AI Creates a New Attack Surface
Traditional cybersecurity wasn't built for probabilistic systems. LLMs can't distinguish trusted instructions from malicious input — and your employees are already using AI tools you don't know about.
Average cost of a breach involving Shadow AI
Of organisations view Shadow AI as a major risk
Of audio is enough to clone someone's voice
Prompt Injection remains the top LLM risk (OWASP)
End-to-End AI Security Services
From red teaming your LLMs to governing your AI lifecycle, we cover both pillars of modern AI security: Security for AI and AI for Security.
AI/LLM Penetration Testing & Red Teaming
Adversarial testing of your LLM-powered applications — prompt injection, jailbreaking, data extraction, agentic misuse, and multi-step attack chains. We go beyond automated benchmarks with expert human-led creative testing.
AI Governance & Compliance
Navigate the EU AI Act, ISO/IEC 42001, and other emerging AI regulations with confidence. We help you classify AI systems by risk tier, build your AI Management System (AIMS), and prepare for conformity assessments.
Shadow AI Discovery & Control
Identify unauthorized AI tool usage across your organisation. We map the hidden AI landscape — browser extensions, consumer chatbots, unsanctioned APIs — and implement governed enablement so productivity isn't sacrificed for security.
AI Supply Chain Security
Protect against poisoned models, malicious serialization exploits, and compromised training data. We assess the provenance and integrity of your AI dependencies from open-source models to proprietary vendor APIs.
Deepfake & AI Social Engineering Defense
Combat voice cloning, video deepfakes, and AI-generated phishing. We deploy detection tools, design out-of-band verification protocols, and train your teams to resist AI-powered social engineering — the fastest-growing attack vector.
AI-Powered SOC & Threat Detection
Supercharge your Security Operations Center with agentic AI that autonomously triages alerts, investigates incidents at L2 depth, and executes response actions — reducing MTTR by up to 90% and eliminating up to 99% of alert noise.
The 10 Most Critical AI Security Risks
Our testing methodology covers every risk in the OWASP GenAI Top 10 — the industry standard for LLM application security.
Prompt Injection
Sensitive Information Disclosure
Excessive Agency
Supply Chain Vulnerabilities
Data & Model Poisoning
Improper Output Handling
Hidden Context Exposure
Vector & Embedding Weaknesses
Misinformation & Hallucinations
Unbounded Consumption
Our AI Security Assessment Process
A structured, repeatable process aligned to OWASP, MITRE ATLAS, and NIST AI RMF that ensures thorough coverage and actionable results.
AI Asset Discovery & Threat Modelling
We map your AI landscape — models, agents, RAG pipelines, integrations, and data flows — to identify the complete attack surface and define the engagement scope.
Risk Classification & Gap Assessment
Classify AI systems against EU AI Act risk tiers and NIST AI RMF. Assess current controls against OWASP GenAI Top 10 and MITRE ATLAS to identify gaps.
Adversarial Testing & Red Teaming
Combined automated and human-led adversarial testing — prompt injection, jailbreaking, agentic exploitation, data extraction, and multi-step attack chain simulations.
Governance Framework Implementation
Build your AI Management System: policies, risk registers, data governance controls, human oversight mechanisms, and compliance documentation.
Continuous Monitoring & Improvement
Deploy guardrails, monitoring, and anomaly detection. Establish feedback loops, re-testing cadences, and update processes as the AI threat landscape evolves.
Aligned to Industry Standards
Our AI security methodology is built on the authoritative frameworks that define best practice for AI risk management worldwide.
OWASP GenAI Top 10
Industry-standard risk catalogue for LLM and generative AI applications
MITRE ATLAS
Adversary tactics, techniques, and case studies targeting AI systems
NIST AI RMF
Govern, Map, Measure, and Manage AI risks across the lifecycle
EU AI Act
Mandatory risk-based regulation for AI systems in the European market
ISO/IEC 42001
International standard for AI Management Systems (AIMS)
Traditional Pentesting Doesn't Cover AI
Standard penetration testing is designed for deterministic software where inputs produce predictable outputs. LLMs are probabilistic — they may behave differently in identical contexts. AI-specific testing must account for risks that scanners simply cannot detect.
Behavioural, Not Just Technical
AI red teaming tests model judgment, safety guardrails, and non-deterministic response patterns — not just API vulnerabilities.
Human + Automated Testing
Automated fuzzing finds broad patterns. Human testers find the creative exploits, jailbreaks, and unknown unknowns that benchmarks miss.
Agentic Risk Assessment
When your AI can call APIs, send emails, or execute code, a prompt injection isn't just an output problem — it's a potential RCE vector.
Context Poisoning via RAG
Attackers can manipulate the data your AI retrieves — emails, documents, web pages — to influence its decisions without touching the model.