Published and updated 7 September 2026 · Altari Systems

Database authority research

Fail-Closed Row-Level Security for AI Agent Systems

Abstract

Fail-closed row-level security denies access when tenant, project or actor context is absent, malformed or unauthorised. For autonomous agents, this matters because workers are distributed and fallible: a missing session variable must produce no rows or an error, never broaden access across customer project state.

01

Default denial is the invariant

A permissive fallback turns a context bug into a cross-project data exposure. RLS policies should derive access from validated database context and reject unset values.

Application filters remain useful, but they are not a substitute for an authority check at the data boundary.

  • Explicit actor identity
  • Validated project scope
  • No wildcard fallback
  • Privileged paths isolated
02

Test absence and misuse

Happy-path tenant tests are insufficient. Verification should cover missing context, invalid identifiers, reused pooled connections and attempts to address another project.

Administrative and migration roles require deliberate bypass design and separate operational controls.

  • Unset session context
  • Connection-pool reset
  • Cross-project identifiers
  • Service-role boundaries
03

Nexus implementation context

Nexus keeps durable multi-project execution state. Fail-closed database rules support separation between customer projects and between actors within an orchestration run.

RLS is one layer; repository credentials, worker isolation and application authorisation must align with it.

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