AI Security

Organisations are embedding AI across products, operations, and decision-making. Agabis helps you understand the security risks those systems introduce and address them before they become incidents.

Why AI Needs Different Security

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AI systems introduce risks that traditional security testing does not cover. A penetration test of your web application will not tell you whether your LLM can be manipulated into leaking data, whether your RAG pipeline retrieves content it should not, or whether your AI agent can be redirected through prompt injection.

These are no longer theoretical concerns. They are documented, reproducible attack patterns that affect production systems today.

AI also creates governance challenges. Regulations and standards are evolving quickly. Clients, investors, and regulators are asking questions about how AI systems are controlled, monitored, and governed. Organisations need structured answers, not vague assurances.

Agabis treats AI security as a distinct practice area because the threat model, the attack surface, and the governance requirements are fundamentally different from traditional application security.

What AI Security Covers

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Generative AI & LLM

Chatbots, copilots, content generation, summarisation tools

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Retrieval-augmented generation (RAG)

Systems that combine LLMs with organisational data, documents, and knowledge repositories

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AI agents and agentic workflows

Systems that can make decisions, trigger workflows, or perform tasks with limited human input

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Traditional ML systems

Models used for classification, prediction, scoring, and recommendation

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AI-enabled applications

Products or platforms with AI embedded into core features and workflows

Our AI Security Services

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AI Security Assessments

Structured security evaluation of AI systems, covering model behaviour, data handling, integration points, access controls, and output validation. Identifies vulnerabilities specific to AI workloads that conventional testing overlooks.

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AI Governance

Implementation and validation of AI governance frameworks aligned with ISO 42001, NIST AI RMF, and emerging regulatory requirements. Covers policy development, risk management, accountability structures, and audit readiness.

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AI Security Architecture Reviews

Technical review of AI system design, data flows, integration patterns, and deployment architecture. Identifies structural weaknesses, insecure configurations, and design decisions that introduce risk before they reach production.

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AI Red Teaming

Adversarial testing of AI systems designed to expose exploitable weaknesses. Covers prompt injection, jailbreaking, data extraction, output manipulation, and abuse scenario modelling. Tests how your AI behaves when someone actively tries to break it.

Our Delivery Approach

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Discovery and Scoping

We begin by understanding your objectives, current environment, business context, and the outcomes you need from the engagement. This ensures the scope is aligned to what matters most.

02

Assessment and Analysis

We review the relevant systems, processes, controls, and workflows in scope to establish a clear picture of the current state and identify areas of risk, weakness, or opportunity.

03

Reporting and Prioritisation

We consolidate the findings into clear, structured outputs with supporting evidence, risk impact, and prioritised actions, helping your teams focus on what should be addressed first.

04

Recommendations and Roadmap

We provide practical recommendations and a clear roadmap that aligns technical, operational, and business priorities, helping you move from immediate actions to longer-term improvement.

FAQ

What is AI security?

AI security is the structured assessment and protection of AI systems, models, data pipelines, integrations, and governance controls. It covers risks across machine learning models, large language models (LLMs), retrieval-augmented generation (RAG), agentic workflows, and AI-enabled applications.

What is an AI security assessment?

An AI security assessment is a structured evaluation of an AI system’s security posture. It examines model behaviour, data handling, integration points, access controls, and output validation to identify vulnerabilities that conventional security testing may not cover. The objective is to provide a clear view of where your AI system is exposed, what the risks are, and what actions should be prioritised.

How is AI security assessment different from traditional penetration testing?

Traditional penetration testing focuses on infrastructure, applications, APIs, and networks. AI security goes further by assessing risks specific to AI systems, including prompt handling, model behaviour, training and inference pipelines, integrations, permissions, and governance controls.

What frameworks does Agabis align AI security work with?

We align our work with ISO 42001, NIST AI RMF, OWASP LLM Top 10, and MITRE ATLAS. The specific framework depends on the engagement type and your regulatory or certification requirements.

How long does an AI security engagement typically take?

It depends on scope. A focused AI security assessment of a single system might take one to two weeks. A broader programme covering multiple systems, architecture review, and governance can run over several weeks. We scope every engagement individually based on what you need.

Do you assess governance and compliance as part of AI security?

Yes. AI security includes both technical assessment and governance review. This may include oversight processes, human review controls, model risk management, access governance, supplier dependencies, and alignment with standards such as ISO 42001.

Understand Your AI Security Posture

Tell us what you are building or deploying. We will help you identify the risks and define the right approach.