Non-Human Identity Governance for Generative AI: Securing AI Workloads and Access

Read full article here: https://www.oasis.security/blog/securing-generative-ai-with-non-human-identity-management-and-governance/?utm_source=nhimg

The rapid adoption of generative AI brings incredible opportunities for business innovation but also introduces unique risks. As organizations implement AI-driven applications, proper governance of non-human identities (NHI) becomes critical to safeguarding data privacy, maintaining integrity, and reducing operational risk.

Understanding Retrieval-Augmented Generation (RAG) Architecture

Retrieval-Augmented Generation (RAG) is an AI architecture that blends large language models (LLMs) with customer-specific “grounding data.” By connecting an LLM like OpenAI’s GPT to local datasets, organizations can build AI applications that answer domain-specific questions or enhance productivity, fully autonomously.

While powerful, RAG architectures rely heavily on backend machine-to-machine communication. This is where non-human identities—service accounts, API keys, access tokens, and other machine credentials, become central to operational workflows.

AI Expands the Non-Human Identity Attack Surface

NHIs are proliferating faster than any other type of identity, often self-managed across development, operations, and cloud teams. Poor governance of these identities—combined with the ubiquity of cloud-based access—creates significant exposure.

Common risks include:

• Unrotated or overly permissive credentials (SAS tokens, service principals, access keys)
• Stale or abandoned identities tied to former employees
• Inadequate monitoring of cloud storage accounts used as RAG data repositories

These vulnerabilities can lead to data leakage, unauthorized access, and data poisoning, jeopardizing both AI training datasets and the integrity of responses generated by AI-enabled apps.

The Intersection of NHI and RAG Risks

In RAG architectures, NHIs bridge AI models with cloud-hosted data. Mismanaged identities in storage accounts or AI pipelines can allow malicious actors to modify training data, inject harmful content, or exfiltrate sensitive information. Even trusted cloud services are at risk if secrets are misconfigured, unrotated, or poorly monitored—highlighting the urgent need for NHI governance.

Implementing Effective NHI Governance for AI

Organizations can mitigate these risks by adopting a comprehensive NHI management strategy, including:

• Inventorying NHIs across multi-cloud, SaaS, and on-prem environments
• Actionable insights and automated remediation for misconfigured or high-risk identities
• Ongoing lifecycle management for critical NHIs tied to high-value projects
• Full visibility of usage and entitlements, ensuring no token or credential is overlooked

Oasis provides the tools and automation needed to manage NHI safely, ensuring that AI-driven applications operate securely and in compliance with best practices.

Conclusion

As generative AI adoption accelerates, organizations must treat NHIs as first-class citizens in cybersecurity. Proper governance, lifecycle management, and continuous monitoring of machine identities are no longer optional—they are critical to protecting data, maintaining AI integrity, and ensuring operational resilience in an increasingly AI-powered world.