Ensuring Safe, Accessible AI Agents: Lessons Learned While Building Scout at Huntbase

A Practical Approach to Guiding AI-Driven Incident Response

Over the past few months at Huntbase, I’ve been immersed in building Scout, our new guidance system for AI agents and SOC Analysts. The goal for the agents we’ve explored is to assist security teams as they respond to incidents on their own networks—gathering additional key information, potentially quarantining systems, blocking malicious traffic, or reconfiguring services to contain threats. The promise is greater speed and efficiency in defending against attacks, but it also raises a crucial question:

How do we give these AI agents the freedom to be helpful without risking serious damage if something goes wrong?

Without proper safeguards, an AI agent given open access to an interface like REST API endpoints could make a single ill-timed request and trigger an outage or compromise critical data. To prevent that, I’ve looked to the practices and tools used in integration and API management—fields that have long navigated the tension between flexibility and control.

Guardrails for AI Agents: Drawing on Integration Principles

Observing the industry, platforms like tray.io and n8n have evolved from basic integration solutions to full-fledged “AI agent builders.” Both rebranding in the past few months with Tray acquiring the coveted AI TLD. Meanwhile, API normalization products like leen.dev (security) and WorkOS (hr/identity) provide a uniform API layer to multiple tools in a specialised vertical. By exposing a curated interface to your AI agents, you ensure they can access a broad range of services only through “safe” methods and routes. These same design principles are akin to the basic use of a network proxy for service access control.

This principle—limiting what an agent can do—fosters a stable, predictable environment. It’s the same idea integration platforms have relied on for years: simplify complexity, enforce consistent data handling, and prevent bad calls before they happen.

Learning from Interfaces Like Steampipe and OSQuery

I’ve also drawn inspiration from query-based interfaces such as Steampipe and osuery. Though not entirely integration platforms themselves, these tools employ SQL-like querying as an interface between source technology and data as structured tables. With them, you can allow an AI agent to explore and analyze data without granting the power to run destructive commands or make unauthorized changes.

This approach lets you tap into well-established permission systems as well. As your policies evolve, so do the AI agent’s capabilities—no need to create a bespoke governance model from scratch. You’re relying on proven patterns, adapted to the new demands of AI-driven security tasks.

Connecting the Dots: Why This Matters

In a previous blog post, I discussed the Crowdstrike outage—a stark reminder of how automation failures can ripple across the globe. Although unrelated to AI agents, it highlights the potential scale of automated missteps at the endpoint. In a security context, where an AI-driven action might isolate hosts or block IPs, even a small error can have big consequences.

By combining the insights from integration platforms and SQL-like interfaces, you could give your AI agents the freedom to assist without risking widespread operational havoc. They gain the ability to investigate threats, identify compromised endpoints, and recommend targeted responses, all within carefully chosen boundaries.

Staying Vigilant in a Growing AI SOC Landscape

With the influx of AI Security Operations Center (SOC) tools, it’s easy to be dazzled by new features. But it’s crucial to dig deeper. Ask how these products integrate with normalization layers, enforce structured querying, and handle fallback scenarios if the agent misinterprets a situation. Not every offering in the market has fully considered these guardrails, and vigilance now can save you headaches later.

At Huntbase, as we developed Scout, we found our stride by relying on established models that have already proven themselves. These frameworks give our agents room to be smart and helpful, without ever compromising the safety and integrity of the networks they protect.

Looking Ahead

As AI-driven security tooling continues to mature, I expect to see more defined best practices, standardized “safe” endpoints, and integration-friendly architectures. Until then, it makes sense to adopt the lessons we’ve learned from traditional integration platforms and query-based interfaces. In doing so, we ensure that Scout and other AI agents remain powerful assets, guiding our security teams effectively without ever putting critical systems at undue risk.

I encourage you to carefully consider what trusted interfaces already exist for the capabilities you want in your AI agent pipeline. How can you leverage those before spinning up a quick, low-code connector that might give your agent more access than you really need — or intend? By taking a moment to think through these questions, you’ll set a stronger foundation for secure, reliable AI-driven operations.