AI-Native
Cybersecurity
We use AI to secure your systems, and we secure the AI systems you deploy.
NPU Labs uses privately governed, restricted-access AI models chosen for cybersecurity work, rather than general-purpose public models.
Our cybersecurity stack combines:
- Privately governed and restricted-access cybersecurity models
- Custom-trained models for specialised detection and classification tasks
- Retrieval-augmented generation (RAG) grounded in approved security knowledge, client context, policies, architecture, findings, and evidence
- AI agents for controlled analysis, testing, validation, and remediation workflows
- Deterministic security tooling and conventional cybersecurity controls
- Human oversight and approval where required
We use different specialised models and retrieval sources at different phases of the security lifecycle. The model for threat analysis need not be the one for secure code review, attack-path reasoning, remediation validation, or regression testing.
Custom-trained models and RAG work alongside traditional security tooling. They add specialised context, repeatability, domain knowledge, and reasoning to the evidence from scanners, source analysis, logs, runtime telemetry, tests, and authorised offensive security exercises.
NPU Labs combines cybersecurity engineering, AI agents, model evaluation, red teaming, blue teaming, and production monitoring to protect both traditional software environments and AI systems. We work across applications, infrastructure, cloud, models, agents, RAG, memory, Model Context Protocol (MCP), and autonomous workflows.
Security services
Application security
Find and fix weaknesses in APIs, applications, agent services, and internal platforms.
Capabilities include:
- Secure code review
- API security
- Authentication and authorisation review
- Dependency analysis
- Secret detection
- Vulnerability discovery
- Remediation validation
- Security regression testing
Secure development lifecycle
Security is built into delivery rather than checked at the end.
NPU Labs embeds security into the software lifecycle through:
- Threat modelling
- Secure coding rules
- Static application security testing (SAST)
- Dependency checks
- Secret detection
- Security-focused code review
- Release quality gates
- Continuous validation
Vulnerability management
We replace periodic scanning with a continuous security lifecycle.
We help teams:
- Discover vulnerabilities
- Classify and prioritise risk
- Analyse attack paths
- Remediate
- Retest
- Convert findings into regression tests
- Monitor for recurrence
AI-assisted red teaming
Authorised offensive testing of:
- Applications
- APIs
- Cloud environments
- Infrastructure
- Networks
- Containers
- Authentication boundaries
- CI/CD systems
- MCP-connected services
We find weaknesses and test whether each one can be exploited in the real system.
AI-assisted blue teaming
Defensive security engineering across:
- Detection engineering
- Threat hunting
- Hardening
- Security monitoring
- Incident triage
- Containment
- Remediation
- Recovery validation
AI can speed up the analysis, while deterministic controls and human accountability stay in place.
Purple teaming
Red-team findings improve blue-team defences.
NPU Labs turns offensive findings into:
- Detection rules
- New controls
- Regression scenarios
- Monitoring requirements
- Guardrail updates
- Security tests
The result is a loop of continuous improvement, in place of a one-off penetration test.
AI system security
Agent security
AI agents can call tools, access systems, make decisions, and perform real actions.
We test agents for:
- Prompt injection
- Tool misuse
- Excessive permissions
- Unsafe autonomy
- Cross-agent attacks
- Goal manipulation
- Unauthorised actions
- Sensitive-data exposure
MCP security
MCP connects AI reasoning to real system capabilities.
NPU Labs evaluates:
- MCP server authentication
- Authorisation
- Tool-level permissions
- Server trust
- Input validation
- Output validation
- Scoped credentials
- Tool allow-lists
- Auditability
- Tool-call behaviour
Model security
We test models for:
- Jailbreaks
- Policy bypass
- Adversarial inputs
- Model extraction
- Unsafe behaviour
- Poisoning
- Evaluation manipulation
RAG security
Retrieval systems can introduce untrusted content directly into model context.
We test for:
- Retrieval poisoning
- Malicious documents
- Embedded prompt injection
- Sensitive-data disclosure
- Unauthorised source access
- Provenance failures
Memory security
Persistent memory creates a new long-lived attack surface.
NPU Labs evaluates:
- Memory poisoning
- Cross-user leakage
- Source provenance
- Trust scoring
- Retention controls
- Unauthorised retrieval
- Tenant isolation
- Audit history
Evaluation suites
NPU Labs turns security and safety requirements into repeatable evaluation suites covering:
The suites let us detect security failures before release and keep them from returning.
See AI testing and evaluation- Golden scenarios
- Security regression tests
- Multi-turn agent testing
- LLM-as-judge evaluation
- Deterministic policy checks
- Tool-use validation
- RAG evaluation
- Human-handover testing
- Release quality gates
Environments
NPU Labs security services integrate with private, self-hosted, hybrid, and cloud AI environments.
We work across:
- Private GPU infrastructure
- NVIDIA DGX Spark environments
- AWS
- Azure
- Containers
- Local model serving
- RAG
- MCP
- AI agents
- Voice systems
- Enterprise APIs
We design security controls around your infrastructure, data residency, privacy, and governance requirements.
Scope and deliverables
In scope
- Applications
- APIs
- Infrastructure
- Cloud
- Networks
- Containers
- Models
- Agents
- MCP servers
- RAG
- Memory
- Workflows
- Guardrails
- Human escalation
Deliverables
- Security findings
- Risk classification
- Reproduction evidence
- Attack-path analysis
- Remediation recommendations
- Retest results
- Regression scenarios
- Detection recommendations
- Governance evidence
- Release-readiness evidence
Talk to us about security testing
NPU Labs helps organisations design, test, secure, and operate production AI systems. If you are deploying private models, AI agents, RAG, MCP, voice systems, or autonomous workflows, design security into the architecture from the start.