AI Stewardship

Overview

AI Stewardship is Prizm’s governance layer for every action an agent takes. It ensures that as Prizm’s agents act with increasing autonomy, every decision remains transparent, risk-scored, and — where warranted — subject to human approval. This is what makes Prizm’s agentic execution trustworthy rather than a black box: humans stay in control even as agents do the work.

Foundational concepts

Understanding Stewardship starts with four building blocks:

Concept	Definition
Metadata	Raw information that describes an asset — titles, schemas, columns, tags. Metadata is a book’s table of contents, chapters, and index.
Context	Metadata + surrounding information + retrieval + relevance that the AI uses to make decisions. Context is interpreted information for intelligent decision-making — a book’s summary plus a teacher’s notes on why it matters.
Action	A change to metadata, quality rules, metrics, schemas, lineage, or policy. Extracting metadata is not an action — but a change detected or applied becomes one. Actions can be generated by a human or by AI.
Log	Every action is logged. Application, API, and AI-driven events appear in the Audit Log; user sessions and activity appear in the Activity Log.

Action sensitivity and risk scoring

Every action carries a default sensitivity level, which combines with the asset’s criticality to produce a quantitative risk score.

Sensitivity weights

Action Sensitivity	Weight	Meaning
Low	0.3	Metadata-only, reversible, safe (e.g. updating a description)
Medium	0.6	Context-dependent, moderate impact (e.g. creating a non-financial metric)
High	1.0	Business-critical, schema/metric/PII-impacting (e.g. PII classification, KPI formula update)

Risk score formula

\text{Action Risk} = \text{Action Sensitivity (Weight)} \times \text{Asset Criticality (Percentile)}

This yields a single 0–100 score. The weighting is deliberate: 0.3 keeps low-risk actions low even on critical assets; 0.6 lets medium-risk actions scale meaningfully with asset importance; 1.0 means high-sensitivity actions fully inherit the criticality of the asset they touch.

Worked examples

	Example A — Low Sensitivity	Example B — Medium Sensitivity	Example C — High Sensitivity
Action	Update description	Create a non-financial metric	PII classification, KPI formula update
Sensitivity	Low → 0.3	Medium → 0.6	High → 1.0
Asset criticality	80	80	80
Risk	0.3 × 80 = 24 → Low	0.6 × 80 = 48 → Medium	1.0 × 80 = 80 → High

Risk level bands

Risk Score	Risk Level
0 – below 40	Low Risk
40 – below 75	Medium Risk
75 – below 90	High Risk
90 – 100	Critical Risk

Autonomy levels

Prizm maps its AI autonomy to a familiar self-driving car analogy — each level represents how much the AI decides and executes versus how much a human must initiate or approve.

AI Autonomy Level	Self-Driving Equivalent	Behavior
Human	Manual Driving	User initiates, decides, and performs all actions. AI only provides visibility (like a lane-assist beep).
AI Guidance	Driver-Assisted Mode	Human starts the action; AI offers recommendations, human still decides.
AI Assisted	Co-Pilot Mode	AI actively detects and suggests actions; human approves, like a co-pilot with hands on the wheel.
AI Completed (Autonomous)	Fully Self-Driving	AI detects, decides, and executes automatically. Human only monitors high-risk events.

Action lifecycle

Every action progresses through a defined lifecycle, whether it’s fully autonomous or awaiting human sign-off:

Detected → Proposed → Pending Approval → Executing → Completed / Partially Completed / Failed / Overridden → Reverted (if needed) → Logged / Archived

Stage	When it happens	Example
Pending	AI detects an opportunity for improvement	”Description unclear → improvement needed”
Ready for Review	AI proposes a specific change	”AI draft ready for steward review”
Verified	Steward approves the update	”Steward validated updated description”
Completed	AI executes the update	”Description updated successfully”
Deprecated	Steward rejects the AI suggestion	”Suggested description not applied”

Overridden means a human rejected an AI-Assisted suggestion. Reverted means an action was rolled back after a negative trust impact was detected. Both states are preserved for audit and learning.

The Stewardship hub

The Stewardship page is the central hub for monitoring every action in the system — autonomous, AI-assisted, or human — giving stewards a single place to review what the platform has done, is doing, or is waiting on.

Action record schema

Every action is captured with a structured record, including:

Field	Description
`action_id`	Unique identifier
`action_type`	e.g. `schema_correction`, `quality_rule_validation`, `business_term_mapping`, `remediation_trigger`
`autonomy_level`	`Autonomous`, `AI-Assisted`, `Human`, `System Triggered`
`domain` / `product` / `application`	Business context the action belongs to
`severity_level`	`Low`, `Medium`, `High`, `Critical`
`confidence_score`	AI’s confidence in the decision (0–1)
`initiated_by` / `approved_by`	Who or what triggered and validated the action
`trigger_source`	`Anomaly Detection`, `Quality Monitor`, `Lineage Drift`, `Manual Input`
`result`	`Success`, `Failed`, `Partially Successful`, `Skipped`
`trust_impact_score`	Net effect on the platform’s trust index
`learning_flag` / `audit_flag`	Whether the action feeds retraining and/or compliance workflows

Example actions by category

🧠 Autonomous Actions — AI-driven, no human input

Schema drift auto-corrected
Missing value patch generated
Data product ownership updated

🤖 AI-Assisted Actions — AI suggests, human approves

Suggested quality rule confirmed
Business term mapping validated
Threshold adjusted via co-pilot

👤 Human Actions — manual, tracked for learning

Steward manually resolved a quality issue
Metadata enrichment via human input
Rule exception documented

🔁 In Review / Next Actions — queued or pending

Pending approval for a schema fix
Awaiting steward confirmation
Under confidence-threshold recheck

Stewardship UI

The Stewardship page is organized into two sections:

Key Metrics — scorecards for Autonomous, AI-Assisted, Steward, and Pending Review counts
Table Component — every action listed with action_id, action_type, autonomy_level, description, domain, product, severity_level, status, start_time, end_time, and duration

Example weekly metrics snapshot

Total Actions This Week: 1,248
Autonomous: 742 · AI-Assisted: 336 · Human: 132 · In Review: 38
Avg Confidence Score (Autonomous): 92%
Avg Time to Resolution: 3.2 hours (↓48% YoY)
Trust Index Improvement: +27%
Steward Adoption Rate: 81%
Actions per Domain: Finance (310), Marketing (228), Ops (186), HR (92)
Insight: 34% of manual tasks converted to AI-assisted over Q3; 12 recurring data patterns now resolved autonomically

Stewardship across the platform

AI Stewardship isn’t confined to one module — it’s applied consistently across every part of Prizm:

Area	What Stewardship does
Catalog	Metadata enrichment, classification governance for sensitive data, drift detection, knowledge graph evolution
Quality	Adaptive rule generation, explainable quality scores, bias detection, improvement recommendations
Observability	Contextual anomaly explanations, predictive health monitoring, impact analysis, self-healing with human oversight

Autonomous Intelligence

See how Stewardship fits into Prizm’s broader autonomy architecture

Multi-Agent Architecture

See the agents whose actions Stewardship governs

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Overview

Foundational concepts

Action sensitivity and risk scoring

Sensitivity weights

Risk score formula

Risk level bands

Autonomy levels

Action lifecycle

The Stewardship hub

Action record schema

Example actions by category

Stewardship UI

Stewardship across the platform

Autonomous Intelligence

Multi-Agent Architecture

​Overview

​Foundational concepts

​Action sensitivity and risk scoring

​Sensitivity weights

​Risk score formula

​Risk level bands

​Autonomy levels

​Action lifecycle

​The Stewardship hub

​Action record schema

​Example actions by category

​Stewardship UI

​Stewardship across the platform

​Related pages

Autonomous Intelligence

Multi-Agent Architecture

Overview

Foundational concepts

Action sensitivity and risk scoring

Sensitivity weights

Risk score formula

Risk level bands

Autonomy levels

Action lifecycle

The Stewardship hub

Action record schema

Example actions by category

Stewardship UI

Stewardship across the platform

Related pages