AI on both sides of the firewall.
We use artificial intelligence to do security work faster and more thoroughly. We also secure the artificial intelligence you are deploying — because every model you wire into production is new attack surface.
Part one: AI applied to security
Automation is not a substitute for an operator. It is what lets a small team cover ground that would otherwise need a large one.
Continuous attack-surface mapping
Agents enumerate hosts, services, certificates and exposed endpoints, then track drift over time. An assessment starts from a live picture rather than a stale inventory.
Model-assisted code & config review
Language models read source, infrastructure definitions and configuration at a volume humans cannot, flagging candidate flaws for an analyst to confirm and exploit. Model output is a lead, never a finding.
Adaptive fuzzing & payload generation
Test inputs mutate in response to application behaviour, reaching states that a fixed wordlist will never produce.
Anomaly detection over telemetry
Behavioural baselines and unsupervised detection across authentication, network and application events — finding the unusual without a signature for it.
Alert enrichment & triage
Automated correlation, context gathering and first-pass classification so human attention concentrates where risk is real.
Remediation assistance
Proposed fixes, patch drafts and regression tests generated alongside the finding, to shorten the distance between report and resolution.
Part two: securing AI systems
Conventional penetration testing does not cover a model that can be talked into ignoring its instructions, or an agent that can be persuaded to use its tools against you. We test those specifically.
Prompt injection
Direct injection from user input and indirect injection through retrieved documents, web content, emails or tool results — the pathway most production LLM incidents actually take.
Agent & tool exploitation
Where a model can call tools, read files or execute code, we test whether those permissions can be turned against their owner through crafted instructions.
Guardrail & filter bypass
System-prompt extraction, safety-filter evasion, encoding and multi-turn techniques that defeat shallow input screening.
Data leakage & memorisation
Whether sensitive context, retrieval corpora or training material can be extracted through crafted queries or inference.
Retrieval pipeline integrity
Poisoning and trust-boundary failures in vector stores and retrieval layers, where untrusted content is silently promoted into model context.
Model supply chain
Provenance and integrity of models, adapters, weights and hosting dependencies pulled from third parties.
Where this matters most
AI is being added to systems faster than it is being secured. These are the areas where that gap creates real exposure.
Customer-facing assistants
Chatbots and support agents with access to internal knowledge or customer records are a direct path to data disclosure.
Autonomous agents with tools
Anything that can send email, move money, modify records or run code inherits the consequences of a successful injection.
Internal knowledge retrieval
Systems summarising documents, tickets and mail merge trusted and untrusted content into the same prompt.
Automated decisioning
Where model output drives approval, pricing, access or eligibility, integrity of that output is a security property.
Deploying AI into a real system?
Get it tested before it is trusted. We will scope what your AI components can reach and how they can be abused.