Hush Security says the AI security problem has shifted from protecting models to governing identities as autonomous agents spread

AI security problem has shifted from protecting models to governing identities as autonomous agents spread">

Less than a year after emerging from stealth to tackle non-human identity security, Israeli cybersecurity startup Hush Security believes the enterprise AI security conversation has fundamentally changed. The company, which earlier this week announced a $30 million Series A round led by returning investors Battery Ventures and YL Ventures with Akamai Technologies joining as a strategic investor, argues that organizations are rapidly moving beyond experimenting with generative AI assistants and into deploying autonomous software agents that require an entirely different security model. While the funding will help expand engineering, U.S. sales and enterprise integrations, Hush is framing the announcement primarily as evidence that identity—not models—is becoming the critical control plane for enterprise AI. "The discussion has moved incredibly fast," CEO and co-founder Micha Rave told VentureBeat in a video call interview following the funding news. When Hush launched last year, the company's focus was securing non-human identities—API keys, service accounts, machine credentials and other identities used by software rather than people. Since then, Rave says, customers have increasingly asked a different question: how do they safely allow AI agents to operate inside production systems? This is a pertinent and urgent question ever since Hugging Face revealed in mid-July it was hacked by an autonomous AI agent , later identified as an OpenAI test agent running internally that escaped its secure sandbox, powered in part by an unreleased model. According to Gartner figures cited by the company, the average Fortune 500 organization could be running more than 150,000 AI agents by 2028 , compared with fewer than 15 only a year earlier. Hush also points to Omdia research suggesting that 96% of organizations are relying on governance models that were never designed for autonomous AI agents. From machine identities to autonomous software The company's original thesis was that enterprises had accumulated thousands of long-lived machine credentials that were difficult to rotate, audit and secure. Rather than relying on static secrets, Hush developed an identity-based system that brokers short-lived, policy-driven access for machines. Rave says AI agents amplify that same problem. "Software now acts autonomously, on its own initiative, inside your most sensitive systems," he said. "AI agents need strict identity, not just API keys." Unlike traditional automation, AI agents frequently act across multiple enterprise systems, invoke external services, make decisions independently and often execute actions using the permissions of the human who launched them. In practice, organizations often grant an agent broad OAuth permissions or administrator credentials simply to enable it to complete tasks. That creates what Hush describes as an identity problem rather than simply an AI problem. During the interview, Rave said virtually every security leader he speaks with faces the same dilemma: either slow AI adoption until appropriate controls exist or allow employees to connect new agents directly into corporate systems despite limited governance. "The answer," he said, "is that they let everything in. You cannot stop innovation in the name of security." Identity becomes the control point Rather than treating AI agents as another application requiring credentials, Hush is extending its existing non-human identity platform into what it calls an "Identity Gateway" for AI agents. The platform sits between agents and enterprise resources, allowing organizations to discover agents, assign each one its own identity, associate it with a responsible human owner, broker task-specific permissions at runtime and maintain centralized audit logs. Instead of allowing an agent to inherit all of a user's privileges indefinitely, Hush attempts to enforce what it calls "least agency"—granting only the permissions necessary for the specific task being executed. The company says every action can be logged, attributed and revoked from a single control plane, while administrators retain the ability to terminate an agent's access immediately if necessary. This represents a broader shift in enterprise identity management. Human identities have long been governed through identity providers, single sign-on and privileged access management systems. Machine identities have increasingly received similar attention as organizations modernized cloud infrastructure. Hush argues autonomous AI agents now represent a third identity category requiring dedicated governance. Hush has not publicly posted its pricing for the Identity Gateway solution. Governing every kind of enterprise agent Hush says enterprises are no longer dealing with a single category of AI software. During the interview, Rave described three broad classes emerging inside organizations: Desktop coding assistants and productivity agents such as Claude, Cursor and VS Code integrations. Enterprise AI platform agents running on services such as Microsoft Foundry, Salesforce Agentforce or AWS AgentCore. Custom agents organizations build internally for business processes or customer-facing applications. Each introduces different governance challenges, but all ultimately require controlled access to enterprise systems. The problem, according to Hush, is that many agents currently authenticate using inherited human credentials or long-lived API keys, making it difficult to determine whether an action originated from a person or from an autonomous system acting on that person's behalf. "If I see something in the Salesforce logs," Rave said during the interview, "did the user do that, or was it the agent the user was using?" That attribution challenge becomes increasingly significant as organizations begin deploying multiple autonomous systems capable of initiating actions without direct human approval. Existing identity tools weren't designed for AI agents Rather than replacing identity providers or secrets managers, Hush positions itself as filling a gap between them. Traditional IAM platforms authenticate employees. Secrets managers store credentials. Neither, the company argues, governs the runtime behavior of autonomous software acting on behalf of humans across multiple systems. Hush says its platform continuously discovers known and shadow agents across enterprise environments, assigns ownership, brokers just-in-time credentials and records every interaction in a centralized audit trail. According to its product documentation, organizations do not need to modify their existing agents because the platform operates by brokering access requests rather than changing application logic. That identity-first approach is attracting customers already deploying enterprise AI initiatives. IT infrastructure services provider Kyndryl says it has deployed Hush internally and has begun offering the platform to enterprise customers. "Our collaboration with Hush is rooted in a shared security philosophy: identity is the ultimate control point for the modern agentic workforce," said Adeel Saeed, senior vice president and CTO for Global Cyber Resiliency at Kyndryl, in a prepared statement. Akamai's participation in the funding round similarly reflects what the company sees as an architectural rather than incremental shift. "AI agents are driving the next transformation, and identity is the piece most companies haven't solved yet," said Ramanath Iyer, Akamai's chief strategist. Security priorities are moving beyond the model itself The broader AI security market has spent the past two years focused largely on prompt injection, model vulnerabilities, jailbreaks and LLM safety. Those remain active research areas, but enterprise deployments increasingly face operational questions around what autonomous systems are permitted to access and how those actions can be governed. Hush argues that identity is becoming the enforcement layer for answering those questions. Rather than asking whether an AI model can safely generate code or summarize documents, enterprises increasingly need to determine which systems an agent may access, whose authority it exercises, how permissions are delegated, and how every action can be traced back to an accountable owner. Whether Hush's identity-centric approach becomes the dominant model remains to be seen. But as enterprises move from experimenting with AI assistants to deploying thousands of autonomous software agents, the company is betting that the next major security challenge won't be securing the models themselves—it will be securely managing the identities of the software acting on their behalf.