AI & Emerging Technology Security
AI Security Assessment & Testing
AI systems introduce attack surfaces that traditional security tools cannot see. We provide specialist security assessments for ML models, LLM deployments, and AI-powered applications — identifying adversarial vulnerabilities before threat actors do.
Organisations Are Deploying AI Faster Than They Can Secure It
The rush to deploy AI — from internal LLM copilots to customer-facing AI agents — is creating an enormous and largely unassessed attack surface. Traditional security tools were not built to test AI systems, and traditional security teams lack the adversarial ML expertise needed to identify vulnerabilities that exist only in AI components.
The consequences of unsecured AI include: prompt injection attacks that manipulate AI agents into performing unauthorised actions, training data poisoning that causes models to behave incorrectly at inference time, and model theft that exposes proprietary IP to competitors and threat actors.
Adversarial Testing
Evasion, model inversion, membership inference, and model extraction attacks
Prompt Injection
Direct and indirect prompt injection testing across LLM applications and agents
Data Security
Training data governance, PII exposure, and data poisoning risk assessment
Supply Chain
Open-source model dependencies, dataset provenance, and pipeline security
5-Phase AI Security Assessment
A structured, research-backed methodology combining threat modelling, adversarial testing, and governance review to deliver a comprehensive view of your AI attack surface.
AI System Scoping & Architecture Review
We map the complete AI system landscape — data pipelines, model training infrastructure, inference APIs, orchestration layers, and integration points — establishing a comprehensive attack surface before any testing begins.
Threat Modelling for AI Systems
We build a threat model specific to your AI deployment — identifying adversarial attack paths including model extraction, data poisoning, prompt injection, and inference API abuse based on your system's architecture and data sensitivity.
Adversarial & Red Team Testing
Our specialists execute realistic attack scenarios against your AI systems — testing for model robustness against adversarial inputs, prompt injection vulnerabilities, jailbreaking, training data extraction, and supply chain risks in model dependencies.
Data Security & Privacy Assessment
We evaluate the sensitivity and protection of training data, inference inputs, and model outputs — assessing data governance, access controls, PII exposure risks, and compliance posture under DPDP Act and GDPR.
Findings Report & Remediation Roadmap
A comprehensive report mapping every identified vulnerability to the OWASP Top 10 for LLMs and MITRE ATLAS framework — with risk-prioritised remediation guidance your engineering and security teams can act on immediately.
Comprehensive AI Security Coverage
From ML penetration testing to LLM security and AI governance — specialist coverage across the full AI security domain.
AI/ML Penetration Testing
Targeted adversarial testing of your machine learning models — evaluating robustness against evasion attacks, model inversion, membership inference, and other ML-specific attack techniques.
LLM Security Assessment
Comprehensive security testing of Large Language Model deployments — covering prompt injection, jailbreaking, data leakage, system prompt extraction, and indirect prompt injection through retrieval-augmented pipelines.
AI Supply Chain Security
Security evaluation of your AI model supply chain — assessing risks in open-source model dependencies, pre-trained model integrity, dataset provenance, and training pipeline security.
Model API Security Testing
Penetration testing of AI inference APIs — evaluating authentication, rate limiting, input validation, output filtering, and the security of model serving infrastructure exposed to external consumers.
Training Data Security Review
Assessing the security and governance of training datasets — evaluating data poisoning risks, access controls over training data stores, data lineage documentation, and exposure of sensitive PII within model training data.
AI Governance & Risk Advisory
Advisory support to establish an AI risk governance framework — defining AI security policies, model risk management procedures, and the oversight structures required by emerging AI regulations.
AI Security Requires Specialists Who Understand Both AI and Security.
Our AI security team combines ML engineering expertise with offensive security skills — the rare combination required to find vulnerabilities that exist only in AI systems.
OWASP LLM Top 10
Our testing methodology is anchored to the OWASP Top 10 for LLMs and the MITRE ATLAS adversarial ML framework — the most comprehensive AI threat taxonomies available.
Multi-Modal Coverage
We test text, image, audio, and multimodal AI systems — covering the full spectrum of AI deployment types with specialist techniques for each modality.
Regulatory Alignment
Our assessments are designed to support compliance with EU AI Act obligations, DPDP Act data protection requirements, and RBI/SEBI AI governance guidance.
Research-Driven
Our team publishes AI security research and actively tracks emerging attack techniques — ensuring your assessment reflects the current frontier of adversarial AI threats.
Frameworks & Standards Our Assessments Address
Frequently Asked Questions
Everything you need to know about AI security assessment services
Secure Your AI Systems Before Attackers Find the Gaps
AI security is not optional — it's the next frontier of enterprise risk. Let our specialists assess your AI deployment and help you build the security controls your models, data, and users deserve.
Get in Touch
Ready to secure your future? Reach out to us for a consultation.