AI Security
Artificial intelligence is transforming how organisations operate, but it also introduces an entirely new class of security risk. From prompt injection and model poisoning to data leakage through LLM integrations, the attack surface is expanding faster than most security programmes can keep pace. Cyse provides specialist AI security services that help organisations adopt AI safely, govern its use responsibly, and defend against the adversarial threats that target AI-powered systems.
Why AI Security Matters
AI systems are not just software — they are probabilistic, data-dependent, and often opaque. Traditional security controls are necessary but insufficient. Organisations need dedicated expertise to address the unique risks that AI introduces across confidentiality, integrity, and availability.
New Attack Surface
LLMs, ML pipelines, and agentic systems introduce novel attack vectors — prompt injection, training data poisoning, model theft, and adversarial inputs — that traditional security tools do not detect.
Regulatory Pressure
The EU AI Act, NIST AI RMF, and sector-specific guidance are mandating risk assessments, transparency, and governance for AI systems. Non-compliance carries significant legal and reputational consequences.
Trust & Assurance
Customers, regulators, and boards need confidence that AI is being used safely and ethically. Independent security assurance provides the evidence to demonstrate responsible AI adoption.
The AI Threat Landscape
Understanding the threats specific to AI systems is the foundation of effective defence. Our services are built around the OWASP Top 10 for LLM Applications and the MITRE ATLAS adversarial threat framework.
Prompt Injection
Direct and indirect prompt injection attacks that manipulate LLM behaviour, bypass safety controls, exfiltrate data, or trigger unintended actions in agentic workflows.
Training Data Poisoning
Manipulation of training or fine-tuning data to introduce backdoors, biases, or targeted misbehaviour into models, compromising output integrity at the source.
Sensitive Data Exposure
Leakage of PII, credentials, or proprietary information through model memorisation, RAG retrieval errors, verbose outputs, or insecure logging of prompts and responses.
Model Theft & Extraction
Adversarial techniques that reconstruct or steal model weights, architectures, and training data through API abuse, side-channel attacks, or supply chain compromise.
Supply Chain Risks
Compromised models, poisoned datasets, malicious plugins, and vulnerable dependencies in the AI supply chain, from Hugging Face repositories to third-party API providers.
Agentic System Abuse
Exploitation of autonomous AI agents through goal manipulation, tool misuse, privilege escalation, and chained actions that exceed intended boundaries or produce harmful outcomes.
Our AI Security Services
We provide end-to-end AI security services covering governance, risk assessment, technical testing, and operational assurance. Whether you are deploying your first LLM integration or operating complex agentic systems, we tailor our approach to your maturity and risk profile.
AI Risk Assessment
Structured risk assessment of AI systems aligned to the NIST AI RMF and EU AI Act, identifying threats to confidentiality, integrity, availability, and ethical use across the full AI lifecycle.
LLM Security Assessment
Hands-on security testing of large language model deployments covering prompt injection, jailbreaking, data leakage, output manipulation, and integration security with RAG pipelines and tool use.
Agentic AI Security Review
Security assessment of autonomous AI agents and multi-agent systems, evaluating tool access controls, decision boundaries, escalation paths, human-in-the-loop safeguards, and failure modes.
AI Governance & Policy
Development of AI governance frameworks, acceptable use policies, risk classification schemes, and oversight structures that satisfy regulatory requirements and organisational risk appetite.
AI Red Teaming
Adversarial simulation against AI systems using techniques from MITRE ATLAS, testing model robustness, safety guardrails, and the effectiveness of detection and response mechanisms.
ML Pipeline Security
Security review of machine learning pipelines including data ingestion, feature stores, model training infrastructure, CI/CD for ML, model registries, and serving infrastructure.
Data Privacy for AI
Assessment of data handling practices across AI systems including training data provenance, consent management, PII detection, differential privacy implementation, and GDPR compliance for AI outputs.
AI Supply Chain Security
Assessment of third-party models, datasets, plugins, and API integrations for security risks including backdoors, licence compliance, dependency vulnerabilities, and provenance verification.
AI Security Training
Tailored training programmes for developers, security teams, and leadership covering secure AI development practices, threat awareness, responsible AI use, and AI-specific incident response.
Frameworks & Standards
Our AI security engagements are grounded in the latest industry frameworks and regulatory guidance, ensuring our assessments are comprehensive, defensible, and forward-looking.
OWASP Top 10 for LLMs
Testing against the definitive list of critical security risks in LLM applications
MITRE ATLAS
Adversarial threat landscape for AI systems, mapping real-world attack techniques and mitigations
NIST AI RMF
Risk management framework for trustworthy and responsible AI development and deployment
EU AI Act
Compliance readiness for risk-based classification, transparency, and governance requirements
Our Approach
AI security requires a different mindset to traditional application security. Our approach combines adversarial testing with governance advisory, covering both the technical and organisational dimensions of AI risk.
Map the AI Estate
Inventory all AI systems, models, datasets, and integrations across your organisation. Classify each by risk tier based on autonomy, data sensitivity, and business impact.
Assess & Threat Model
Conduct structured threat modelling against each AI system, mapping attack surfaces, trust boundaries, data flows, and adversarial scenarios specific to ML and LLM architectures.
Test & Red Team
Hands-on adversarial testing including prompt injection, jailbreaking, data extraction, model evasion, and agentic abuse scenarios to validate the effectiveness of safety controls.
Govern & Harden
Deliver prioritised remediation guidance, governance framework recommendations, and monitoring strategies to continuously manage AI risk as systems evolve and regulations mature.
Adopt AI Safely and Securely
Whether you are evaluating your first LLM deployment or operating complex AI systems at scale, our specialists can help you identify and manage the risks that come with AI adoption.