Managing Generative AI Supply Chain Risks in the Enterprise
Artificial Intelligence & Security · 2 min
Articles by Luís Carvalho
Generative AI creates new opportunities but also new risks related to data, models, infrastructure, and compliance. Learn about key strategies for safe and responsible adoption.
TL;DR
- Generative AI introduces new dependencies throughout the technological supply chain
- Data quality and model transparency are critical factors for risk reduction
- Infrastructure and vendor security must be considered from the outset
- The AI Act reinforces governance and documentation requirements for AI systems
- An AI governance strategy helps reduce technical, legal, and reputational risks
The Complexity of the Generative AI Supply Chain
The increasing adoption of generative AI is transforming how organizations develop products, automate processes, and support decision-making. However, this evolution also creates a more complex supply chain, composed of training data, foundational models, computing infrastructure, cloud services, and specialized providers. Each of these elements can introduce risks related to security, compliance, availability, or information quality, making integrated management of the entire ecosystem essential.
Data and Models: The First Level of Risk
The quality of the data used to train models directly influences the results produced. Incomplete, biased, or inadequately sourced data can lead to incorrect, discriminatory, or unsubstantiated responses. Similarly, attacks such as model poisoning or the use of models without sufficient information about their origin make it difficult to assess the reliability of implemented solutions. For this reason, transparency regarding data and model provenance is becoming increasingly important.
Infrastructure and Vendors
The infrastructure supporting generative AI applications must adhere to the same security principles required for any critical system. Incorrect configurations, access control failures, or vulnerabilities in programming interfaces (APIs) can compromise models, data, or services. Evaluating the [security posture](/en/solutions/seguranca-fisica-e-logica) of vendors and [cloud services](/en/solutions/cloud) used is, therefore, an essential component of risk management.
Compliance and Governance
The entry into force of the European Union's AI Act has reinforced the need for a structured approach to AI governance. Organizations must ensure adequate documentation, risk assessment, human oversight mechanisms, and control processes proportionate to the risk level of the systems used. Effective governance not only addresses [regulatory requirements](/en/blog/nis2-os-principais-passos-para-reforcar-a-conformidade) but also contributes to increasing user trust and reducing operational risks.
Strategies for Risk Reduction
A consistent approach involves: 1. carefully selecting vendors and models; 2. continuously assessing technical and legal risks; 3. monitoring system behavior in production; 4. reducing excessive dependencies on a single vendor; 5. developing internal capabilities in AI, security, and governance. The creation of an internal [AI governance model](/en/blog/agentes-de-ia-e-identidades-nao-humanas-desafios-de-governacao) allows for defining responsibilities, evaluation processes, and control mechanisms throughout the entire lifecycle of implemented solutions.
Conclusion
Generative AI represents a significant opportunity to accelerate business innovation, but it also introduces new risks that must be managed in a structured manner. By combining governance, security, transparency, and continuous monitoring, organizations will be better prepared to [adopt AI solutions safely](/en/solutions/aiops-genai), responsibly, and in compliance with the European regulatory framework.