AI Security
& Consulting
Everyone is racing to ship AI features and adopt large language models, and almost nobody is asking how they break. We assess the security of your AI and LLM-powered applications, review how you consume third-party AI services, and advise on safe adoption: prompt injection, data leakage, model and supply-chain risk, and the governance regulators are starting to expect.
- Prompt injection, jailbreak and abuse testing of LLM features
- Security review of AI and LLM-powered applications and APIs
- Data leakage and sensitive-data exposure assessment
- AI supply-chain, model provenance and plugin risk review
- Secure AI adoption policy, guardrails and governance
- Practical staff guidance on safe use of AI tools
Common Questions
What is prompt injection?
Attacker-supplied input that hijacks what your AI application does - the LLM equivalent of SQL injection, and the top entry in the OWASP Top 10 for LLM Applications. If your product accepts user input near an LLM, it needs testing for this.
We only use AI tools, we don't build them - is there still a risk?
Yes - mostly data leakage. Staff pasting client data, credentials, or source code into public AI tools is this decade's shadow IT. Policy, guardrails, and sanctioned alternatives fix it without banning the productivity.
Are there recognised frameworks for AI security?
Yes - the NIST AI RMF, ISO/IEC 42001 for AI management systems, and MITRE ATLAS for adversarial threat techniques. Our assessments and governance work map to all three.