Enterprises moving quickly to deploy AI agents are running into a new vulnerability. The models themselves are becoming gateways to sensitive data.
This week, Y Combinator introduced Clam, a startup building what it describes as a semantic firewall. The system is designed to secure AI frameworks such as OpenClaw at the network layer, inserting a policy checkpoint between models and enterprise systems.
The company is entering the market at a moment when AI adoption inside corporations is accelerating faster than governance. AI agents now connect to internal databases, customer records, ticketing systems, and financial tools. Traditional security controls were not built for probabilistic systems that generate queries and synthesize data autonomously.
Clam’s argument is that AI traffic must be interpreted differently from human activity. A standard firewall can block an IP address. Identity systems can restrict credentials. But neither evaluates the semantic meaning of what a model is asking for or returning.
Its product inspects prompts, outputs, and tool calls in real time, applying policy controls before data moves across the network. The goal is to prevent leakage, unauthorized access, or unintended exposure of sensitive material.
Frameworks like OpenClaw illustrate the risk. Agent platforms orchestrate language models to retrieve data, call APIs, and execute multi step workflows. That autonomy improves productivity, but each tool invocation expands the attack surface. A single prompt injection or misconfigured retrieval chain could expose an entire internal knowledge base.
Security leaders are increasingly aware of the gap. Many enterprises began experimenting with AI through innovation teams rather than security departments. As those pilots transition into production systems, CISOs are confronting regulatory exposure tied to customer data, financial information, and intellectual property.
A separate AI security category is beginning to take shape. Startups are focusing on model monitoring, output filtering, red teaming, and prompt injection defense. Clam is positioning itself as infrastructure rather than a feature layered into individual applications.
That strategy carries strategic upside. If enterprises adopt multiple agent frameworks, a centralized enforcement layer could simplify governance. A standardized checkpoint for AI traffic may reduce operational complexity compared with embedding custom controls inside each deployment.
The technical burden is substantial. Determining whether a model output violates policy requires contextual understanding of sensitive data definitions, access rules, and business logic. Overblocking would frustrate users. Underblocking would create liability.
Clam’s founders, Anshul Paul and Vaibhav Agrawal, are betting that enterprises will prioritize prevention over speed. Historically, every major infrastructure shift has triggered a parallel security cycle. Cloud computing drove cloud security. Mobile computing created mobile threat defense. AI systems are now generating a new perimeter challenge.
The broader strategic question is whether semantic controls become embedded within model providers or remain an independent layer managed by enterprises. For now, the demand signal is strong. Organizations want the productivity gains of AI agents without the uncontrolled risk.
Clam’s launch reflects a growing consensus that intelligence deployed at scale requires guardrails built for meaning, not just access.