NewCore secures $66M to solve AI agent identity crisis in enterprises

Introduction

NewCore just closed a $66 million funding round to address what it calls the next big enterprise security challenge: managing AI agents, not human employees. The startup believes companies will soon need identity systems for autonomous software agents that act like digital workers.

What Happened?

The Series A round positions NewCore to build identity and access management systems specifically for AI agents. Traditional enterprise security focuses on human employees accessing company systems. NewCore argues this model breaks down when software agents autonomously perform tasks, make decisions, and interact with sensitive data without human oversight.

Companies already deploy AI agents for customer service, data analysis, and workflow automation. These agents operate with varying levels of autonomy, from simple chatbots to complex systems that can modify databases, send emails, and trigger business processes. Most organizations treat these agents as applications rather than entities that need proper identity management.

NewCore's platform assigns unique identities to each AI agent, tracks their actions, and applies access controls based on their specific roles and responsibilities. The system monitors agent behavior for anomalies and can revoke permissions when agents act outside their defined parameters. This approach mirrors how companies manage human employee access but adapts the controls for software entities.

The funding round included participation from several enterprise security investors, though NewCore has not disclosed specific investor names or the exact valuation. The company plans to use the capital to expand its engineering team and build partnerships with major cloud providers who host AI agent infrastructure.

The Impact

The rise of AI agents creates new security blind spots for enterprises. Unlike human employees who can be trained on security protocols, AI agents operate based on their programming and training data. They can potentially access vast amounts of company information without the natural limitations that govern human behavior.

Current identity management systems assume a human is behind every action. When an AI agent performs thousands of operations per minute across multiple systems, traditional audit trails become meaningless. Security teams struggle to understand which agent performed which action and whether that action was appropriate.

The market for AI agent management could grow rapidly as more companies deploy autonomous software. Research firm Gartner predicts that 30% of enterprise applications will include AI agents by 2027, up from less than 5% today. This shift will force security teams to rethink fundamental assumptions about identity, access, and audit controls.

How to Avoid This

Organizations deploying AI agents should start thinking about them as digital employees rather than simple tools. Each agent needs defined roles, specific access permissions, and clear boundaries around what it can and cannot do. Companies should document which systems each agent can access and regularly review these permissions.

Security teams should implement logging systems that can track AI agent actions separately from human user activities. This means tagging agent-generated events with unique identifiers and maintaining audit trails that show the chain of decisions leading to each action. Traditional user activity monitoring tools may need updates to handle the volume and patterns of AI agent operations.

Businesses should also establish governance frameworks for AI agent deployment. This includes approval processes for new agents, regular reviews of existing agent permissions, and incident response procedures when agents behave unexpectedly. The goal is treating AI agents with the same security rigor applied to human employees while accounting for their unique operational characteristics.