HiddenLayer raised $100 million in a Series B round, led by Delta-v Capital with participation from Microsoft’s M12, Morgan Stanley and Ten Eleven Ventures. The cash will fuel its AI runtime security platform as enterprises push generative AI and autonomous agents into production.
The attack surface around AI models is exploding. Companies that once ran proof-of-concept notebooks now embed large language models and agentic workflows into customer-facing services, internal decision pipelines and even defense-grade systems. Every new endpoint—prompt interface, tool integration, model version—offers a foothold for adversaries, and the market for tools that defend those endpoints is outpacing the technology itself.
Why AI security moved from theory to necessity
Three years ago most security analysts treated AI-specific threats as a research curiosity. The idea that adversarial attacks would scale was debated more than acted upon. Gartner now forecasts enterprise spending on AI security tools will hit $2.83 billion this year, an 83 percent jump from last year, and climb toward $4.78 billion by next year. Those numbers show a shift from experimental pilots to mission-critical deployments and signal that organisations realize a compromised model can cost far more than preventative tooling.
HiddenLayer’s growth mirrors that trend. CEO Chris Sestito says the company’s annual recurring revenue has multiplied more than tenfold in the past twelve months, now sitting in the “tens of millions” range. Over 90 percent of that increase comes from new customers—financial institutions, large tech firms, the U.S. Department of Defense and the intelligence community. One undisclosed client, which supplies a frontier model to more than 700 million weekly users, has also signed on, underscoring the breadth of the problem.
From model hardening to protecting autonomous agents
Traditional machine-learning security focused on securing the training pipeline and detecting adversarial inputs that subtly distort predictions. HiddenLayer still covers those basics—discovery, runtime protection, attack simulation and supply-chain checks—but it now tackles the unique risks of generative AI.
- Prompt injection – Malicious inputs that coerce a model into executing unintended commands or revealing confidential data. The platform sanitises prompts before they reach the model and monitors output for policy violations.
- Agent manipulation – Autonomous agents that chain tools, APIs and data sources can be hijacked mid-execution, causing harmful actions or data leaks. HiddenLayer injects policy checks at each workflow step, aborting actions that breach defined rules.
- Malicious tool use – AI agents increasingly rely on external plugins, code interpreters or retrieval modules. The solution watches these integrations for anomalous behaviour, such as a retrieval module returning unexpected payloads.
Open-source and open-weight models present a particular headache. Because the model files are freely distributable, threat actors can embed hidden code or malicious weights that execute once the model loads. HiddenLayer parses and scans roughly 50 AI file formats, looking for “hidden models inside of models” that could act as backdoors. This supply-chain defence stops compromised artifacts before they ever run in production.
How the new capital will be spent
HiddenLayer will allocate the $100 million to three thrusts:
- Sales and distribution – Transform a niche go-to-market effort into a broader enterprise sales engine, reaching sectors just beginning to adopt AI at scale.
- Engineering research – Double down on detection logic for emerging attack vectors, such as multi-modal prompt injection and cross-agent coordination attacks.
- Geographic expansion – Build a presence in Europe, the Middle East and Africa (EMEA), where regulatory pressures around AI transparency and security are tightening.
Sestito likens HiddenLayer’s offering to “Endpoint Detection and Response (EDR) for AI.” Just as EDR agents watch operating-system processes for malicious activity, HiddenLayer’s runtime guard watches model inference calls, tool invocations and data flows for policy breaches.
The counter-point: could the cloud giants swallow the niche?
The company acknowledges that large cloud providers—AWS, Azure, Google Cloud—could embed similar runtime protections into their managed AI services. If a cloud vendor offers built-in prompt-injection filters or agent-policy enforcement, enterprises might see less value in a third-party add-on.
Sestito argues that governance, identity and policy controls will stay complex enough to justify specialised solutions. “When you’re dealing with multi-tenant, high-value workloads that cross organisational boundaries, a one-size-fits-all cloud offering can’t address every compliance nuance,” he says. He believes a dedicated security layer can move faster than the massive product cycles of the hyperscalers, and that deep integration with existing security information and event management (SIEM) tools will keep the platform relevant.
What to watch in the coming months
- Adoption velocity – If hidden-layer security tools become a prerequisite for AI procurement contracts, enterprise licences could surge, especially in regulated sectors.
- Competitive moves – Watch for announcements from major cloud providers about native AI runtime hardening. A bundled offering could force hidden-layer to pivot toward hybrid-cloud or on-premises deployments.
- Regulatory pressure – As governments draft AI-specific security standards, compliance requirements may explicitly reference runtime protection, giving vendors like HiddenLayer a regulatory tailwind.
- Threat evolution – New attack techniques—coordinated prompt injection across multiple agents or supply-chain attacks that poison model weights at scale—will test any detection engine. The speed at which HiddenLayer adapts its detection models will be a key differentiator.
Bottom line
HiddenLayer’s $100 million raise shows the market for AI-specific runtime security has moved from academic debate to commercial imperative. As enterprise AI expands beyond isolated models into complex, tool-rich agents, continuous, policy-driven protection becomes as essential as traditional endpoint security. Whether the startup stays ahead of sophisticated adversaries and the security arms race among cloud giants will decide if it remains the “EDR for AI” or becomes another feature folded into a larger platform. The next quarter will reveal whether dedicated AI security vendors can carve out a lasting niche in an ecosystem still defining its own boundaries.