Research platform overview

An Open Research Platform for Automation Governance

meghIQ is an independent research project exploring how enterprises can discover, govern, and evidence AI-driven automations across disparate SaaS, cloud, and hybrid stacks.

Evidence systems under study

New evidence, privacy, and agent-control features now implemented.

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.

codeimplemented surfacereview basisenterprise relevancestudy lens
A01
Tamper-Evident Audit Chainaudit_events
Hash chain + HMAC signatures

Gives enterprise reviewers a verifiable sequence of actions instead of a loose activity log that can be rewritten after the fact.

integrity
A02
HMAC Key Rotationsigning_keys
Versioned signing keys

Lets the audit chain keep cryptographic continuity while keys evolve, which matters for long-lived regulated environments.

continuity
P01
Encrypted PII With Lookup Hashessensitive_fields
Fernet fields + companion hashes

Protects sensitive identity fields while preserving operational search and ownership lookup without exposing plaintext.

privacy
G01
GDPR Data-Subject Rightsprivacy_requests
Export + erasure router

Supports export and erasure workflows so privacy review is part of the platform architecture, not a manual afterthought.

rights workflow
R01
Resource Mutation Evidenceresource_mutations
Shared audit hooks across routers

Turns creates, updates, deletes, executions, toggles, acknowledgements, and remediations into reviewable tenant-scoped evidence.

change lineage
AG1
AI Agent Registryagent_registry
Agent registry API + UI

Creates a controlled inventory for autonomous agents so organizations can study ownership, access, lifecycle, and operational state.

ownership
Research framing: capability evidence, not commercial claims.Review API surfaces
Core capabilities

Capability Areas

The capability areas the research prototype is designed to address: discovery, governance, encrypted ownership lookup, AI agent inventory, and tamper-evident oversight.

Automation Discovery

Automatically discover and catalog all automations across your enterprise, including hidden and undocumented workflows.

Real-time Analytics

Comprehensive dashboards and KPIs to monitor automation health, performance, and ROI across your entire ecosystem.

Evidence Governance

Tamper-evident audit chains, HMAC signatures, resource mutation hooks, and compliance remediation events are now part of the backend control surface.

AI-Powered Insights

Unique features like Automation DNA Profiling, Health Scoring, and Lifecycle Prediction powered by advanced AI.

Optimization Engine

Designed to surface duplicate automations and consolidation candidates, with modeled savings attached to each candidate.

Privacy-Safe Ownership

Encrypted identity fields and lookup hashes preserve sensitive owner data while still supporting governance workflows.

Cryptographic integrity

Immutable Trust & Verification

Compliance events, audit trails, and automation lineage records are cryptographically anchored for tamper-proof governance.

Immutable Audit Trails

Every compliance check, violation, and remediation is recorded with cryptographic HMAC proofs.

TX: 0x1A2B3C...Verified

Provenance Tracking

Automation DNA profiles and version history are stored for verifiable lineage and authenticity.

DNA Hash: 0x4D5E6F...On-Chain

Regulatory Compliance

GDPR, HIPAA, and SOX compliance review events are independently verifiable by auditors.

Ledger VerifiedPublic
Intelligence analysis

The Science of Automation Intelligence

DNA profiling, health scoring, and predictive diagnostics that transform how you understand complex automation behavior.

Automation DNA Profiling

Analyze automation patterns to predict failures before they occur and recommend optimal architectures that prevent costly downtime.

Dependency Risk Graph

Map complex automation dependencies and surface high-blast-radius relationships before they trigger cascading failures across your ecosystem.

Version History & Forecast

Navigate through automation history, compare versions side-by-side, and preserve evidence around state changes that may create regression risk.

Health Score & Diagnostics

Continuous health monitoring with AI-powered diagnosis and automated remediation plans that keep automations running at peak performance.

Influence & Blast-Radius Scoring

Measure automation impact and identify high-influence workflows that could cause widespread disruption if they fail.

Behavioral Classification

Classify automation behaviors and match compatible workflows to optimize performance and reduce conflict-driven failures.

Template Library (planned)

Planned: a library of automation templates with reliability scoring. Not yet released.

Lifecycle Predictor

Forecast automation obsolescence and proactively suggest migration strategies before legacy systems become critical liabilities.

Conflict Resolution Engine

Automatically detect and resolve automation conflicts in real-time, preventing data corruption and workflow disruptions.

Lineage Tracking

Trace automation lineage through generations to identify inherited vulnerabilities and prevent systemic failures from propagating.

Ecosystem coverage

Planned Integrations

The prototype is designed to integrate across automation platforms and cloud functions.

Zapier
Microsoft Power Automate
GitHub Actions
AWS Lambda
Azure Functions
Jenkins
Slack
Microsoft Teams
Email (SMTP)
Custom APIs
Collaborator Inquiry

Collaborate on the Research

meghIQ is shared with research collaborators evaluating how to govern AI-driven automations. No fees, no commercial obligations.