AI Continuous Assurance Platform
Assurance Intelligence at Machine Speed
Your AI systems and agents face new risks every day — vulnerabilities published, models degrading in new contexts, supply chains compromised. Without continuous intelligence, your testing is always one step behind.
Testing Without Context
Even the best testing platforms can only answer the questions you think to ask. AICAP continuously monitors the landscape around your AI systems — so you see the risks emerging, know which tests to run, and act before failures happen.
An LLM-powered analysis system is redeployed from one operating region to another. It passes all standard tests.
Six weeks later, analysts discover:
A known vulnerability in the foundation model was published three months prior
The model's training data had significant gaps in content relevant to the new operating region
A recent benchmark showed degraded performance on the relevant language pair
An autonomous logistics agent chains three models, accesses procurement APIs, and makes supply decisions. It passed functional testing.
Nobody assessed:
Whether a prompt injection via a compromised supplier catalogue could hijack the agent's tool calls
That the open-weight base model had a known jailbreak allowing privilege escalation through chained tool use
How the three models' individual vulnerabilities compound when orchestrated in an agentic pipeline
Your AI platform depends on LiteLLM, a popular LLM proxy used by agent frameworks and orchestration tools. A routine pip install pulls the latest version.
March 2026 — a supply chain attack:
Malicious PyPI packages steal environment variables, SSH keys, and cloud provider credentials
Data encrypted and exfiltrated to attacker-controlled servers — Kubernetes tokens, database passwords, AWS keys
Organisations with unpinned transitive dependencies exposed without ever directly installing the compromised package
From Threat to Test in Minutes
AICAP runs a continuous intelligence cycle that transforms raw threat data into prioritised, actionable test requirements — automatically. No more waiting months for manual horizon scanning to surface what matters.
Monitor
Know within minutes when something changes that affects your AI systems. Continuous ingestion from CVE feeds, adversarial research, model registries, benchmark updates, training data disclosures, agent framework advisories, and AI code generation vulnerability patterns.
Correlate
Instantly understand which systems are affected. Trace vulnerabilities, benchmark changes, and provenance updates through model families, fine-tuning chains, agent tool dependencies, and multi-model pipelines.
Prioritise
Focus on what matters most. Multi-factor risk scoring weighs exploitability, context impact, agent autonomy level, and remediation complexity.
Generate
Get test-ready in minutes. LLM-powered synthesis produces test specifications, adversarial scenarios, and red team targeting packages.
Evaluate
Understand results, not just pass/fail. Automated analysis flags gaps and generates audit-ready reports aligned to multiple regulatory frameworks.
Feedback
Every cycle makes the next one better. Results refine future prioritisation, improving the intelligence that drives your assurance posture.
Built for the People Who Need It Most
Testing & Evaluation Teams
Know what to test before it's too late
Stop relying on static test plans. Get continuously updated test requirements for AI systems and agents, driven by real-world threat intelligence.
Programme Leads
Continuous assurance, not periodic reviews
Shift from point-in-time testing to an always-on assurance posture that keeps pace with evolving AI threats.
Safety Officers
Complete audit trails from threat to evidence
Every test requirement traces back to source intelligence. Demonstrable due diligence for safety authorities and domain regulators.
Intelligence Analysts
Track emerging AI risks through the same pipeline
The same intelligence cycle that assures your systems can map emerging attack techniques, vulnerability exploitation patterns, and supply chain risks across the AI ecosystem.
The AICAP Difference
Proven in Defence. Built for Every Regulated Industry.
The same continuous intelligence architecture that protects Defence AI systems extends to any domain where AI assurance is critical.
Agentic AI
Autonomous agents chain multiple models, tools, and APIs — multiplying the assurance surface. AICAP is designed to trace vulnerabilities through entire agent pipelines, from base models to tool-use permissions.
AI & Software Supply Chain
From open-weight model provenance to AI-generated code, AICAP will monitor the full AI supply chain. Phantom dependencies, vulnerable code patterns from AI assistants, and package compromises — tracked through fine-tuning chains and model registries.
Red Teaming
AICAP won't just find vulnerabilities — it will generate targeting packages for your red teams. Know exactly where to probe, with adversarial scenarios tailored to your deployed systems.
Regulatory Readiness
EU AI Act, UK AI Safety Institute guidance, SSCoP, Cyber Security and Resilience Bill — the regulatory wave is coming. AICAP is designed to map assurance evidence across frameworks automatically.
Critical Infrastructure
Healthcare, energy, and transport systems increasingly rely on AI. Continuous assurance isn't optional when the consequences of failure are measured in lives.
Open-Weight Models
Sovereign AI means self-hosted open-weight models. No vendor patches, community-discovered vulnerabilities, unknown training provenance. AICAP is designed to monitor what the vendor can't.
Ready to Close the Assurance Gap?
AICAP doesn't replace your existing test infrastructure — it provides the intelligence layer that tells it what to test next.