AI agent sandbox alternative

Sandboxing is necessary, but not sufficient.

A sandbox answers where an agent can run. Enterprise agent security also needs to answer who the agent is, what it can access, what data it can see, what needs approval, and what happened.

Why sandbox-only products miss the control plane

Sandboxing reduces the execution blast radius, but it does not automatically create accountable agent identities, scoped permissions, approval routing, secret redaction, PR evidence, compliance retention, or revocation workflows.

For coding agents, the useful product is not just a safer box. It is a policy boundary around files, commands, network, Git, cloud tools, databases, and SaaS integrations.

What an AI agent security platform should include

Identity

Agent IDs, owners, sessions, risk tiers, lifecycle, expiration, and revocation.

Runtime enforcement

Filesystem, terminal, network, Git, database, cloud, browser, and SaaS controls.

Data protection

Secret detection, PII detection, redaction, model-routing policy, and unknown egress controls.

Audit evidence

Append-only logs, PR reports, approval trails, security review artifacts, and compliance history.

The Securie direction

Securie starts with the secure runtime for AI coding agents because that is where immediate risk is concentrated. The long-term platform is a zero-trust control plane for enterprise agents across developer, DevOps, browser, database, SaaS, support, sales, finance, HR, security, and workflow contexts.

Move from sandbox-only to identity-aware runtime security.

Start with the checklist