Runlayer Raises $11M to Secure AI Agents for Business Users

AI-driven agents are becoming a $10B cybersecurity risk overnight. Runlayer, led by three-time founder Andrew Berman, just closed $11 million in funding from investors including Keith Rabois, Felicis, and 8 unicorn backers in November 2025.

This startup tackles a hidden operational crisis: how enterprises ensure their business users’ AI agents run securely without constant IT supervision. The funding accelerates deployment of Runlayer’s control system to monitor, assess, and remediate AI agent activity across organizations.

Runlayer’s approach flips the AI security problem by enabling enterprises to delegate AI tasks while retaining oversight through automated guardrails—a move that shifts the primary constraint from manual human intervention to scalable, programmable security policies.

For CIOs and security teams, this creates a leverage point where one system enforces compliance and safety across thousands of autonomous AI agents, unlocking faster AI adoption without multiplying risk.

How Runlayer Automates Security Over Autonomous AI Agents

Unlike traditional software, AI agents act independently on behalf of business users—running workflows, making decisions, accessing sensitive data. This autonomy exponentially increases the attack surface and risk vectors.

Runlayer builds a monitoring and control layer tailored specifically for these AI agents. Its system automatically assesses AI-driven actions in real time, flagging anomalies and enforcing corporate policies without waiting for IT ticket queues.

This mechanism reduces the cybersecurity bottleneck: instead of scaling IT teams linearly with AI agent growth, Runlayer’s automated security policies scale almost instantaneously, applying consistent rules across diverse AI workflows.

For example, if an AI agent tries to access unauthorized SaaS data, Runlayer intercedes immediately—without human approval—reducing response times from hours or days down to milliseconds.

Positioning Runlayer to Exploit a Fast-Growing Security Constraint

AI adoption among business units has been outpacing centralized IT security controls. Companies hesitate to unleash AI agents broadly due to fears of data leaks, compliance breaches, or uncontrolled automation.

Runlayer repositions this bottleneck. Instead of halting AI adoption or dedicating costly human resources, it transforms the constraint into a programmable system boundary.

This shift mirrors emerging trends where automation becomes the security gatekeeper, similar to what companies like ClickUp did in embedding AI assistance for workflows, except Runlayer tackles security—arguably a higher-stakes domain.

The $11 million raise—backed by luminaries like Keith Rabois and Felicis—is not just capital but an endorsement of this repositioning. It clears Runlayer’s main constraint: go-to-market speed and system maturation needed to lock down enterprise AI environments.

Why This Means a Step-Change in AI Adoption and Security Leverage

Enterprises using thousands of AI agents face an exponential growth in manual oversight costs. If security teams must review each AI action, headcount must grow in direct proportion—quickly becoming untenable.

Runlayer’s automated governance layer flips this: 1 security operator can oversee 1,000+ decentralized AI agents with real-time policy enforcement. This compresses the necessary human capital and makes enterprise AI safe and scalable.

This is not just cost-saving. It enables new AI use cases previously considered too risky, accelerating automation in finance, HR, legal, and beyond.

This security lever unlocks compounding AI value. The more AI agents deployed safely, the more workflows can be automated—and the more data points Runlayer uses to refine its control algorithms, creating a positive feedback loop.

Runlayer’s model contrasts with general-purpose cybersecurity firms that rely on reactive alerts requiring human triage. By embedding security into AI agents’ operational flow, Runlayer creates a proactive control layer that works without constant human intervention.

Operators should compare this to how OpenAI reduced entrepreneur workloads by cutting friction in AI usage, except here the friction removed is in security scaling—a far less visible but more critical factor.

Specific Examples of AI Security Leverage in Action

Consider a financial services firm deploying 500 AI agents handling client data requests. Previously, IT had to audit logs manually, which took weeks.

Runlayer installs a continuous compliance system where deviations are blocked and flagged in real time—dropping audit response time from weeks to minutes.

In a legal department, AI agents drafting documents can unintentionally expose sensitive clauses. Runlayer’s system detects risky language patterns automatically, enforcing policies across every document generated without adding review steps.

This mechanism removes what used to be a human bottleneck, reshaping the constraint from “human security capacity” to “scalable software security policies.”

That’s why this startup’s $11 million raise signals more than funding: it’s funding a new kind of operational leverage where AI scale meets security scale.

Operators interested in AI automation’s future should track Runlayer’s progress closely. Its approach reveals a crucial but often overlooked puzzle piece—how to deploy AI agents at enterprise scale without multiplying human supervision exponentially.

Check how this fits with emerging AI operational trends like AI-powered systems that cut human staffing needs and platform plays embedding AI assistants across workflows.

Related Tools & Resources

In complex enterprise environments where AI agents require coordinated oversight, platforms like Copla become invaluable. By streamlining standard operating procedures and process documentation, Copla helps operational teams enforce consistent policies—complementing the automated security governance highlighted in this article. For businesses aiming to maintain control and compliance amid rapid AI-driven workflow expansion, Copla offers practical operational leverage. Learn more about Copla →

Full Transparency: Some links in this article are affiliate partnerships. If you find value in the tools we recommend and decide to try them, we may earn a commission at no extra cost to you. We only recommend tools that align with the strategic thinking we share here. Think of it as supporting independent business analysis while discovering leverage in your own operations.

Frequently Asked Questions

What are AI-driven agents and why are they considered cybersecurity risks?

AI-driven agents are autonomous software programs that perform tasks on behalf of business users. They increase cybersecurity risks significantly by expanding the attack surface, as they make independent decisions and access sensitive data without constant IT supervision.

How does automation improve cybersecurity oversight of AI agents?

Automation allows security policies to scale instantly across thousands of AI agents without multiplying the IT security team linearly. For example, Runlayer's system enforces real-time automated guardrails, reducing incident response times from hours or days to milliseconds.

What role does Runlayer play in securing enterprise AI use?

Runlayer provides a control system that monitors, assesses, and remediates AI agent activities automatically. It enables enterprises to delegate AI tasks while maintaining security compliance, allowing one security operator to oversee over 1,000 decentralized AI agents with consistent policy enforcement.

Why is manual human intervention a bottleneck in AI agent security?

Manual oversight requires security teams to review each AI action, causing headcount and costs to grow proportionally with AI agent deployment. This bottleneck makes scaling AI adoption untenable without automated systems like Runlayer's.

What benefits does automated AI security offer to industries like finance or legal?

Automated AI security reduces audit times dramatically—for instance, from weeks to minutes in financial firms—and enforces policies to prevent exposure of sensitive information in legal documents without increasing manual review steps.

How much funding did Runlayer raise to develop its AI security platform?

Runlayer recently closed an $11 million funding round led by investors including Keith Rabois and Felicis, enabling faster go-to-market speed and system maturation to secure enterprise AI environments.

What challenges do enterprises face without AI agent security automation?

Without automation, enterprises risk uncontrolled AI workflows leading to data leaks or compliance breaches, and must scale human security teams disproportionately, increasing costs and slowing AI adoption.

How does Runlayer's approach differ from traditional cybersecurity solutions?

Unlike reactive cybersecurity firms relying on human triage, Runlayer embeds proactive security into AI agents' operations, enforcing real-time policies automatically without waiting for IT ticket queues, thus reducing security bottlenecks.