Radar / AI security / Corbenic AI Galahad
Corbenic AI Galahad (beta)
Security layer for AI working memory: encrypts stored memory, keeps each customer's data separate, and records exactly what passes through an agent's memory.
Why it matters
Answers the 'what did the agent touch?' auditability problem — developers can inspect recorded memory activity and restore earlier memory states; internal tests claim 92-93% GPU-energy savings by reusing cached work instead of re-processing.
What you could build with it
A startup building AI support agents for banks could put Galahad's encrypted, per-customer memory ledger between the agent and its vector store, then sell compliance-ready agents whose every memory read and write is inspectable by the client's auditors.
Does it hold up?
Too early to judge — beta announced today; the energy-savings and audit claims are internal tests, not independently verified, and no third-party integrations are public yet.
Learn more
First spotted on article: source.
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