What an accountable AI platform is
CloudSeals’ definition, who stays responsible, how policy boundaries and human approval work, what evidence is kept, and where CloudSeals is not a fit.
Guidance on accountable AI in regulated operations.
Short, operational pages written to be checked rather than admired. Each one says what it covers, what it does not claim, and where the source is.
Available now
Evaluating AI governance platforms
Neutral criteria for comparing accountable AI and AI governance platforms, with sourced notes on established vendors and an honest view of where CloudSeals fits.
EU AI Act readiness
How obligations around literacy, human oversight, risk context, approvals and retained evidence become operational workflows. Informational, not legal advice.
Article 4 AI literacy as an evidence use case
Operationalising literacy measures and keeping records of guidance and training — not a certification of compliance.
What evidence is recorded, and for whom
The record shape behind AI-assisted decisions, and the reviewer, auditor or regulator it is written for.
Security controls for accountable AI workflows
Deployment boundaries, identity and access, data protection, retention and operational oversight — described as control areas, settled in a security review.
The TrustOps operating loop
Observe, reason, apply policy, assess risk, approve, execute, retain evidence — and how it differs from AI governance.
Questions teams ask before adopting
Short answers on scope and boundaries for TrustOps, Evidence Fabric and the three products.
In preparation
Longer guides, each with a named author, publication and review dates, sources and stated limitations. Titles are working titles; nothing here is published until it has been reviewed.
An accountable AI platform buyer’s guide
What to ask, what evidence to request, and how to run a security review before committing.
Human approval patterns for AI-assisted operations
Where a person must sign, what they need to see, and how the approval is recorded.
An AI audit-evidence checklist
The fields a reviewer needs to reconstruct an AI-assisted decision after the fact.
Finance AI exception management
Reconciliation breaks, unapplied cash and PO mismatches as reviewable exceptions rather than silent adjustments.
Industrial CCTV safety detection and privacy boundaries
Configured rules, human review, retention and worker-representation questions.
The carbon MRV evidence chain
From source data through approved method and exception review to something an assurer can check.
Glossary: TrustOps, Evidence Fabric and governed agentic AI
Plain definitions of the terms used across this site, and what each is not.