Operational risk management that runs on live data, not quarterly spreadsheets
Risk registers, KRIs, incidents and control testing as one connected application, with AI that helps risk teams see patterns earlier.
July 2026
Articles published in July 2026.
Identity, tenancy and authorization from the first commit
Why identity, multi-tenancy and authorization are generated into the core of every fazeZERO application foundation instead of added before go-live.
AI in the SOC: alert triage and investigation with evidence
Security operations applications that use AI to enrich, summarize and prioritize alerts while analysts keep the decisions and the evidence trail.
Document intelligence in regulated workflows: extraction with verification
Extracting data from documents with AI is easy to demo and hard to trust. How to build extraction with validation, confidence and human review.
Reconciliation and exception workbenches: where finance AI earns its keep
Why transaction and ledger reconciliation is one of the most practical AI applications in finance: matching, break investigation and evidence.
AI-assisted case management: summaries, triage and human decisions
Case management across government services and enterprise operations: intake, triage, assignment, decisions and appeals, with AI assisting.
Agentic automation with human checkpoints
How to use AI agents in enterprise workflows safely: bounded tools, read-before-write, explicit approvals and an audit trail of every step.
Compliance evidence should be produced by the workflow, not assembled for the audit
Regulatory evidence collection and control attestation as an application: controls mapped to evidence, captured as work happens, reviewed by owners.
Deployment-ready is not production: an honest maturity model
Why we describe applications in three stages (deployment-ready foundation, customer-configured, production deployment) and never call a template 'production'.
Series: The Application Factory
AI model governance should be an application, not a policy document
Model inventory, evaluation, deployment approval and monitoring as one governed workflow, so AI governance produces evidence instead of meetings.
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