Independent AI security and assurance
Trust requires evidence.
We attack, evaluate and measure AI systems so organizations can deploy them with evidence, not assumptions.
Assurance record
System evaluation / 047
18
Attack paths
246
Test cases
31
Controls
92
Evidence items
Attack
Find how systems can be manipulated or abused.
Evaluate
Measure behaviour under real-world pressure.
Measure
Turn system performance into defensible evidence.
Assure
Support confident deployment and operation.
What organizations need to know
Can this AI system actually be trusted?
Can it be manipulated?
Does it expose sensitive data?
Will it behave reliably?
Can it act beyond its authority?
Are its claims evidence-based?
What changes after release?
Engagements
Evidence you can act on.
AI Security Assessment
A technical assessment of AI applications, RAG systems, APIs and agentic architectures.
AI Red Team
Adversarial testing that exposes prompt injection, tool abuse, data exfiltration and system-specific attack paths.
AI Assurance Assessment
Independent evaluation of reliability, robustness, factuality, safety and behavioural consistency.
Continuous Assurance
Repeatable evaluation as models, prompts, data, tools and releases change.
AI systems, not isolated models
The real attack surface is the system around the model.
Vigilantia Research
Research that strengthens practice.
We turn field experience into evaluation methods, adversarial techniques, assurance frameworks, benchmarks and technical tools.
Explore our research direction