Service

MLSecOps & Adversarial Defense

AI pipelines against prompt injection, data poisoning and model evasion. Aligned with NIST AI RMF and OWASP Top 10 LLMs.

MITRE ATLAS OWASP Top 10 LLMs NIST AI RMF ART CleverHans TF Privacy MLflow Seldon BentoML SHAP LIME
What's included

AI threat modelling — MITRE ATLAS + OWASP Top 10 LLMs applied to your architecture

MLOps pipeline hardening — dataset validation, training isolation, granular access control

Active adversarial defence — ART, CleverHans, TF Privacy robustness testing

Post-deployment monitoring — drift detection, output anomalies, prompt injection alerts

NIST AI RMF alignment — Govern, Map, Measure, Manage lifecycle coverage

EU AI Act audit — risk-level classification, technical documentation, audit registry

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Core technologies
MITRE ATLAS NIST AI RMF OWASP LLMs ART
FAQ

Frequently Asked Questions

What is prompt injection and why is it dangerous?
Prompt injection is an attack where a malicious input manipulates an LLM into ignoring its instructions, leaking sensitive data, or executing unintended actions. It is the OWASP #1 risk for LLM applications and requires specific architectural defences beyond traditional security controls.
Does the EU AI Act apply to my AI system?
If you develop, deploy, or use AI systems within the EU, the EU AI Act applies. High-risk systems in sectors like healthcare, critical infrastructure, and employment face the strictest requirements from August 2026. We provide a risk-level classification as the first step of every MLSecOps engagement.
What is data poisoning in machine learning?
Data poisoning is an attack where an adversary injects malicious samples into a training dataset to corrupt the model's behaviour — causing targeted misclassifications or creating hidden backdoors. It is particularly dangerous in systems that retrain continuously on user-generated data.

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