Strategic Trust Framework

Trust is created not by controlling AI outputs, but by PROVING that systems behave consistently, safely, and transparently across scenarios.

Trust Layers

Policy-Aligned Reasoning

AI cannot generate decisions outside approved boundaries

Data Sensitivity Awareness

Automatic adjustments based on data classification

Scenario-Aware Oversight

Context factors evaluated before every action

Transparent Decision Trails

Complete auditability of every AI decision

Policy-Aligned Reasoning

Ensure AI reasoning stays within approved boundaries:

from duragraph.governance import PolicyAlignedReasoning

aligned_reasoning = PolicyAlignedReasoning(
    # Embed policy constraints in prompts
    system_constraints=[
        "You are a customer support agent for ACME Corp.",
        "You may only discuss ACME products and services.",
        "You cannot provide legal, medical, or financial advice.",
        "You must verify information before sharing.",
    ],
    # Constitutional AI-style constraints
    constitutional_principles=[
        "Responses must be helpful and accurate.",
        "Responses must not cause harm.",
        "Responses must respect user privacy.",
    ],
    # Hard stops for policy violations
    hard_stops=[
        "competitor_mention",
        "unauthorized_promise",
        "pii_disclosure",
    ],
)

Implementation Approaches

Prompt Engineering

# Embed policies in system prompt
system_prompt = """
You are a support agent bound by these policies:
- Only discuss authorized topics
- Cannot make promises about refunds over $100
- Must escalate legal questions to human agents

POLICY VIOLATION = IMMEDIATE STOP
"""

Constitutional AI

# Self-critique and revision
constitutional = ConstitutionalAI(
    principles=[
        "Is this response accurate?",
        "Does this stay within authorized scope?",
        "Could this cause harm to the user?",
    ],
    revision_rounds=2,
)

Hard Stops

# Post-generation policy check
def policy_check(response: str) -> bool:
    violations = detect_violations(response)
    if violations:
        log_violation(violations)
        return False  # Block response
    return True

Data Sensitivity Awareness

AI automatically adjusts behavior based on data classification:

from duragraph.governance import DataSensitivityHandler

sensitivity_handler = DataSensitivityHandler(
    classification_actions={
        "public": {
            "controls": ["basic_logging"],
            "processing": "standard",
        },
        "internal": {
            "controls": ["access_logging", "basic_redaction"],
            "processing": "standard",
        },
        "confidential": {
            "controls": ["strict_access", "full_audit", "encryption"],
            "processing": "restricted",
            "require_justification": True,
        },
        "restricted": {
            "controls": ["approval_required", "encryption", "limited_retention"],
            "processing": "minimal",
            "require_approval": True,
            "max_retention_days": 30,
        },
    },
)

Classification Detection

from duragraph.governance import DataClassifier

classifier = DataClassifier(
    detection_rules={
        "pii": {
            "patterns": ["ssn", "credit_card", "email", "phone"],
            "classification": "confidential",
        },
        "financial": {
            "patterns": ["account_number", "balance", "transaction"],
            "classification": "confidential",
        },
        "health": {
            "patterns": ["diagnosis", "medication", "treatment"],
            "classification": "restricted",
        },
    },
)

# Automatic classification
data_class = await classifier.classify(input_data)
# Returns: "confidential" with reasoning

Scenario-Aware Oversight

Every AI decision is evaluated against contextual factors:

Temporal Context

temporal_factors = {
    "time_of_day": "business_hours",  # vs off-hours
    "deadline_pressure": "normal",     # urgent, normal, relaxed
    "data_freshness": "current",       # How recent is the data?
}

# Different governance for off-hours requests
if temporal_factors["time_of_day"] == "off_hours":
    require_additional_verification = True

Source Context

source_factors = {
    "request_origin": "api",           # internal, external, api
    "user_history": "established",      # new, established, trusted
    "channel": "production",            # dev, staging, production
}

# Stricter controls for external sources
if source_factors["request_origin"] == "external":
    apply_enhanced_validation = True

Purpose Context

purpose_factors = {
    "stated_intent": "billing_inquiry",
    "inferred_intent": "billing_inquiry",  # ML-detected
    "historical_pattern": "consistent",     # Does this match past behavior?
}

# Flag mismatched intents
if purpose_factors["stated_intent"] != purpose_factors["inferred_intent"]:
    flag_for_review = True

Constraint Context

constraint_factors = {
    "regulatory": ["GDPR", "SOC2"],     # Applicable regulations
    "organizational": ["no_competitors"], # Company policies
    "technical": ["rate_limited"],        # System limitations
}

Transparent Decision Trails

Complete audit trails for every AI decision:

from duragraph.governance import DecisionTrail

# Automatically captured for every interaction
trail = DecisionTrail(
    decision_id="dec_abc123",
    timestamp="2024-12-29T10:30:00Z",

    # Context at decision time
    context_snapshot={
        "user_id": "user_123",
        "session_id": "sess_456",
        "data_classification": "confidential",
        "risk_score": 0.45,
    },

    # Reasoning chain
    reasoning_chain=[
        {"step": 1, "action": "classify_intent", "result": "billing_inquiry"},
        {"step": 2, "action": "evaluate_risk", "result": "medium"},
        {"step": 3, "action": "select_policy", "result": "customer_support"},
        {"step": 4, "action": "apply_guardrails", "result": "passed"},
    ],

    # Data sources used
    data_sources=[
        {"type": "user_profile", "id": "profile_123"},
        {"type": "knowledge_base", "id": "kb_billing_faq"},
    ],

    # Policies evaluated
    policies_applied=["customer_support", "pii_protection"],

    # Controls triggered
    controls_triggered=["audit_log", "pii_redaction"],

    # Final outcome
    outcome={
        "action": "respond",
        "confidence": 0.92,
        "response_id": "resp_789",
    },
)

Querying Audit Trails

# Get specific decision trail
GET /api/v1/governance/trust/audit/dec_abc123

# Search trails by criteria
GET /api/v1/governance/trust/audit?user_id=user_123&date_from=2024-12-01

Response:

{
  "decision_id": "dec_abc123",
  "timestamp": "2024-12-29T10:30:00Z",
  "context_snapshot": {
    "user_id": "user_123",
    "risk_score": 0.45
  },
  "reasoning_chain": [{ "step": 1, "action": "classify_intent", "result": "billing_inquiry" }],
  "policies_applied": ["customer_support"],
  "controls_triggered": ["audit_log"],
  "outcome": {
    "action": "respond",
    "confidence": 0.92
  }
}

Trust Scoring

Assign trust scores to entities and decisions:

from duragraph.governance import TrustScorer

scorer = TrustScorer()

# Score an entity (user, agent, data source)
trust_score = await scorer.score_entity(
    entity_id="user_123",
    factors={
        "history_length": 365,      # Days of history
        "violation_count": 0,        # Past violations
        "verification_level": "2fa", # Identity verification
        "behavior_consistency": 0.95, # How consistent is behavior
    },
)
# Returns: TrustScore(value=0.87, level="high", factors={...})

# Score a decision
decision_trust = await scorer.score_decision(
    decision_id="dec_abc123",
    factors={
        "data_quality": 0.9,         # Source data reliability
        "model_confidence": 0.85,     # AI certainty
        "guardrails_passed": True,    # All checks passed
        "human_verified": False,      # Human review status
    },
)

Trust Score API

GET /api/v1/governance/trust/score/user_123

{
  "entity_id": "user_123",
  "trust_score": 0.87,
  "trust_level": "high",
  "factors": {
    "history": 0.95,
    "verification": 0.9,
    "behavior": 0.85,
    "violations": 1.0
  },
  "recommendations": ["Enable advanced features", "Reduce verification friction"]
}

Compliance Integration

Trust framework supports regulatory compliance:

EU AI Act

compliance = EUAIActCompliance(
    risk_classification="high_risk",  # Based on use case
    requirements={
        "transparency": True,          # Disclose AI usage
        "human_oversight": True,        # Human can intervene
        "accuracy_monitoring": True,    # Track performance
        "documentation": True,          # Maintain records
    },
)

SOC 2

compliance = SOC2Compliance(
    trust_criteria={
        "security": ["access_controls", "encryption"],
        "availability": ["uptime_monitoring", "failover"],
        "processing_integrity": ["validation", "audit_trails"],
        "confidentiality": ["classification", "access_logs"],
        "privacy": ["consent", "data_minimization"],
    },
)

Best Practices

Progressive Trust

Start with lower trust levels and increase as entities prove reliable. Don’t grant maximum trust immediately.

Trust Decay

Trust should decay over time without positive reinforcement. Inactive accounts or unchanged scores should gradually decrease.

1. Make trust visible - Show users and admins trust scores and reasoning
2. Enable trust recovery - Provide paths to rebuild trust after violations
3. Calibrate regularly - Adjust trust algorithms based on outcomes
4. Audit the auditors - Monitor the trust system itself for drift
5. Document decisions - Every trust change should have clear reasoning

Next Steps

Guardrails

Configure behavioral guardrails to enforce boundaries

Governance Overview

Return to governance overview for architecture details