Author

Mudassir Khan.

CEO of Cube A Cloud. Writes on decision systems, AI governance, human-in-the-loop oversight, and the risk frameworks that bind AI to a defensible record.

About the author

Mudassir Khan is CEO of Cube A Cloud LLC. The practice covers two areas: AI governance and decision systems, and growth systems for search, answer engines, and marketplaces. Both share the same operating stance, which is that a system that cannot refuse a risky action is not trustworthy.

The writing on this site documents the methodology in public. Every framework, protocol, and stated limit is derived from operational work rather than from theory, and each essay is written to be checked against the standards it cites.

Writes about

AI Agents
AI Governance
AI Operations
AI Safety
Decision Systems

Published essays (20)

AI GovernanceAI Contextual Governance Framework ExplainedContextual AI governance replaces static rules with situational oversight. The three-tier model, its primary focuses, and how to apply it.
AI GovernanceHow to Run an AI Readiness AssessmentA practical guide to assessing AI readiness across 7 governance dimensions: the framework, the scoring method, and the 90-day action plan.
AI GovernanceWhat Belongs in an AI PolicyAn AI policy is not a list of prohibitions. Approved uses, data handling, refusal conditions, HITL requirements, and incident response.
AI GovernanceScoring AI Risk Without a SpreadsheetMost AI risk templates are spreadsheets nobody reopens. A 12-dimension scoring model that produces actionable HITL placement instead.
AI GovernanceWhy a Governance Prompt Library Is DifferentGeneric prompt libraries optimise for productivity. A governance prompt library optimises for accountability. Here is the distinction.
AI GovernanceThree Primary Focuses of AI Governance FrameworksAccountability, transparency, and control are the three primary focuses of AI governance frameworks. What each requires in practice.
AI AgentsAI Agent vs AI Assistant: Understanding the Critical DifferenceAI agent vs AI assistant comes down to autonomy, scope, tools, and persistence. Here is the four-dimension test and what each implies for oversight.
AI GovernanceAI Audit: How to Evaluate and Monitor Your AI Systems for Risk and ComplianceAn AI audit produces four artefacts: decision log, model card, risk register, refusal log. Internal, external, and continuous modes, mapped to NIST.
Decision SystemsAI for Decision Making: A Practical Framework for High-Stakes Business ChoicesAI for decision making works inside a bounded scope, refuses on irreversible commitments, and operates through a constraint gate. Here is the framework.
AI SafetyAI Guardrails: How to Build Safety Boundaries That Actually WorkAI guardrails come in four types: input, output, behavioural, and policy. Where each fits in the request lifecycle, how they fail, and how to test them.
AI SafetyAI Red Teaming: How to Test Your AI Systems for Failure Before They Fail in ProductionAI red teaming is structured pre-deployment testing for failure. Four test classes, the five-stage engagement, and how findings feed refusal.
Decision SystemsAutomated Decision Making: Where It Works, Where It Fails, and How to Stay in ControlAutomated decision making works in three contexts and fails in three. The operational test, GDPR Article 22, and the four artefacts.
Decision SystemsDecision Intelligence: How AI Transforms High-Stakes Business DecisionsDecision intelligence is a discipline, not a dashboard. The working definition, the three pillars, and how it differs from BI, AI, and DSS.
Decision SystemsDecision Intelligence Platform: What to Look For and How to Evaluate VendorsA decision intelligence platform enforces rules before commitment. The structural test that separates real platforms from rebadged BI.
Decision SystemsDecision Support Systems in the AI Era: From DSS to Decision IntelligenceA decision support system is the classical software category. Anatomy, four DSS types, and how DSS differs from decision intelligence and decision systems.
AI GovernanceHuman-in-the-Loop AI: Why Human Oversight Is the Key to Trustworthy AI SystemsHuman-in-the-loop AI keeps a person in the decision path. The three patterns, four conditions for meaningful oversight, and the EU AI Act.
Decision SystemsWhat Is a Decision System? Definition & ExamplesA decision system enforces constraints, order, and verification before commitment. The definition, examples, and how it differs from AI agents.
AI GovernanceAI Risk Management Framework: Enterprise GuideA practical guide to building an AI risk management framework: NIST AI RMF (Govern, Map, Measure, Manage), ISO/IEC 42001, and a rollout plan.
AI GovernanceWhy AI Refusal Matters: Systems That Say NoAn AI system that cannot refuse is a liability. This essay explains why refusal is a feature, what conditions should trigger it, and how to design it in.
AI OperationsAI Agents vs AI Employees: Key DifferencesAI agents complete tasks. AI employees hold a bounded role with authority and accountability. Compared across scope, authority, and oversight.

Contact

Engagement criteria are documented on the contact page. Direct correspondence: contact@cubeacloud.com.