Shadow AI isn't "rogue models."
It's unauthorized hybridization inside your ML pipeline—exactly the pattern the Myth-Tech Bestiary names the Chimera.
The Chimera in One Diagram
┌───────────────────────────────┐
│ SHADOW AI = CHIMERA RISK │
└───────────────────────────────┘
│
▼
┌──────────────────────┐
│ Hybrid Components │
│ (unapproved, mixed) │
└──────────────────────┘
│
▼
┌──────────────────────┐
│ Broken Lineage │
│ (unknown origins) │
└──────────────────────┘
│
▼
┌──────────────────────┐
│ Epistemic Drift │
│ (unpredictable ML) │
└──────────────────────┘
Where the Chimera Appears in the Pipeline
[ Data ] → [ Features ] → [ Training ] → [ Serving ] → [ Monitoring ]
│ │ │ │ │
▼ ▼ ▼ ▼ ▼
Shadow AI Hybrid FEs Mixed Checkpoints API Chains Opaque Alerts
Concrete Examples
These are Chimera heads:
- LLM-generated feature transformations
- HuggingFace models with unknown training data
- Fallback LLM calls in inference
- "Temporary" enrichment scripts
- Personal GPT agents used in ETL
Each one individually harmless.
Collectively catastrophic.
Detection Heuristics
if component.origin == "unknown":
raise ChimeraWarning()
if pipeline.step.status == "temporary":
raise ChimeraWarning()
if external_api.used and not security_review.passed:
raise ChimeraWarning()
Governance Rituals (Developer Edition)
# 1. Declare lineage for every component
lineage_declare(component)
# 2. Require review for external AI
require_review(external_ai_component)
# 3. Update the provenance ledger
provenance_ledger.update(component)
# 4. Quarterly de-chimerization cycle
def quarterly_dechimerize():
hybrids = identify_unauthorized_hybrids()
for hybrid in hybrids:
isolate(hybrid)
replace_with_governed_equivalent(hybrid)
restore_lineage(hybrid)
The Takeaway
Shadow AI is hybridization without lineage.
The Chimera gives teams a way to see it, name it, and contain it.
Not "Who messed up?"
But "Which creature is this?"
That reframe alone unlocks governance.
Learn More
The Myth-Tech Bestiary is a canonical framework introducing 15 mythic creatures representing AI-era failure modes.
🔗 Full Bestiary: https://doi.org/10.5281/zenodo.18226784
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