AI, Data & Intelligence
AI Model Security
Protection of machine learning models themselves against theft, tampering, poisoning, adversarial inputs and malicious model files, from training through registry to production serving.
Capability overview
What ai model security involves
Trained models are valuable assets and attack surfaces. A competitor can approximate a model by querying it repeatedly, an insider can alter a model file in the registry, poisoned training data can plant hidden behaviour, and carefully crafted inputs can fool image or fraud models while looking normal to people.
Model security controls address these threats at each stage. Examples include scanning downloaded models for unsafe serialisation such as malicious pickle payloads, preferring safer formats like safetensors, signing model artefacts, restricting registry access, validating training data provenance and testing resilience against adversarial examples.
What is included
Controls we implement
Model artefact integrity
Hashing and signing of models, with serving infrastructure refusing to load unsigned or modified artefacts.
Safe model intake
Scanning of open-source and third-party models before use, with approved sources and formats defined in policy.
Training data protection
Provenance tracking, access control and anomaly checks to detect poisoning or unauthorised changes in datasets.
Adversarial resilience testing
Evasion attacks generated with open-source security libraries such as ART to measure how easily predictions can be manipulated.
Extraction resistance
Rate limits, query monitoring and output detail reduction that make copying a model through its API harder.
How we work
How we deliver ai model security
Model asset inventory
Models, datasets, registries and serving endpoints listed with their value and exposure.
Threat prioritisation
Threats ranked by attacker motivation and impact for each model, for example evasion for fraud models.
Control implementation
Signing, scanning, access and monitoring controls added to the ML pipeline and registry.
Resilience evaluation
Adversarial tests run and mitigation options such as input preprocessing or adversarial training assessed.
Pipeline enforcement
Controls made mandatory in CI/CD so new models cannot reach production without passing them.
Related capabilities
Related capabilities in Responsible AI & AI Security
LLM Security
Runtime security for applications built on large language models: controls against prompt injection, sensitive data disclosure, insecure output handling, excessive agency and unbounded consumption.
AI Red Teaming
Adversarial exercises in which specialists try to make your AI system misbehave, including jailbreaks, harmful or biased outputs, data leakage and misuse of tools, before real users or attackers do.
Prompt Injection Risk Assessment
A focused assessment of how exposed your RAG applications, copilots and AI agents are to direct and indirect prompt injection, and what an attacker could achieve through it.
AI Privacy & Data Protection
Privacy by design for AI systems: lawful data use for training and inference, DPDP Act and GDPR obligations, data minimisation, retention, vendor terms and handling of individuals' rights when AI is involved.
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Questions & answers
Questions about AI Model Security
Cannot find what you need? Our team responds to technical and commercial questions within one business day.
Ask a questionMany are, but model files can contain executable code or hidden behaviour. Scanning, using safer file formats, pinning versions and sourcing from reputable publishers reduce the risk considerably.
Models facing motivated adversaries, such as fraud detection, content moderation, malware classification, identity verification and visual inspection used for safety or compliance.
Model security protects the model artefact and its training and serving pipeline for any kind of model. LLM security focuses on how language model applications behave at runtime, such as prompt injection and data leakage.
Assessments are fixed price per model family. Implementation of pipeline controls is quoted as a project, often combined with MLOps improvements.
Next step
Discuss ai model security with Acmez
Share what you need to change, build, integrate or support. We will map the practical next step.