XPlain-R: Agentic AI made simple

Audit By AI

XPlain-R© as a Compliance Enabler

These audit frameworks define what organisations should do (governance principles, audit requirements, risk management). XPlain provides how to operationalise it (structured reasoning, audit trails, provenance).

COBIT 2019 (ISACA): Internal Controls & Risk Metrics

What they require:

  • Performance measures for AI governance
  • Internal controls for IT systems
  • Risk metrics and monitoring

How XPlain delivers:

  • Meta-Primer framework = control structure for AI reasoning
  • Romer records = performance measurement (reasoning steps, choices, confidence)
  • Blockchain provenance = audit control (tamper-evident process)
  • Cross-model alignment % = risk metric (consistency across providers)

Your claim: “XPlain-R implements COBIT control objectives for AI reasoning by providing structured governance (meta-primer), measurable performance (alignment metrics), and auditable processes (blockchain).”

COSO ERM: Enterprise Risk Management

What they require:

  • Governance + strategy + stakeholder collaboration
  • 5-step audit program (governance → risk management)
  • Model monitoring

How XPlain delivers:

  • Governance: Meta-primer defines reasoning rules
  • Risk identification: Romer records show where models diverge
  • Monitoring: Cross-provider testing reveals model-specific risks
  • Audit program: Genesis block → topical primer → artifact → romer = complete audit trail

Your claim: “XPlain-R enables COSO-compliant AI risk management by documenting reasoning processes end-to-end and quantifying cross-model risk through consistency metrics.”

GAO AI Framework: Four Principles

Their principles:

  1. Governance – Clear accountability for AI decisions
  2. Data quality – Reliable inputs and processes
  3. Performance consistency – Repeatable, predictable outputs
  4. Ongoing monitoring – Continuous validation

How XPlain delivers:

  1. Governance: Meta-primer = policy layer; blockchain = accountability
  2. Data quality: Topical primers validate inputs; metadata-not-raw-data approach
  3. Performance consistency: This is literally your core value proposition – 90-95% cross-model alignment
  4. Ongoing monitoring: Version-controlled primers + blockchain tracking = continuous validation

Your claim: “XPlain-R was designed specifically to address GAO Principle 3 (Performance Consistency), achieving 90-95% cross-model alignment while providing full lifecycle auditability for Principles 1, 2, and 4.”

This is your strongest framework alignment.

IIA AI Auditing Framework: Risk-Profiled Audits

What they require:

  • Strategy, ethics, cyber resilience alignment
  • Data architecture transparency
  • Performance alignment verification

How XPlain delivers:

  • Audit trail by design: Every reasoning step documented in romer records
  • Multi-model verification: Testing across 6 providers reveals model-specific biases
  • Transparent architecture: Open-source meta-primer + GitHub provenance
  • Performance validation: Empirical cross-model alignment data

Your claim: “XPlain-R provides auditors with machine-readable reasoning provenance (romer records) and cryptographic integrity verification (blockchain), enabling risk-profiled audits of AI decision-making.”

Singapore PDPC Model: Transparency & Ethical Use

What they require:

  • Transparency in AI operations
  • Policy management
  • Ethical use case implementation
  • Implementation guides

How XPlain delivers:

  • Transparency: Romer records = human-readable reasoning explanation
  • Policy management: Meta-primer enforces organizational policies
  • Ethical implementation: Structured reasoning prevents “black box” decisions
  • Implementation guides: Topical primers = reusable policy templates

“XPlain-R is a compliance infrastructure that enables organisations to meet GAO, COBIT, COSO, IIA, and PDPC requirements for auditable, governed AI systems”

Enterprise Value Proposition

“XPlain provides the technical infrastructure to demonstrate compliance with established AI governance frameworks, reducing audit risk and regulatory exposure”

Academic → Enterprise Bridge

Research proving 90-95% cross-model alignment becomes evidence for compliance officers that:

  • AI systems can be made predictable (GAO Principle 3)
  • Performance can be measured objectively (COBIT metrics)
  • Risk can be quantified (COSO risk management)
  • Audits can be conducted systematically (IIA framework)