meghIQ is an independent, uncompensated research initiative investigating methods for enterprises to discover, evidence, and govern autonomous workflows across hybrid infrastructure.
The latest backend work strengthens enterprise-grade governance: tamper-evident audit records, cryptographic key rotation, encrypted sensitive fields, GDPR rights workflows, resource mutation evidence, and an AI Agent Registry.
Gives enterprise reviewers a verifiable sequence of actions instead of a loose activity log that can be rewritten after the fact.
integrityLets the audit chain keep cryptographic continuity while keys evolve, which matters for long-lived regulated environments.
continuityProtects sensitive identity fields while preserving operational search and ownership lookup without exposing plaintext.
privacySupports export and erasure workflows so privacy review is part of the platform architecture, not a manual afterthought.
rights workflowTurns creates, updates, deletes, executions, toggles, acknowledgements, and remediations into reviewable tenant-scoped evidence.
change lineageCreates a controlled inventory for autonomous agents so organizations can study ownership, access, lifecycle, and operational state.
ownershipmeghIQ is maintained as an independent, uncompensated research project. The maintainer receives no financial compensation, royalties, or commercial payouts from this project.
Access is extended to research collaborators on a non-commercial basis. No commercial sales, paid licensing, or consumer subscription plans are offered.
Three fundamental challenges structure our empirical investigations.
"How can enterprises find every AI and automation workload running across SaaS, cloud, and homegrown stacks — without manual inventory?"
Active Threads"How can governance systems preserve owner and user lookup while encrypting sensitive identity fields at rest?"
Active Threads"What does tamper-evident audit, policy enforcement, and human-in-the-loop oversight look like for AI-driven automations operating at scale?"
Active ThreadsGrounded in reproducible artifact generation and transparent telemetry instrumentation.
Each research track produces a working implementation that collaborators can evaluate against real topologies.
Findings are verified using live runtime execution data from production-grade heterogeneous stacks.
Technical write-ups, architecture specifications, and verification proofs form the public outputs.
Beta access is offered without charge to research teams studying AI agent governance, audit non-repudiation, and operational observability.