The AI Gold Rush is lit. But without identity-first security, all deployments are open doors. Most organizations protect native AI like web apps, but act like junior employees with root access and no manager.
From hype to high stakes
Generated AI has moved beyond the hype cycle. The companies are:
Automating customer service workflows with AI agents that integrate AI agents to accelerate deployment software development for LLM Copilots
Whether you’re connecting to a building with an open source model or a platform like Openai or humanity, your goal is speed and scale. But what most teams miss is this:
All LLM access points or websites are new ID edges. And all integrations add risk, unless identity and device attitude is forced.
What is the AI Build and Purchase Dilemma?
Most companies face crucial decisions:
Build: Create internal agents tailored to internal systems and workflows Purchase: Adopt commercial AI tools and SaaS integration
The threat surface doesn’t care about the path you choose.
Custom build agents extend the internal attack surface, especially when access control and identity segmentation are not enforced at runtime. Third-party tools are often misused or accessed by business users with personal accounts where governance gaps exist, or more generally, fraudulent users.
Protecting AI isn’t about algorithms, it’s about who (or which device) is talking, what interactions unlock.
What is actually at risk?
AI agents are agents, and are able to act on behalf of humans and access data like humans. They are often incorporated into business systems that include:
Source Code Repository Finance and Payroll Applications Email Inbox CRM and ERP Platforms Customer Support Logs and Case History
When a user or device is compromised, the AI agent becomes sensitive data through a high-speed backdoor. These systems are highly privileged, and AI amplifies attacker access.
Common AI-specific threat vectors:
Identity-based attacks such as Credentials stuffing and Sessions The LLM API targeting Hijacking targets has excessive permissions and the wrong agent with scoped role-based access control (RBAC) is inconsistent in weak sessions where infected or unstable devices require privileged actions via LLM
How to Secure Enterprise AI Access
To eliminate the risk of AI access without killing innovation, you need to:
Granular RBAC for all users and devices accessing the Phishing-ResistantMFA LLMS or Agent API is associated with a business role.
AI access control must evolve from a one-time login check to a real-time policy engine that reflects current identity and device risk.
Secure AI Access Checklist:
No Shared Secrets Trusted Device Assumptions Over-Free Agent No Productivity Tax
Fix: Protect your AI without slowing down
There’s no need to swap security for speed. With a proper architecture, you can:
Eliminate trust assumptions on all layers that block unauthorized users and devices by default.
Beyond identity, it makes this possible today.
Beyond the IAM platform of identity, unauthorized access to AI systems is not possible by enforcing device recognition and continuous access control that can withstand AI systems phishing. There is no password. There are no shared secrets. There are no devices that are unreliable.
Prototype the secure design architecture of an in-house AI agent that combines the user ID and device attitude beyond identity, verifying the agent’s authority, enforces RBAC at runtime, and continuously evaluates risk signals from EDR, MDM and ZTNA. For example, if an engineer loses full disk access for Cloud Strike, the agent will immediately block access to sensitive data until the attitude is corrected.
Do you want to see it?
Sign up for Beyond Identity webinars to explore how the global head of IT security has built and secured the internal enterprise AI agent currently used by more than 1,000 employees. You can see a demonstration of how one of Fortune’s fastest growing companies can use access controls bound to phishing-resistant devices to make unauthorized access impossible.
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