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PALO Framework

Principled AI Lifecycle Orchestration

v2.3.1

The PALO value proposition

From theory to operationalization.

Turn responsible AI principles into decisions, controls, measurements, evidence, and continuous review.

The PALO operating loop

One connected path. Six accountable stages. Continuous learning.

01

FRAME

Make purpose, owners, stakeholders, affected people, and intended outcomes explicit.

PALO modules
AI Model Canvas
Human Agency Risk Map
Tech Trends Observatory
Use-case brief
02

CLASSIFY

Translate context into an initial risk route and identify relevant obligations.

PALO modules
Risk Tiering Calculator
Framework Comparison
Regulatory Watch 2026
Risk route
03

ASSESS

Identify fundamental-rights impacts, delegated authority, autonomy, and human oversight.

PALO modules
FRIA Assessment
PALO-AM
Assessment Path
Impact record
04

CONTROL

Connect each risk to engineering, process, assurance, and review controls.

PALO modules
Vibe Coding Governance
AuditBench Explorer
Poisoning Boomerang
Control plan
05

MEASURE

Turn governance intent into accountable KPI, KRI, thresholds, owners, and review cadence.

PALO modules
KPI and KRI Generator
P.A.L.O. Toolbox
KPI/KRI register
06

PROVE & REVIEW

Preserve decisions, sources, readiness, controls, and outcomes for the next review cycle.

PALO modules
Evidence Bundle
Documentation Hub
Regulatory Watch
Versioned evidence bundle
New context Evidence feeds the next review

Operational resultWhat changes in practice

01Decision record
02Impact and control record
03KPI/KRI register
04Versioned JSON or Markdown evidence bundle

PALO is not another principles document. It is a connected operating path for making, reviewing, and improving AI decisions.