Founded in 2019, ShieldGrid AI has grown from a London-based research lab to a globally trusted AI cybersecurity platform — protecting over 500 enterprises across 60 countries.
ShieldGrid AI was founded by a team of ex-GCHQ analysts, CERN network security researchers, and Silicon Valley engineers who shared a single conviction: that traditional cybersecurity was fundamentally broken. Rule-based systems couldn't keep pace with AI-augmented adversaries.
We built NeuroShield™ from scratch — a deep learning engine that doesn't just react to known threats, but predicts and prevents unknown ones. Today, our platform protects banks, hospitals, government agencies, and Fortune 500 companies across 60+ countries.
Team of 12 ex-intelligence and ML researchers founded ShieldGrid AI in Canary Wharf, London.
Major funding round led by Sequoia and Lakestar. NeuroShield™ v1.0 launched commercially.
Achieved full certification across major compliance frameworks. Expanded to North America and APAC.
NeuroShield™ v9.4 deployed globally. Recognised as Gartner Magic Quadrant Leader for XDR.
Our culture is defined by three unwavering commitments that guide every decision — from how we build our product to how we serve our clients.
We never trade security for convenience. Every product decision is made with the assumption that adversaries are watching — because they are.
We believe clients deserve to understand exactly how our AI makes decisions. Our models are explainable, auditable, and never a black box.
We red-team everything we build. Our internal adversarial AI continuously tests our own defences — making us our own toughest critic.
Industry veterans from GCHQ, NSA, Palantir, Darktrace, and leading academic institutions in machine learning and network security.
Ex-GCHQ Technical Director. 18 years in national cyber intelligence. MSc Computer Science, Cambridge.
Former NSA cryptographer. Co-author of 12 published ML security research papers. PhD, MIT.
15 years at Palantir leading threat intelligence programmes. CISSP, CISM certified. MSc, Imperial College.
Previously at Darktrace and DeepMind. Expert in adversarial ML and neural network security architectures.
How ShieldGrid AI has defended some of the world's most targeted organisations from sophisticated cyber attacks.

A major European bank faced a sophisticated Lazarus Group intrusion targeting core banking APIs. ShieldGrid's NeuroShield™ engine detected the multi-stage attack at the reconnaissance phase and autonomously contained all 47 compromised endpoints before any data exfiltration.

LockBit 4.2 ransomware entered an NHS Trust network through a phishing email. ShieldGrid detected the polymorphic dropper at execution via behavioural AI, triggered isolation of the infected workstation, and prevented lateral movement — all before a single file was encrypted.

A coordinated DDoS attack targeting a major UK energy provider's operational control systems reached 3.4 Tbps — one of the largest attacks ever recorded in Europe. ShieldGrid's network layer detection and mitigation absorbed the attack with zero operational downtime.

A Series D fintech company needed SOC 2 Type II certification before a major enterprise deal. ShieldGrid's compliance automation mapped existing controls, identified 23 gaps, and automated remediation — achieving full certification in 6 weeks instead of the typical 9 months.
Whitepapers, threat reports, and technical guides authored by the ShieldGrid AI research team — available free for security professionals.
Annual threat landscape report covering emerging AI-augmented attack techniques, nation-state TTPs, and defensive AI advancements across 60+ countries.
A practical implementation guide for enterprise zero-trust architecture — covering network segmentation, identity verification, and continuous authentication across hybrid environments.
Step-by-step incident response playbook for ransomware attacks — covering detection, containment, recovery, and post-incident hardening for security operations teams.
Technical research paper examining how threat actors are weaponising ML to evade detection systems — and how defensive AI models can be trained to recognise adversarial inputs.
Practical 50-point checklist for CISOs evaluating cloud security posture — covering IAM, network security, data encryption, workload protection, and compliance mapping.
On-demand webinar: how threat actors are targeting AI model training pipelines, LLM APIs, and MLOps infrastructure — with live demonstrations of ShieldGrid's AI supply chain protection module.
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