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AI Integration
& Strategy

AI is transforming how organisations operate, but without the right security and governance foundation, it also expands your attack surface and compliance exposure. We help you harness AI's potential without taking on unnecessary risk.

Service Scope

What We Deliver

End-to-end AI advisory, from identifying use cases through to secure deployment, governance, and ongoing risk management.

AI Use Case Identification

Structured discovery workshops to identify where AI can deliver measurable value in your operations, and where it introduces risk that outweighs the benefit.

Discovery ROI Analysis

AI Governance Framework

Design and implementation of AI governance policies, covering model selection, data handling, output validation, human oversight, and acceptable use, aligned to your regulatory environment.

Policy Design Oversight

LLM Security Assessment

Security review of your LLM deployments against OWASP LLM Top 10, covering prompt injection, data leakage, insecure plugins, training data poisoning, and supply chain risks in your AI stack.

OWASP LLM 10 Prompt Injection

Vendor & Tool Evaluation

Independent security and privacy evaluation of AI vendors and tools before procurement, reviewing data retention practices, model training terms, API security, and contractual protections.

Due Diligence Privacy Review

AI Acceptable Use Policy

Development of clear, enforceable AI acceptable use policies for your staff, covering approved tools, prohibited inputs, data classification rules, and the consequences of misuse.

AUP Staff Guidance

Board & Executive AI Briefings

Clear, jargon-free briefings for boards and leadership teams on AI risk, emerging regulation (EU AI Act, UK AI framework), and their governance responsibilities in an AI-enabled organisation.

EU AI Act Board-Level
AI Risk Assessment

Framework-Aligned
AI Risk Assessment

AI risk is fundamentally different from traditional IT risk, it introduces probabilistic outputs, data lineage challenges, and novel attack vectors. Our AI risk assessments are structured against purpose-built frameworks developed specifically for these challenges.

NIST AI RMF 1.0 Govern · Map · Measure · Manage

The NIST AI Risk Management Framework provides a structured approach to identifying, assessing, and managing AI risks across the full lifecycle. We assess your AI systems across all four core functions and produce a maturity profile with targeted improvement actions.

OWASP LLM Top 10 2025 Edition

Security-focused testing against the ten most critical LLM vulnerabilities, including prompt injection, sensitive information disclosure, insecure output handling, training data poisoning, and supply chain risks in your AI pipeline.

EU AI Act Risk Classification & Obligations

Classification of your AI systems under the EU AI Act's risk tiers (unacceptable, high, limited, minimal) and assessment of compliance obligations, including conformity assessments, technical documentation, and human oversight requirements.

ISO/IEC 42001:2023 AI Management System Standard

The emerging international standard for AI management systems, covering risk assessment, governance structures, human oversight, and responsible AI practices. We assess readiness and support organisations pursuing certification.

Simulation Exercises

AI Incident Tabletop Exercises

AI systems fail in ways traditional software does not, from hallucinated outputs reaching customers to prompt injection attacks manipulating decisions. Our AI TTX sessions prepare your teams for these novel scenarios.

Prompt Injection Attack

A user injects malicious instructions into your customer-facing AI tool, causing it to leak internal data or take unintended actions. Tests detection, response, and customer communication.

AI-Generated Misinformation

Your AI assistant confidently provides incorrect regulatory guidance to a client. Tests escalation procedures, liability assessment, remediation, and regulatory disclosure obligations.

Training Data Exfiltration

A security researcher demonstrates that your LLM can be made to reproduce training data containing PII. Tests breach assessment, data subject notification, and GDPR obligations.

Shadow AI Discovery

IT discovers staff have been using an unapproved LLM tool and uploading customer data. Tests your AI acceptable use enforcement, data recovery options, and notification responsibilities.

4
AI-specific frameworks assessed in our review
10
OWASP LLM vulnerabilities tested in every assessment
Why It Matters

The Risks of Unmanaged AI

Organisations that adopt AI without a security and governance foundation are taking on risks that compound quickly, and that regulators are increasingly scrutinising.

Data Exposure

Sensitive data entered into AI tools may be used for model training or accessible to third parties, breaching confidentiality obligations and GDPR.

Adversarial Manipulation

Prompt injection attacks can cause AI systems to ignore safety guardrails, leak data, or perform unintended actions, attackers actively exploit these in production systems.

Regulatory Liability

The EU AI Act, ICO guidance, and sector-specific regulators are actively developing AI oversight requirements. Non-compliance creates financial and reputational exposure.

Shadow AI

Employees routinely adopt AI tools without IT or security approval, uploading customer data, proprietary information, and credentials into unvetted third-party systems.

Get Started

Harness AI.
Without Increasing Risk.

Book a free consultation. We will discuss where you are on your AI journey, what risks you are carrying, and how to build a programme that drives real value, securely.