We protect AI models against manipulation, data poisoning, and prompt injection; we detect Shadow AI usage and establish a safe AI consumption policy.
The critical topics this service addresses and the outcome we deliver in each.
Within the OWASP ML Top 10 framework we test model resilience with prompt injection, jailbreak and data poisoning tests and report findings with remediation.
With network traffic analysis and SaaS audit we discover unapproved AI use and produce a risk map.
With an approved-tool list, data classification rules and DLP integration we build controls that prevent corporate data from leaking into AI platforms.
Through an AI usage policy and role definitions we provide a quickly deployable governance structure aligned with the EU AI Act and NIST AI RMF.
How we phase the service across delivery, governance, and connected service pillars.
Model testing: with OWASP ML Top 10 and adversarial ML techniques we test resilience to prompt injection, jailbreak and data poisoning.
Shadow AI discovery: with network traffic analysis and SaaS audit we surface unapproved AI use and build a risk classification matrix.
Policy and governance: with an approved-tool list, data classification rules and DLP integration we design a secure AI consumption policy.
Illustrative surfaces where this service is commonly activated.
Organisations wanting to control the use of ChatGPT-like tools with a secure usage policy instead of blocking it.
Teams wanting to test the LLM and ML models they train for adversarial robustness and data poisoning resilience.
Organisations wanting to clarify risk classification and compliance requirements under regulation such as the EU AI Act.
This service's depth on sector-specific technical and compliance topics.
With adversarial ML, prompt injection and data poisoning tests we assess LLM, vision model and ML pipeline security against OWASP ML Top 10 and NIST AI RMF.
With network traffic analysis and SaaS audit we discover unapproved AI use and design a corporate AI usage policy and governance framework.
We clarify compliance requirements through EU AI Act risk classification; legal rulings are left to legal counsel.
The rapid enterprise adoption of generative AI and large language models introduces a category of security risk that traditional application security programmes were not designed to address. Prompt injection, model inversion, training data poisoning, and insecure AI output handling create novel attack surfaces that attackers are already exploiting. Our Artificial Intelligence Security practice provides the threat modelling, testing, and governance frameworks needed to deploy AI systems with confidence that they are resilient against both adversarial attack and unintended misuse.
Identify and remediate critical AI-specific vulnerabilities before production deployment in AI applications
Make risk and response indicators visible through measured controls, rehearsed playbooks, and evidence review
Turn the outcome into a measurable target with baseline, owner, and review cadence
AI security engagements cover the full AI system lifecycle from design and development through deployment and ongoing monitoring. We assess model access controls, data pipeline security, inference API hardening, and AI governance processes. For generative AI deployments, we address both internal enterprise copilot use cases and customer-facing AI products, each of which carries distinct risk profiles.
Satisfy EU AI Act conformity assessment requirements for high-risk AI systems
Make risk, control, and compliance indicators visible through measured targets and evidence records
Enable responsible AI deployment at enterprise scale with governance controls auditors can verify
AI security deliverables bridge the gap between emerging regulatory requirements and practical engineering implementation. We produce both compliance-oriented artefacts for regulatory and board audiences and engineering-grade specifications for AI development teams. All deliverables are versioned to reflect the rapidly evolving AI security landscape.
Accelerate AI product time-to-market by addressing security in parallel with development rather than as a gate
Provide legal and compliance teams with EU AI Act conformity documentation reducing regulatory risk
Demonstrate responsible AI practices to enterprise customers requiring AI security due diligence evidence
Prompt injection occurs when malicious instructions embedded in user input or retrieved content override the AI system's intended behaviour, causing it to perform unauthorised actions such as leaking system prompts, bypassing access controls, or executing unintended tool calls. It is the highest priority risk because it is pervasive in LLM applications that process untrusted input, difficult to fully prevent at the model level, and can be exploited without any technical sophistication by end users.
Enterprise AI security requires controls at multiple layers: access control on approved data connectors, output scanning to detect and redact sensitive information before it reaches the user, prompt injection detection on user-supplied or imported content, and audit logging of generated outputs and tool actions for forensic purposes.
For foundation model-based systems, we focus our assessment on the application layer controls rather than the model itself. We test the system prompt and instruction hierarchy, input validation and sanitisation, tool call authorisation logic, output filtering, and the data retrieval layer. We also review the contractual security obligations in the foundation model provider's terms of service and assess supply chain risk.
The EU AI Act requirements depend on the risk classification of your AI system. High-risk systems (those affecting fundamental rights, safety, or critical infrastructure decisions) require conformity assessments, human oversight mechanisms, technical documentation, and registration in the EU AI database before deployment. We conduct risk classification assessments and build the required technical and governance documentation to support conformity.
We recommend a security assessment for any significant change to the AI system including model version upgrades, new tool integrations, expansion of the input data scope, or changes to the system prompt or retrieval configuration. A full annual assessment should also be conducted to capture changes in the threat landscape and emerging attack techniques that may affect previously assessed components.
Yes. We offer an AI security capability building programme that trains your internal security team in adversarial machine learning techniques, prompt injection testing methodologies, and AI red team tooling. The programme includes a structured curriculum, hands-on labs using your actual AI systems, and a competency assessment. We also help you establish an internal AI red team operating model with defined scope, cadence, and reporting structure.
This short form routes your request to the right support team. We clarify context first, then define the safe sharing method.
Privacy-aware first contact; safe sharing flow when needed; no sales pressure.
