Can it remember accepted risks and false positives to cut noise?

Yes. Agents record durable observations, accepted exceptions, known false positives, prior determinations, and read them back during triage, so they stop re-flagging risks the team already characterized.

What about the sensitivity of security data?

Memory contents live in your data store, which is the security boundary. Record only what your policies allow; Glen is org-scoped with provenance on every observation so you can see who recorded each determination.

Shared memory for security engineers

Security engineers hold a constantly shifting model of risk: the accepted exceptions, the known weak spots, the threat patterns you have seen before, the reasons a control is configured the way it is. As security teams adopt AI agents to triage findings, review code for vulnerabilities, and respond to incidents, each agent works without memory of that risk model, so it re-flags accepted risks, misses context behind a control, and re-investigates patterns the team already characterized. Glen, shared memory for AI agents, gives your team's agents one shared, durable memory exposed as a single MCP tool, so security knowledge persists and is recalled rather than relearned.

Security work is judgment built on accumulated context: this finding is a known false positive, that exception was accepted for a documented reason, this dependency was reviewed and cleared, that subsystem is the soft underbelly we watch closely. AI security agents without shared memory cannot apply any of it, so they generate noise, re-flagging accepted risks, re-triaging the same findings, and approaching each review as if the team had no history. Glen makes the risk model durable: connected over MCP, an agent reads the shared store before it triages or reviews, picking up the accepted exceptions and prior determinations, and writes back what it concludes, so the team's understanding of its own risk surface compounds instead of resetting.

For security engineers this turns scattered tribal knowledge into operational memory that the agents actually consult. The rationale behind a control, the list of accepted findings, the threat patterns you have already characterized, all become memory an agent reads when deciding whether something is worth escalating, cutting false-positive noise and sharpening real signal. Because Glen is org-scoped, this memory is shared across the whole security function and is even readable by engineering agents, so a note that a dependency is risky reaches a developer's agent before they pull it in. As a standard MCP server, Glen complements the code-scanning and vulnerability servers your agents already use. Connect once over OAuth or an API key, and remember to treat the underlying data store as your security boundary and record only what your policies allow. Used that way, Glen turns your accumulated risk knowledge into memory every agent can read.

FAQ

Can it remember accepted risks and false positives to cut noise?: Yes. Agents record durable observations, accepted exceptions, known false positives, prior determinations, and read them back during triage, so they stop re-flagging risks the team already characterized.
What about the sensitivity of security data?: Memory contents live in your data store, which is the security boundary. Record only what your policies allow; Glen is org-scoped with provenance on every observation so you can see who recorded each determination.