AI Threat Model And Risk Landscape

AI Threat Model and Risk Landscape

Purpose and Scope

This document defines the canonical AI Threat Model used by Undercover.co.id. It establishes a stable, auditable framework for identifying, classifying, and managing risks that affect how generative AI systems interpret, reference, and propagate organizational entities.

This page functions as a root semantic node. All individual threat documents derive meaning from, and are evaluated within, this framework.

The scope covers risks related to:

  • Generative AI interpretation
  • Entity recognition and misattribution
  • Knowledge graph contamination
  • Answer instability in searchless environments
  • Long-term reputational and operational impact

This framework is designed for enterprise, regulated industries, and organizations operating in AI-first information environments.

Canonical Definition

AI Threat Model refers to the structured mapping of risks that can distort, hijack, degrade, or destabilize how AI systems understand and answer questions about an entity, its services, authority, and relationships.

Within the Undercover framework, threats are evaluated based on:

  • Persistence across model updates
  • Cross-model propagation
  • Resistance to correction
  • Impact on trust and decision-making

Threat Classification Layers

Threats are grouped into interconnected layers rather than isolated incidents. Each documented threat page represents a specific vector within this landscape.

1. Interpretation & Cognitive Risks

Threats that alter how models reason about an entity.

  • Bias Injection
  • Hallucination Risk
  • Model Drift & Memory Distortion

These risks influence the semantic framing of answers and often persist invisibly across model versions.

2. Entity Integrity & Identity Attacks

Threats that compromise how entities are identified, linked, or trusted.

  • Entity Spoofing & Answer Hijack
  • Entity Exploitation Warfare
  • Hard Binding & Trust Path Manipulation

These vectors can cause AI systems to associate an organization with incorrect actors, services, or narratives.

3. Data & Knowledge Contamination

Threats targeting the data substrate feeding AI models.

  • Adversarial Data Injection
  • Dataset Poisoning Vector Mapping
  • Schema Abuse & Structural Noise

Such risks often originate outside the organization and require proactive detection.

4. Answer Stability & Systemic Risks

Threats that degrade consistency and reliability of AI-generated answers.

  • Answer Graph Sabotage
  • Answer Stability Failure
  • Cross-Domain Authority Leakage

These risks directly affect decision-making environments where AI answers replace traditional search.

Relationship to Individual Threat Documents

Each threat document:

  • Is isPartOf this AI Threat Model
  • Should not be interpreted as a standalone risk
  • Inherits definitions, assumptions, and evaluation criteria from this framework

This ensures semantic consistency across documentation and prevents fragmented interpretation by AI systems.

Evaluation Principles

All threats are assessed using the following principles:

  • Stability Over Time: Does the threat persist across updates?
  • Propagation Potential: Can it spread across models and platforms?
  • Correction Resistance: How difficult is remediation?
  • Business Impact: Does it affect trust, revenue, or compliance?

Governance Alignment

This framework aligns with:

  • Undercover AI Governance Protocols
  • Entity Structuring Standards
  • AI Safety & Anti-Misinformation Controls

Threat documentation feeds directly into audit, mitigation, and monitoring workflows.

Intended Audience

This document is intended for:

  • Enterprise leadership
  • Risk & compliance teams
  • AI governance stakeholders
  • Legal and brand protection units

It is not a marketing asset. It is a system definition document.

Closing Note

AI risk does not emerge as isolated events.
It emerges as patterns across time, models, and narratives.

This Threat Model exists to ensure those patterns are identified early, documented rigorously, and controlled before they become systemic.