I'M SGR —

WANTED

Dead or Alive

ClickScope

“Sounding Line”

฿ 260,000,000

Read-only ClickHouse access for an AI agent, enforced three separate ways — statement policy, server-side caps, database grants — so a hijacked prompt still can't drop a table or scan a billion rows it was only meant to query. The layer most MCP database servers skip entirely.

securitymcpclickhousepython

Dossier Sealed — Source Private

WANTED

Dead or Alive

ClickScope

“Sounding Line”

฿ 260,000,000

Read-only ClickHouse access for an AI agent, enforced three separate ways — statement policy, server-side caps, database grants — so a hijacked prompt still can't drop a table or scan a billion rows it was only meant to query. The layer most MCP database servers skip entirely.

securitymcpclickhousepython

Dossier Sealed — Source Private

WANTED

Dead or Alive

ClickScope

“Sounding Line”

฿ 260,000,000

Read-only ClickHouse access for an AI agent, enforced three separate ways — statement policy, server-side caps, database grants — so a hijacked prompt still can't drop a table or scan a billion rows it was only meant to query. The layer most MCP database servers skip entirely.

securitymcpclickhousepython

Dossier Sealed — Source Private

WANTED

Dead or Alive

Clauden

“Crow's Nest”

฿ 148,000,000

A quiet audit trail for shadow AI usage — it names who in the org reached for a more capable model, and when, without ever reading a prompt, a file, or a tool call. Governance for the question most teams aren't asking yet: who's actually driving the agent.

securityclaude-codepythonobservability

WANTED

Dead or Alive

Clauden

“Crow's Nest”

฿ 148,000,000

A quiet audit trail for shadow AI usage — it names who in the org reached for a more capable model, and when, without ever reading a prompt, a file, or a tool call. Governance for the question most teams aren't asking yet: who's actually driving the agent.

securityclaude-codepythonobservability

WANTED

Dead or Alive

Clauden

“Crow's Nest”

฿ 148,000,000

A quiet audit trail for shadow AI usage — it names who in the org reached for a more capable model, and when, without ever reading a prompt, a file, or a tool call. Governance for the question most teams aren't asking yet: who's actually driving the agent.

securityclaude-codepythonobservability