rapid evolution of AI isn’t simply rewriting the playbook. It’s erasing the gap between a lone operator and a well-funded, state-backed threat team. By 2026, the rise of autonomous hacking tools, AI-driven vulnerability discovery, and cheap, powerful computing will put destructive capabilities into far more hands than ever before. Sophisticated attacks will be within reach of anyone with motivation, a laptop and cloud access.

This shift demands more than incremental fixes. The defensive model itself needs a reset – to keep pace with autonomous, self-improving threats. As we move towards the new year, four developments are set to reshape what “good” cybersecurity looks like.

Agentic AI’s first autonomous breach

By mid-2026, at least one major global organisation will collapse due to a breach caused or significantly advanced by a fully autonomous agentic AI system. Unlike static generative models, agentic AI uses reinforcement learning (RL), multi-agent collaboration, and continuous feedback to plan, adapt and execute attacks without human supervision.

Now, a single operator can deploy a swarm of AI agents to map external attack surfaces, mutate malware in real time, pivot, escalate and retreat autonomously. Tasks that once required months of coordinated nation-state effort could be executed in days. Defending against such threats will require organisations to integrate AI-native platforms that automatically correlate signals across endpoints, identities, networks and cloud services.

Quantum risks outpace preparedness

By 2026, adversaries preparing for the quantum era will intensify efforts to exploit vulnerabilities in traditional public‑key encryption, as organisations lag in their multiyear transition to post‑quantum cryptography (PQC). This gap will increase exposure to “harvest‑now, decrypt‑later” (HNDL) tactics, in which encrypted data is collected today with the expectation that future cryptographically relevant quantum computers (CRQCs) will be able to crack the code.

“Sophisticated attacks will be within reach of anyone with motivation, a laptop and cloud access”

Organisations should therefore prioritise PQC for their most critical, long‑lived data, building on NIST’s 2024 standards – FIPS 203 (ML‑KEM, based on CRYSTALS‑Kyber), FIPS 204 (ML‑DSA, based on CRYSTALS‑Dilithium), and FIPS 205 (SLH‑DSA, based on SPHINCS+). They should also track the emerging HQC KEM, which NIST selected for standardisation in 2025.

For transitional protection, CISOs should adopt hybrid schemes that combine classical and post‑quantum algorithms, ensuring security even if one component is later weakened. Above all, they must design for crypto‑agility: the ability to rapidly swap cryptographic primitives and parameters as the quantum and AI threat landscape evolves.

Ransomware enters its autonomous era

Ransomware is evolving into a fully automated, self-optimising business model. Autonomous campaigns will self-discover targets, weaponise zero-day exploits, and orchestrate complex, multi-stage extortion without human oversight. AI-driven ransomware will adapt in real time – modifying encryption keys when backup processes are detected, embedding exfiltrated data within benign cloud traffic, and escalating to DDoS attacks if the victim delays payment. It will be a persistent, always-on extortion ecosystem.

To counter these campaigns, a resilient defence requires three critical pillars: immutable offline backups verified through automated restore testing; strict Zero Trust network access to limit lateral movement; and behavioural analytics to detect abnormal patterns in real time.

Critical infrastructure becomes the new front line

By 2026, over a third of global energy and utilities infrastructure will experience cyber pre-positioning activity – quiet access, reconnaissance and operational mapping. Attackers will exploit vendor ecosystems and supply-chain touchpoints to infiltrate operational technology (OT) environments, while minimising detection.

“Ransomware is evolving into a fully automated, self-optimising business model”

AI will help adversaries to map dependencies across IT, OT, IoT and cloud environments, optimising pathways to disrupt operations with maximum impact and minimal exposure. Stolen engineering diagrams, load models and configuration files will be used to build high-fidelity simulations of physical processes before executing targeted attacks.

To protect critical infrastructure against sophisticated adversaries, organisations will require strict network segmentation and monitoring across all operational zones. They’ll also need mandatory Software Bills of Materials (SBOMs) and integrity checks for all third-party code, as well as anomaly detection explicitly tuned for industrial environments.

The era of AI-enabled adversaries

AI has levelled the cybersecurity playing field. That means organisations will require AI-defender parity: security systems capable of identifying, analysing and responding at machine speed. Fragmented security will not suffice. CISOs need AI-native security architectures grounded in crypto-agility, continuous exposure management and intelligence-driven resilience to stay ahead. This is a world where attackers think and move at the speed of algorithms.