PAM 
A useful way to evaluate a modern identity security platform is to look at three core pillars: strong authentication and access controls, Privileged Access Management (PAM)
3 min read
Published on February 25, 2026
As organizations rely more on Artificial Intelligence (AI) to power critical operations, the infrastructure supporting AI development and deployment is becoming a high-value target for cybercriminals. From model training and data pipelines to cloud workloads and APIs, AI operations rely on access to privileged credentials and critical systems.
In reality, AI environments are not only operated by human users but also by Non-Human Identities (NHIs) such as AI agents, containers and service accounts. Compared to human identities, NHIs are especially difficult to monitor and secure in dynamic, automated pipelines. To reduce the risk of AI-enabled cyber attacks, organizations must secure every human and machine identity by enforcing least-privilege access, eliminating standing privileges and applying zero-trust security principles.
Continue reading to learn why identity security is essential to protecting against AI security risks and how Keeper® helps secure both human and non-human identities.
Why is identity security important?
AI-based systems rely on distributed infrastructure and elevated access to critical environments. Human users, including engineers, DevOps teams and data scientists, typically require elevated permissions to manage databases, Kubernetes clusters, GPUs and production workloads. However, internal employees aren’t the only human users who require access. Many organizations rely on third-party vendors who may be granted privileged access to help manage infrastructure or maintain underlying platforms, introducing external risk that must be tightly governed. Vendor Privileged Access Management (VPAM) addresses this risk by controlling and monitoring third-party access. VPAM focuses on securing and managing external access to internal systems by enforcing least-privilege access and maintaining auditable visibility.
While human identities are crucial to driving AI operations, NHIs like AI agents, service accounts, APIs and automation scripts are just as important. NHIs need privileged access and credentials to move data, deploy AI models and run automated pipelines. Unlike human users, NHIs typically operate continuously and at scale, making them high-value targets for cybercriminals. If any human or machine identity is compromised, the consequences can range from data theft and credential abuse to lateral movement and compliance violations.
Challenges with human and non-human identities
Managing identities in AI environments is challenging because organizations must secure both human users and automated processes across rapidly changing infrastructure. Human users often accumulate excessive permissions over time, especially in fast-paced environments where engineers and data scientists are granted standing access to speed up development.
As complex as access management may be for human users, NHIs introduce a different set of challenges. Service accounts, AI agents, scripts and APIs often rely on secrets hardcoded into scripts or embedded in containers, making them difficult to track, rotate or revoke. Teams tend to lack visibility into what NHIs are doing, which systems rely on them or what access they have. As AI infrastructure expands across multi-cloud environments, secrets sprawl becomes harder to control, and audit trails for automation are practically non-existent. Without proper oversight and controls, compromised identities can remain undetected, enabling unauthorized access to critical systems and AI models. To address this, behavioral analytics and continuous validation are important for detecting unusual activity across AI workflows, ensuring both human and machine identities act within approved access limits.
Best practices for securing identities across AI environments
To protect sensitive data and critical infrastructure, organizations must adopt an identity-first security strategy to secure both human and non-human identities. Here are some best practices for securing identities across every stage of the AI lifecycle:
- Apply least-privilege access: Limit access to only what’s necessary for each specific role or task, and continuously validate that access based on context to reduce the impact of a compromised identity.
- Use secrets management with automated credential rotation: Eliminate hardcoded credentials by securely storing them and automatically rotating them on a regular basis. This is crucial for machine identities that operate continuously across multiple pipelines.
- Enforce Role-Based Access Controls (RBAC) and Multi-Factor Authentication (MFA): Implement RBAC to define granular access policies by role or team, and require MFA for privileged actions across all systems to prevent unauthorized access.
- Monitor and record privileged sessions: Record session activity for all human and machine access to critical systems. This ensures accountability, helps detect abnormal activity and simplifies auditing.
- Eliminate standing privileges with Just-in-Time (JIT) access: Provision access and automatically revoke it once a task is completed. JIT access significantly reduces the window of opportunity for cybercriminals and supports zero-trust security.
- Integrate identity security into CI/CD: Embed secrets management and access controls directly into development workflows to ensure security doesn’t hinder automation in AI environments.
- Enforce temporary access for vendors with VPAM: Use VPAM to give third parties temporary, policy-based access to internal systems, ensuring their access is limited and traceable.
How Keeper secures human and non-human identities
Keeper is a zero-trust control plane for human and machine identities, built to meet the identity security demands of modern AI environments. Keeper protects every identity in an organization’s ecosystem with:
- Secrets management: Keeper securely stores and rotates secrets, including SSH keys, tokens and certificates, in an encrypted vault, ensuring no plaintext secrets are exposed.
- JIT access: Using Keeper, access is provisioned only when necessary, with automatic revocation to eliminate standing access, preventing credentials from becoming liabilities in fast-moving AI environments.
- Session recording: To support internal security policies and compliance requirements, all privileged sessions can be recorded and audited, providing full visibility into who accessed what and when.
- AI threat detection and response: KeeperAI identifies high-risk sessions and automatically terminates them, with full activity analysis and categorization.
- Multi-cloud environment coverage: Keeper supports access across AWS, Azure, GCP and on-premises environments, enabling centralized control even in distributed infrastructures commonly used in automated workflows.
- Developer tool integrations: Keeper supports many integrations with tools that power modern AI development, including Terraform, Kubernetes, SQL clients and more. These integrations ensure security is embedded in developer workflows without introducing friction.
- VPAM: For contractors and third-party collaborators, Keeper enforces time-limited, policy-based access with detailed audit trails, ensuring vendor activity is tightly controlled and fully monitored.
Protect every identity from AI security risks with Keeper
As AI adoption accelerates, every human and machine user introduces new security risks connected to unmanaged identities and unmonitored access if not properly secured. Traditional access management methods cannot keep up with the rapid expansion of modern infrastructure, which is why a modern identity security platform like Keeper is necessary. Keeper enables organizations to protect AI environments without slowing down operations, delivering zero-trust security that scales with fast-growing AI infrastructure.
Start your free trial of Keeper today to secure every identity in your modern environment.
