Artificial Intelligence
Design and deliver AI systems — document intelligence, LLM applications and decision support — that are secure and governed.
AI Security
Security for AI systems — from training data to production agents
Assess and secure machine-learning models, LLM applications and AI agents against adversarial and data-driven attacks.
The problem
Why this matters
AI systems introduce attack paths that traditional application testing does not cover: prompt injection, training-data poisoning, model extraction, sensitive-data leakage through outputs and agents that take actions with excessive permissions.
Common challenges
- LLM applications that trust untrusted content in prompts and retrieved documents
- Agents and tools granted broader permissions than their task requires
- Limited visibility into training-data provenance
- Emerging regulatory expectations for AI risk management
Our approach
How we work
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Inventory AI usage
Identify models, providers, data flows and the decisions or actions each system can influence.
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Threat-model the system
Apply the OWASP Top 10 for LLM Applications and MITRE ATLAS to the specific architecture.
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Test adversarially
Red-team prompts, retrieval pipelines, tool calls and output handling against realistic abuse.
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Govern and monitor
Implement guardrails, evaluation suites and monitoring aligned to the NIST AI RMF.
Capabilities
What our ai security work covers
- 01
LLM application testing
Direct and indirect prompt injection, jailbreak resistance, data exfiltration and insecure output handling.
- 02
AI agent security review
Tool permission scoping, human-in-the-loop controls and action authorisation.
- 03
ML pipeline security
Data provenance, training pipeline integrity, model registry and artefact signing.
- 04
Model robustness evaluation
Adversarial input testing and evasion analysis for classification and scoring models.
- 05
AI governance frameworks
Policies, model inventories and risk assessments mapped to NIST AI RMF and ISO/IEC 42001.
- 06
Guardrail engineering
Input and output filtering, retrieval access control and evaluation harnesses.
Engagement
Deliverables and benefits
What you receive
- AI system threat model
- Adversarial test report with reproducible cases
- Guardrail and architecture recommendations
- AI risk register and governance artefacts
What it changes
- AI features released with known, managed risk
- Defensible position for regulators and customers
- Reusable evaluation suites for future model changes
Standards & technology
FAQ
Frequently asked questions
Do you test third-party models such as hosted LLM APIs?
We test your application and how it uses the model — prompts, retrieval, tools, output handling and access control. Testing the provider’s underlying model itself is governed by that provider’s terms.
Is AI security relevant if we only use AI internally?
Yes. Internal assistants often have access to sensitive documents and systems, and indirect prompt injection through shared content is a realistic internal threat.
Discuss your ai security requirements
Let’s discuss it. Tell us what you are working on and an engineer — not a sales script — will respond.