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AI Security
Security engineering and technical assessment for AI-enabled applications, LLM systems, intelligent agents and emerging AI architectures.
Understand the system
Model behaviour is only part of the picture. We start from the architecture: what the model can reach, and on whose authority.
Map the attack surface
Prompts, retrieval sources, tool interfaces, plugins, memory, identity and downstream effects are all entry points.
Engineer the controls
Recommendations target design and implementation — isolation, authorisation, validation and observability — not policy language.
The problem
An AI system's attack surface is not the model.
Most AI security incidents are architectural. A model is given tools, credentials, retrieval access and an audience, and the interesting failures happen at those boundaries rather than inside the weights. Navrysa reviews AI systems the way it reviews any other distributed system with an untrusted input path.
- Interface
- Who and what can reach the model, how input is constructed, and what an attacker controls in the prompt they never see.
- Model
- Behaviour under adversarial input, refusal boundaries, output handling, and what downstream code trusts about the response.
- Orchestration & tools
- Tool and function interfaces, agent loops, action authorisation, sandboxing, and the blast radius of a single bad decision.
- Data & retrieval
- Retrieval sources and their trust level, indirect injection paths, data segregation, and what enters context from where.
- Identity & infrastructure
- The credentials and permissions an AI component operates with, secret handling, isolation and logging.
How we work
Assessment, design review, and adversarial testing.
Engagements range from a focused review of one AI feature to sustained security engineering alongside a product team.
Assessment
AI application & LLM security review
Examine an AI-enabled application end to end: input paths, context construction, output handling, and the code that acts on model responses.
Agents
Agentic threat analysis
Analyse autonomous and semi-autonomous agents: tool authorisation, loop control, privilege boundaries, memory poisoning and cascading actions.
Architecture
Secure AI system design
Work with your engineers on architecture — isolation, mediation, least privilege, validation and the separation of generation from authorisation.
Testing
Adversarial evaluation
Structured adversarial testing against a defined threat model, including prompt injection, indirect injection through retrieved content, and tool misuse.
Navrysa's AI security work is engineering-led. We assess and design systems; we do not issue certifications or compliance attestations.
AI Security
Have an AI system going to production?
A structured review before launch is cheaper than an architecture change afterwards. Tell us what the system does and what it can reach.