This ensures your AI systems are built on verifiable, trustworthy foundations.
AI Supply Chain Security Assessment
Securing the models, data, and dependencies your AI relies onModern AI systems are assembled from pre-trained models, open-source libraries, datasets, embeddings, and third-party components, each introducing potential supply chain risk beyond traditional code dependencies. Unlike conventional application security, AI brings a new supply chain of models, data, and weights, making hidden or compromised components a critical concern—addressed by Entersoft’s AI Supply Chain Security Assessment to identify and mitigate these risks before they reach production.What Is an AI Supply Chain Security Assessment?
AI Supply Chain Security Assessment is a specialized service under Entersoft’s AI Application Security Testing (AI-AST™) framework.
It focuses on where your AI components come from, how they are validated, and what risks they introduce.
- Model provenance and trustworthiness
- Dataset integrity and poisoning risks
- Dependency and version control gaps
- Hidden behaviors embedded in model weights
- Governance and approval workflows for AI assets
Why AI Supply Chain Security Matters
AI supply chains operate differently from traditional software supply chains.
Pre trained models from public repositories
Fine tuned vendor or partner models
Open source embedding models
Third party datasets and prompt templates
Inherited without provenance
Leak sensitive information
AI supply chain attacks do not break systems they corrupt intelligence.
Where AI Supply Chain Risks Exist
AI supply chain risks commonly arise in systems that use
- Public model repositories (e.g., open-source model hubs)
- Third-party or vendor-provided AI models
- Fine-tuned models inherited across teams
- Open-source embedding or feature extraction libraries
- Prompt templates or system prompts sourced externally
If your AI system depends on components you did not fully create or audit, AI supply chain security applies.
How AI Supply Chain Attacks Happen
AI supply chain attacks are subtle by design.
- Backdoored models that behave normally until triggered
- Poisoned datasets that bias or manipulate outcomes
- Malicious weights embedded during training or fine-tuning
- Dependency confusion across model versions or forks
- Untracked updates altering model behavior post-deployment
These attacks bypass traditional security controls because they operate inside the AI decision layer.
How Entersoft Assesses AI Supply Chain Risk
Our AI Supply Chain Security Assessment begins with full visibility into your AI asset ecosystem.
Model Sources & Lineage
We examine where models originate, how they are versioned, and how updates are introduced to detect untrusted sources or hidden changes.
Dataset Origins & Validation
We assess data sources, labeling workflows, and validation checks to identify poisoning risks, bias introduction, or integrity gaps.
Third-Party & Open-Source Dependencies
We review external libraries, frameworks, and vendor components for dependency risks, insecure configurations, and update exposure.
Fine-Tuning & Transfer Learning
We analyze fine-tuning pipelines and model reuse practices to uncover inherited risks, embedded behaviors, or unsafe training artifacts.
Approval, Monitoring & Rollback
We evaluate governance mechanisms to ensure changes are approved, continuously monitored, and reversible when anomalies are detected.
Built for Enterprise Risk & Compliance
AI Supply Chain Security Assessment aligns with
- OWASP LLM Top 10 (Supply Chain & Training Data Risks)
- OWASP ML Top 10
- NIST AI Risk Management Framework (AI RMF)
- ISO/IEC 42001 and ISO/IEC 23894
Findings are delivered in a format suitable for security teams, auditors, and boards.
Security Beyond Models and Data
AI supply chain security is not just a technical problem it’s a governance challenge.
- Asset inventory and ownership
- Change management and version control
- Audit trails and traceability
- Risk acceptance and vendor accountability
- Alignment with Responsible AI and regulatory expectations
This ensures AI systems remain secure, explainable, and defensible.
Why Enterprises Choose Entersoft for Agent Security Testing
Organizations trust Entersoft because we combine
Offensive Security Depth
Real-world attacker expertise applied to AI agent threats.
Automation Experience
Hands-on security testing for autonomous and automated systems.
Audit-Ready Reporting
Framework-aligned findings built for compliance and audits.
Actionable Remediation
Clear, practical fixes tailored to live environments.
When Do You Need AI Supply Chain Security?
You should conduct an AI supply chain security assessment if
You use pre-trained or open-source AI models
You integrate third-party or vendor AI components
You fine-tune models across teams or environments
You operate in regulated or high-trust industries
You cannot fully trace AI asset provenance
