Turning AI Ambition into Secure Advantage
In today’s fast‑moving digital world, artificial intelligence (AI) and machine learning (ML) are no longer futuristic ideas; they’re integral to how organisations make decisions, deliver services, and build efficiency. But alongside the promise of smarter systems comes a lesser‑told story: the hidden vulnerabilities embedded in the supply chain of AI.
The Australian Cyber Security Centre (ACSC)’s new article “Artificial intelligence and machine learning: Supply chain risks and mitigations” warns that while AI and ML systems drive innovation, they also introduce unique vulnerabilities, especially when components such as training data, models, and software libraries come from third-party sources.
A single compromised dataset, library, or cloud service can expose sensitive information, disrupt operations, or erode public trust. As per the ACSC, in 2025, 65% of organisations reported AI-related data leaks, and 13% reported breaches directly linked to AI systems. The lesson? Ignoring supply chain risks isn’t an option. Leaders can no longer treat AI risk as a technical side issue; it’s a strategic business concern that directly impacts resilience, compliance, and reputation.
In this blog, we unpack how the AI/ML supply chain works, why it presents unique security challenges, and what practical steps organisations can take to build resilience. Let’s dive in.
Understanding and Managing AI/ML Supply Chain Security Risks
AI/ML supply chain risk management focuses on protecting every stage of how an AI system is built and delivered - this “supply chain” includes everything from the data used to train models to the algorithms, software tools, and external vendors involved. Each link in this chain can introduce risks.
For example, training data might be manipulated, a third-party model could contain hidden vulnerabilities, or open-source code might include backdoors. Unlike traditional risk management, which deals with broader cybersecurity threats, AI/ML supply chain risk management zeroes in on these AI-specific risks to ensure that the entire ecosystem - from data to deployment - remains secure and reliable. Organizations must source and manage:
- Training data
- Machine learning models
- AI software and libraries
- Infrastructure and hardware
- Third-party services
Each component can carry vulnerabilities. Attackers could exploit these weaknesses to expose sensitive data, disrupt operations, or inject malicious code. That’s why supply chain security must be a top priority.
To mitigate these threats effectively, organizations must move beyond awareness and take deliberate action to integrate AI and ML supply chain considerations into their cybersecurity programs. Key steps, in this regard, include:
- Identify all players: Know your suppliers, subcontractors, and service providers.
- Assess new functionality: AI features in existing systems may change risk profiles.
- Define shared responsibilities: Work with third parties early to clarify who handles which security aspects.
How You Can Implement Supply Chain Risk Management for AI
AI supply chains are often invisible until something goes wrong. From corrupted training data to compromised models or cloud services, vulnerabilities can appear at every step, and the consequences can ripple across operations, reputation, and compliance. Understanding where these risks lie and how to address them is essential for organisations that want to leverage AI safely and responsibly.
A. Securing AI Data
Data is the lifeblood of AI, and a major target for attackers. Risks include:
- Low-quality or biased data - Leads to inaccurate or unfair AI outputs.
- Data poisoning - Malicious modifications can degrade model performance or introduce hidden triggers.
- Training data exposure - Attackers can reverse-engineer models to extract sensitive information.
Mitigation steps:
- Use trusted data sources and maintain data provenance.
- Conduct preprocessing and sanitisation to remove bias, noise, or malicious content.
- Consider data obfuscation or synthetic data to protect sensitive information.
- Apply ensemble methods to detect inconsistencies across multiple datasets or models.
B. Protecting Machine Learning Models
ML models are not immune to attacks. Key threats include:
- Model poisoning - Degrades performance or embeds hidden backdoors.
- Malware embedding - Models themselves become carriers for malicious code.
- Evasion attacks - Clever inputs can bypass AI-based security mechanisms.
Mitigation steps:
- Only use models from trusted sources.
- Implement secure file formats and safe loading procedures.
- Employ model verification, explainability, and ensemble methods.
- Perform reproducible builds and continuous testing and monitoring.
- Consider adversarial training and model refining to increase resilience.
C. AI Software, Infrastructure, and Hardware
AI software often relies on multiple libraries and frameworks, each is a potential attack vector. Hardware such as GPUs or AI accelerators adds additional vulnerabilities via drivers and firmware.
Mitigation steps:
- Treat AI software and hardware like any critical IT asset.
- Conduct malware scanning, integrity validation, and least-privilege practices.
- Ensure signed firmware, verified boot, and network segmentation.
D. Managing Third-Party Services
Many organisations rely on cloud-based AI services or fully outsourced AI solutions. Third parties simplify deployment but add risk.
Mitigation steps:
- Conduct vendor assessments on security practices and vulnerability management.
- Include cybersecurity requirements in contracts (e.g., audit rights, continuity planning, and data handling restrictions).
- Monitor ongoing compliance and maintain transparency across the supply chain.
Secure AI, Strengthen Your Future
AI and ML promise extraordinary gains, but only for organisations that build them on trusted, transparent, and secure foundations. Supply chain vulnerabilities aren’t just technical flaws; they’re potential points of failure that can ripple across entire operations.
By embedding AI-specific risk management into your cybersecurity strategy, protecting data, validating models, assessing vendors, and enforcing accountability, you not only reduce threats but also increase confidence in your AI systems.
The message is clear: secure AI is sustainable AI. Those who take a proactive approach to supply chain security today will be the ones leading responsibly, innovating confidently, and earning long-term trust in tomorrow’s digital ecosystem.
Securiti’s Genstack AI Suite secures the entire GenAI lifecycle, delivering end-to-end AI governance including secure data ingestion and extraction, masking, LLM configuration, inline controls, AI model discovery, AI risk assessments, Data+AI mapping, and regulatory compliance.
Request a demo to learn more about how Securiti can help.
