Service
Architecture review before implementation
Implementation scope and rollout planning
Clear next-step recommendation
Comprehensive security audits to identify and mitigate risks from poisoned models, backdoors, and compromised dependencies across your AI supply chain.
Your AI's security is a chain of dependencies. A single compromised model from Hugging Face, poisoned training data, or a vulnerable deployment pipeline can introduce critical backdoors. We conduct end-to-end security assessments of your AI development lifecycle to eliminate these risks.
We map your entire AI supply chain—from third-party repositories and data sources to CI/CD pipelines—identifying vulnerabilities before they become breaches.
Move from reactive patching to proactive security. Our assessments provide a hardened, auditable supply chain, reducing the risk of model hijacking and ensuring the integrity of your AI deployments. For continuous protection, explore our Continuous AI Red Teaming Programs.
Our AI Supply Chain Security Assessment delivers concrete, actionable results that mitigate risk and build enterprise trust. We move beyond theoretical threats to provide verified security improvements.
A comprehensive inventory and risk scoring of all external models, datasets, and libraries in your AI pipeline. We identify unvetted dependencies, known vulnerabilities (CVE tracking), and license compliance issues, providing a clear roadmap for remediation or replacement.
Verifiable documentation tracing the origin, transformations, and custody of all training and fine-tuning data. This audit prevents poisoned or biased data ingestion and is critical for compliance with frameworks like NIST AI RMF and the EU AI Act's data governance requirements.
Implementation of security gates, automated scanning for model artifacts, and integrity checks within your MLOps deployment pipeline. This prevents compromised models from reaching production and integrates security into the AI development lifecycle (AI-SDLC).
Detailed assessment of the security practices of your AI technology vendors and data providers. This report evaluates their adherence to security best practices, helping you manage third-party risk and inform procurement decisions.
A tailored response plan for AI-specific security incidents, such as a detected model backdoor or data poisoning attack. This ensures your security team can contain, eradicate, and recover from supply chain compromises with minimal business impact.
A clear mapping of your AI supply chain security controls against major regulatory and industry standards, including NIST AI RMF, ISO/IEC 42001, and the EU AI Act. We provide prioritized recommendations to close compliance gaps efficiently.
Our phased assessment methodology provides clear deliverables and timelines to systematically secure your AI development lifecycle, from model sourcing to deployment.
| Assessment Phase & Key Deliverables | Starter (2-3 Weeks) | Professional (4-6 Weeks) | Enterprise (6-8 Weeks) |
|---|---|---|---|
Third-Party Model & Repository Audit | |||
Training Data Provenance & Poisoning Analysis | Limited Scope | ||
CI/CD Pipeline & Deployment Security Review | |||
Custom Adversarial Testing (Prompt Injection, Backdoors) | Selected Models | Full Suite | |
Vendor Risk Assessment & Dependency Mapping | |||
Remediation Roadmap & Technical Controls | Summary Report | Prioritized Plan | Custom Policy-as-Code |
Executive Summary & Compliance Gap Analysis (NIST AI RMF, ISO 42001) | |||
Ongoing Monitoring & Re-assessment | Quarterly Scan | Continuous Program | |
Starting Investment | $15K | $45K | Custom |
Our AI Supply Chain Security Assessment is engineered for industries where compromised models or poisoned data pipelines pose existential business, compliance, and safety risks. We deliver actionable audits that map to your specific threat landscape.
Enabling Efficiency, Speed & Accuracy
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
Get specific answers about our methodology, timeline, and deliverables for securing your AI development lifecycle from third-party risks.
Our assessment covers the entire AI development lifecycle. We audit third-party model repositories (Hugging Face, PyPI), training data sources and pipelines, CI/CD deployment tooling, and the provenance of all dependencies. This identifies risks like poisoned models, backdoored weights, compromised SDKs, and insecure data handling. For a detailed breakdown, see our AI Red Teaming and Adversarial Defense pillar.
About the author
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
The first call is a practical review of your use case and the right next step.
