Let’s be real. Most companies don’t know the full extent of identities operating in their IT environment.
With identity sprawl and complexity constantly increasing, it’s very common for IT ops and security teams to feel overwhelmed. They can’t effectively manage or secure what they don’t know.
Identities aren’t limited to human users. They extend to a variety of non-human identities (NHIs), including machine and AI identities, which play a crucial role in automated processes and AI-driven tasks. These identities aren’t new, but a majority of organizations don’t yet have a handle on securing them.
Delinea’s platform helps you manage and secure four primary types of identities:
- IT admin identities
- Workforce identities
- Developer identities
- Machine and AI identities
Let’s dive into the complex world of machine and AI identities so you understand the risks and see how you can mitigate them.
What are machine and AI identities?
The term machine identity refers to the digital identities of devices, workloads, and AI.
- Devices encompass IoT and operational technology devices.
- Workloads include cloud workloads, containers, and APIs.
- AI identities leverage other machine identities to interact with systems and perform tasks autonomously.
To gain privileged access, machine identities authenticate through services accounts or leverage credentials such as SSH keys, API keys, certificates, and OAuth tokens. These credentials should be managed centrally for ongoing oversight, similar to password management.
The rise and risks of machine and AI identities
Machine identities proliferate due to the rise of automation in DevOps and AI workflows. With a ratio of 46:1 machine identities to human identities in the average enterprise, it's no surprise that organizations are increasingly concerned about machine identity risk.
Compromised machine identities can provide access to automated systems and sensitive data, undermining their integrity and security.
The rise of Agentic AI—AI systems capable of making independent decisions without human intervention—is increasing the use of machine identities and expanding the attack surface at many organizations. AI agents can execute tasks, interact with systems, and even create and manage credentials, posing significant risks if compromised.
With the right access, these powerful agents can be weaponized. Attackers can leverage AI agents to mix harmful or deceptive data into datasets used to train machine learning models, a process known as “AI poisoning.” If poisoned, AI agents could be manipulated to bypass security controls, escalate privileges, or exfiltrate data.
Should a breach or cyberattack occur, organizations may have to pay regulatory fines due to non-compliance, lose the trust of their customers, partners, and investors, and suffer disruption to business operations.
Challenges in securing machine and AI identities
As organizations increasingly rely on automated processes and AI-driven tasks, the complexity of managing machine and AI identities grows. Organizations often face difficulties in discovering and understanding the full scope of machine identities, including their dependencies and interactions.
Machine identities often have short lifespans and require frequent updates, renewals, or deactivations. Users may not know what access machine identities need, resulting in excessive access to data and business systems beyond their requirements. Without proper human oversight, machine identities can be provisioned incorrectly and easily become orphaned.
How can you secure machine identities?
Understanding and securing machine and AI identities is crucial for maintaining the integrity and security of automated systems.
With a modern solution designed for both human and machine identities, you can address six components of identity security with consistent policies and a common interface:
1. Discovery and inventory
Automatically discover and inventory secrets, accounts, and credentials across on-premises, cloud environments, and applications. This ensures that all machine identities are accounted for, reducing the risk of unmanaged or orphaned credentials that could be exploited by malicious actors.
2. Identity posture and threat analysis
By auditing and monitoring all machine identity access, you can detect and remediate threats in real time. Continuous monitoring allows for the identification of suspicious activities and potential breaches, enabling swift responses to mitigate risks.
3. Protected credentials
Securely vault machine identities and rotate them as needed. By eliminating static secrets and migrating to dynamic or ephemeral credentials, you can significantly reduce the attack surface and enhance your security posture.
4. Privileged secure access
Ensure that all machine-to-machine communications are authenticated and authorized with fine-grained access control, preventing unauthorized access and ensuring that only legitimate interactions occur. This is crucial for maintaining the integrity and confidentiality of data exchanged between machines.
5. Zero standing privilege
Enforce consistent least privilege and just-in-time access for all machine identities, including AI identities. This approach minimizes the risk of privilege escalation and ensures that machine identities have only the access they need when they need it.
6. Identity lifecycle and governance
Automate the provisioning, deprovisioning, and governance of machine identities including AI identities. This automation streamlines identity management processes, reduces administrative overhead, and ensures compliance with security policies and regulations.
Securing machine and AI identities in the Delinea Platform
The Delinea Platform enables you to secure each identity with seamless, intelligent, centralized authorization at every interaction without sacrificing productivity. Streamlined administration saves you time and improves security by eliminating data silos with a single source of truth across teams. Learn more about the Delinea Platform and get a personalized demo.