AI Contextual Governance

DuraGraph provides infrastructure-level AI contextual governance that adapts to intent, sensitivity, environment, and trust boundaries.

Key Insight

“Context is the new control layer for AI.” Unlike static rules, contextual governance ADAPTS to situational conditions.

Why Contextual Governance?

Traditional rule-based governance fails modern AI systems:

Challenge	Impact
45% of enterprises experienced GenAI data leakage	Static rules can’t prevent dynamic threats
Only 25% have implemented AI governance	Governance is seen as blocker, not enabler
Multi-modal AI combines diverse data	Single rule sets can’t cover all scenarios

The missing dimension: CONTEXT determines acceptable risk, privacy requirements, reasoning boundaries, and interaction patterns.

Architecture Overview

┌─────────────────────────────────────────────────────────────────────┐
│                   AI CONTEXTUAL GOVERNANCE LAYER                    │
├─────────────────────────────────────────────────────────────────────┤
│                                                                     │
│  ┌────────────────┐  ┌────────────────┐  ┌────────────────┐        │
│  │                │  │     Policy     │  │   Automated    │        │
│  │ Context Engine │  │  Orchestration │  │     Risk       │        │
│  │                │  │     Layer      │  │   Evaluation   │        │
│  └───────┬────────┘  └───────┬────────┘  └───────┬────────┘        │
│          │                   │                   │                  │
│          └───────────────────┼───────────────────┘                  │
│                              │                                      │
│  ┌────────────────┐  ┌──────┴───────┐  ┌────────────────┐          │
│  │      AI        │  │  Behavioral  │  │ Multi-Environ  │          │
│  │ Observability  │◄─┤  Guardrails  │──┤  Enforcement   │          │
│  │                │  │              │  │                │          │
│  └────────────────┘  └──────────────┘  └────────────────┘          │
│                                                                     │
└─────────────────────────────────────────────────────────────────────┘

Core Components

Context Engine

Interprets metadata, domain rules, user intent, and operational signals to determine governance response.

Policy Orchestration

Programmable, composable governance building blocks with hot-reload and inheritance.

Behavioral Guardrails

Adaptive guardrails that prevent misuse, hallucinations, and drift from expected behavior.

Risk Evaluation

Real-time scoring based on sensitivity, impact, and probability of misalignment.

Quick Start

Enable Basic Governance

from duragraph import Graph, llm_node
from duragraph.governance import GovernanceEngine, Policy

# Create governance engine
governance = GovernanceEngine()

# Define a policy
policy = Policy(
    name="customer_support",
    guardrails=[
        {"type": "output_filter", "config": {"block_pii": True}},
        {"type": "topic_restriction", "config": {"blocked": ["competitor_info"]}},
    ],
    audit_level="full",
)

governance.register_policy(policy)

@Graph
class GoverndAssistant:
    @llm_node(governance=governance)
    async def respond(self, state: State) -> State:
        # Governance automatically applied
        response = await self.llm.complete(state.messages)
        state.response = response
        return state

Context-Aware Governance

from duragraph.governance import ContextEngine, GovernanceProfile

# Context engine evaluates situational factors
context_engine = ContextEngine()

# Evaluate context
profile = await context_engine.evaluate(
    data_metadata={"classification": "confidential", "contains_pii": True},
    user_context={"role": "support_agent", "department": "customer_success"},
    intent="answer_billing_question",
    environment="production",
)

# Profile contains:
# - risk_score: 0.65
# - allowed_actions: ["read_account", "view_invoice"]
# - required_controls: ["audit_log", "pii_redaction"]
# - applicable_policies: ["customer_support", "pii_protection"]

Risk Evaluation

DuraGraph evaluates risk across multiple dimensions:

Data Sensitivity

sensitivity_factors = {
    "classification_level": "confidential",  # public, internal, confidential, restricted
    "pii_presence": True,
    "regulatory_scope": ["GDPR", "HIPAA"],
}

Decision Impact

impact_factors = {
    "reversibility": "low",      # Can decision be undone?
    "affected_parties": 50,       # Number of people impacted
    "financial_exposure": 10000,  # Potential monetary impact
}

Operational Context

operational_factors = {
    "time_pressure": "normal",    # urgent, normal, relaxed
    "human_oversight": True,       # Is human review available?
    "system_confidence": 0.85,     # Model certainty
}

Governance Maturity Model

DuraGraph supports progressive governance maturity:

Level	Name	Characteristics
1	Reactive	Static policies, manual updates, post-hoc audit
2	Proactive	Dynamic policies, real-time evaluation, continuous monitoring
3	Adaptive	Self-adjusting policies, predictive risk, autonomous guardrails
4	Intelligent	Goal-aware governance, self-healing controls, strategic trust

Compliance Frameworks

DuraGraph governance aligns with:

• EU AI Act - Risk classification and transparency requirements
• NIST AI RMF - Risk management framework controls
• ISO 42001 - AI management system certification
• SOC 2 - Trust service criteria
• HIPAA - Healthcare data protection

API Reference

Context Evaluation

POST /api/v1/governance/context/evaluate

{
  "data_metadata": {
    "classification": "confidential",
    "source": "customer_database"
  },
  "user_context": {
    "user_id": "user_123",
    "role": "analyst"
  },
  "intent": "generate_report",
  "environment": "production"
}

Response:

{
  "governance_profile": {
    "policies": ["data_analyst", "pii_protection"],
    "risk_level": "medium"
  },
  "risk_score": 0.45,
  "allowed_actions": ["read", "aggregate", "export_anonymized"],
  "required_controls": ["audit_log", "data_minimization"]
}

Policy Management

# List policies
GET /api/v1/governance/policies

# Create policy
POST /api/v1/governance/policies

# Simulate policy
POST /api/v1/governance/policies/simulate

Next Steps

Guardrails

Configure behavioral guardrails for your AI workflows

Trust Framework

Implement strategic trust with audit trails