Beyond Identity’s New Ceros Platform: How to Securely Run Autonomous AI Agents at Enterprise Scale

Autonomous and agentic AI are shifting from demos into core workflows such as code deployment, finance approvals, incident response, and customer operations.[7] Gartner projects that by 2028 one‑third of enterprise applications will include agentic AI and 15% of day‑to‑day work decisions will be made autonomously.[7]

As agents gain power to spin up tools, call internal APIs, and run for long periods without humans, the attack surface expands and traditional security and identity controls fall short.[2]

💡 Key takeaway: Enterprises need infrastructure‑grade controls for AI agents before trusting them with revenue‑generating and mission‑critical workflows.[5]

Why Enterprises Need a Dedicated Platform to Secure AI Agents

As AI agents plug into CRMs, CI/CD, and ticketing systems, basic security questions are often unanswerable:[2][6]

• Which user initiated an AI action?
• Which device or container did the agent run on?
• What internal data did it access?
• What did it do over time?

This undermines governance, incident response, and audits.[2][6]

Traditional IAM assumes:[2]

• Short, human‑driven sessions
• Clear login / logout boundaries
• Limited tool chaining

Agentic systems instead:[2][5]

• Run for hours or days
• Chain across many tools and services
• Act on delegated permissions long after the human is gone

Session‑based controls do not capture this continuous, tool‑hopping behavior.[2][5]

📊 Key point: Best practice now combines pre‑deployment governance with continuous runtime oversight of agent behavior and access.[5]

Forward‑leaning security teams treat AI agents as critical infrastructure:[5][8]

• Maintain an inventory of agents
• Map agents to owners and business processes
• Document which systems and data each agent may touch

Without this discipline, enterprises face new risks:[1][2][5][6]

• Data exfiltration via prompt injection or misconfigured connectors[2][6]
• Shadow agents deployed outside security’s view[1][5]
• Over‑privileged access to tools, models, and production data[6]
• Non‑attributable actions without provable identity[2]

These gaps drive demand for identity‑centric, runtime‑aware security platforms for autonomous AI.

Inside Beyond Identity’s Ceros Platform and AI Security Suite

Beyond Identity’s Ceros platform acts as a control plane for AI agents.[1] Security teams can:

• Decide which agents may launch
• Enforce policies at the process level for every session
• Constrain each agent to a tightly scoped authorization envelope (e.g., “triage internal tickets” rather than “access all Jira and GitHub”).[1]

⚡ Key capability: Ceros restricts tasks, applications, and systems an agent can access, then degrades or terminates sessions that violate policy while emitting real‑time alerts.[1]

Beyond Identity’s AI Security Suite introduces “real identity” for AI:[2]

• Every agent gets a cryptographic credential bound to the hardware of its device, VM, or container
• Identities cannot be copied or shared
• Every request has forensic proof: which identity, on which device, did what, under which permissions.[2]

Ceros is part of the Identity Defense Platform, which already delivers passwordless, device‑bound authentication and continuous device posture checks to stop credential‑based attacks on humans.[3] The same model now governs non‑human identities and agents, creating a single control plane across users, machines, and agents.[3]

💡 Visibility highlight: Ceros shows:[1]

• Which end user owns an agent
• The device and applications it uses
• Which AI models, inference engines, MCP servers, and tools it accesses
• Whether unapproved “shadow” agents are present

It can then block, limit, or transparently replace unsanctioned tools with approved equivalents.[1]

Enterprise Deployment Playbook: Safely Scaling AI Agents with Ceros

A pragmatic rollout starts with discovery:[5][8]

• Inventory existing agents
• Map them to business owners and processes
• Standardize ownership, access reviews, and escalation paths

With this model in place, Ceros can enforce guardrails as AI moves from pilots to core workflows.[5]

Next, combine posture and runtime controls:[1][3][4]

• Use Beyond Identity’s device‑trust and phishing‑resistant authentication so only healthy, trusted devices can launch agents[3][4]
• Let Ceros continuously evaluate arguments, tool use, and behavior, revoking or downgrading access if risk rises mid‑session[1][4]

⚠️ Security pattern: Keep agents narrow and purpose‑built, then enforce that scope at runtime so they cannot quietly pivot into adjacent systems.[1][6]

Design agents with least‑privilege access to:[1][6]

• Only required tools and APIs
• Only relevant datasets
• Only approved workflows and environments

Use Ceros to block agents from pushing code or data directly into production without explicit approval or human review.[1][6]

A real‑world pattern: Monolithic Power Systems built a private AI platform to protect IP and avoid public LLM exposure, now used by 1,000+ employees.[4] They pair internal agents with passwordless, phishing‑proof access and continuous device posture checks, blocking risky devices in real time.[4] Combined with Ceros’ process‑level controls, similar enterprises can broadly adopt agents without losing control over sensitive designs, models, or customer data.[1][4]

Ongoing governance should include:[1][5]

• Reviewing Ceros telemetry for shadow agents or anomalous tool chains[1]
• Detecting configuration drift as teams iterate on agents[5]
• Updating policies as new models and tools appear[1][5]

💼 Operational reality: Agent security becomes a standing discipline, similar to vulnerability management or identity governance.[5]

Conclusion: Making Agentic AI Safe Enough for Core Workflows

As agentic AI embeds in revenue, operations, and security processes, enterprises must adopt identity‑centric, runtime‑aware governance rather than ad‑hoc controls.[5][7] Ceros and the AI Security Suite provide cryptographic identity, process‑level enforcement, and deep visibility so agents can operate autonomously without forfeiting control or exposing sensitive data.[1][2]

Now is the time to act: identify where agents already run, close gaps in ownership and observability, and consider a Ceros proof of concept.[1][2][3] That foundation—provable AI identity, fine‑grained runtime controls, and unified governance for humans and agents—creates a safe path to scale agentic AI across your most critical workflows.[1][2][3]