# Policy Engine for Evaluating Decision Rules Against Compliance Frameworks: Semantica Implementation Guide

> Explore Semantica's policy engine for evaluating decision rules against compliance frameworks. Implement real-time validation and predictive impact analysis for robust enterprise governance.

- Repository: [Semantica /semantica](https://github.com/semantica-agi/semantica)
- Tags: how-to-guide
- Published: 2026-09-11

---

**Semantica's Policy Engine, implemented in [`semantica/context/policy_engine.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/context/policy_engine.py), provides versioned CRUD operations, real-time compliance validation against decision metadata, and predictive impact analysis for enterprise governance.**

The `semantica-agi/semantica` repository delivers a **graph-native infrastructure** that converts fragmented enterprise data into a structured, queryable **Context Graph**. Embedded within the intelligence layer (`semantica.context`), the **Policy Engine** enables deterministic evaluation of decision rules against compliance frameworks, featuring immutable versioning, bi-temporal fact storage, and comprehensive audit trails via native graph relationships.

## Architecture and Core Components

Semantica organizes its data pipeline into distinct layers, with the Policy Engine residing in the **Intelligence Layer** alongside ontology management, reasoning engines, and provenance tracking. The engine integrates tightly with `ContextGraph` and `AgentContext` to enforce governance at the point of decision.

The intelligence layer exposes four primary capabilities through the `PolicyEngine` class:

- **Versioned Policy CRUD** – Immutable `policy_id` values with semantic versioning (e.g., `1.0.3`) and full history via `VERSION_OF` relationships.
- **Compliance Evaluation** – Rule interpretation using prefixes like `min_`, `max_`, and `required_` against decision metadata.
- **Impact Analysis** – "What-if" simulations that quantify how proposed rule changes affect existing decisions.
- **Exception Management** – Audit-ready override tracking through `Exception` nodes linked via `GRANTED_EXCEPTION` and `OVERRIDDEN_POLICY` edges.

## Policy Storage and Versioning

### Cypher Backend Implementation

For graph stores supporting Cypher (Neo4j, FalkorDB), the engine creates immutable policy nodes with the following structure:

```cypher
CREATE (p:Policy {
    policy_id: $policy_id,
    name: $name,
    description: $description,
    rules: $rules,
    category: $category,
    version: $version,
    created_at: $created_at,
    updated_at: $updated_at,
    metadata: $metadata
})

```

When `update_policy()` is invoked, the engine generates a new version node and links it to the predecessor via a `VERSION_OF` relationship, preserving the complete historical chain. This satisfies regulatory requirements for **bi-temporal versioning** and rollback capabilities.

### Generic Store Fallback

For backends lacking Cypher support, the engine falls back to the generic `add_node()` API and manually constructs version edges using `find_nodes()` queries. The `get_applicable_policies(category, entities)` method normalizes results from both storage types into standardized `Policy` objects, ensuring consistent behavior across polyglot persistence layers.

## Compliance Evaluation Logic

The `_evaluate_compliance()` helper drives all rule validation. Semantica employs a declarative naming convention where rule keys imply validation logic:

| Prefix | Validation Logic |
|--------|------------------|
| `min_` | Numeric value must be greater than or equal to the rule threshold. |
| `max_` | Numeric value must be less than or equal to the rule threshold. |
| `required_` | For lists, the value must contain all specified items; for scalars, exact equality is required. |
| `allowed_outcomes` | The decision outcome must exist within the provided list of valid strings. |
| `required_categories` | The decision category must match one of the listed values. |
| `min_confidence` | The decision confidence score must meet or exceed the threshold. |

The engine retrieves target values from `decision.metadata` via `_get_metadata_field()`, falling back to direct attribute access when metadata keys are absent. Unknown rule keys are treated as required metadata fields, ensuring strict validation by default.

## Impact Analysis and Simulation

Governance teams use `analyze_policy_impact(policy_id, proposed_rules)` to preview downstream effects before deploying changes. This method aggregates all decisions linked to the current policy via `APPLIED_POLICY` edges, then simulates compliance against the proposed rule set.

The method returns a structured impact report:

```json
{
  "affected_decisions": 42,
  "compliance_impact": -0.12,
  "risk_increase": 0.06,
  "rule_changes": { "max_loan_amount": {"old": 500000, "new": 750000} },
  "total_rule_changes": 1
}

```

**Compliance impact** represents the percentage change in compliant decisions, while **risk increase** quantifies exposure introduced by relaxed constraints. This enables data-driven approval workflows for policy modifications.

## Exception Management and Audit Trails

When business necessity requires violating a policy, `record_exception()` creates an `Exception` node containing the justification, approver identity, and timestamp. The engine automatically creates:

- A `GRANTED_EXCEPTION` edge linking the exception to the decision node.
- An `OVERRIDDEN_POLICY` edge linking the exception to the specific policy version.

This dual-linkage pattern guarantees that auditors can traverse from any decision to its governing policy and any exceptions that modified enforcement, satisfying requirements for **explainable AI** and regulatory reporting.

## Practical Implementation Guide

### Initializing the Policy Engine

Import the core components from `semantica.context` and instantiate with your graph backend:

```python
from semantica.context import ContextGraph, PolicyEngine

# Initialize the knowledge graph with advanced analytics enabled

graph = ContextGraph(advanced_analytics=True)

# Bind the policy engine to the graph store

policy_engine = PolicyEngine(graph_store=graph)

```

### Creating and Versioning Policies

Define policies using the `Policy` dataclass from [`semantica/context/decision_models.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/context/decision_models.py):

```python
from datetime import datetime
from semantica.context.decision_models import Policy

policy = Policy(
    policy_id="loan_policy_01",
    name="Standard Lending Policy",
    description="Baseline rules for consumer loan approvals",
    category="lending",
    version="1.0",
    created_at=datetime.now(),
    updated_at=datetime.now(),
    rules={
        "min_credit_score": 650,
        "max_loan_amount": 500_000,
        "allowed_outcomes": ["approved", "rejected"],
        "required_documents": ["ID", "income_statement"]
    },
    metadata={"entities": ["customer_123"]}
)

policy_id = policy_engine.add_policy(policy)

```

Update to a new version while preserving history:

```python
new_version = policy_engine.update_policy(
    policy_id="loan_policy_01",
    rules={"min_credit_score": 660, "max_loan_amount": 750_000},
    change_reason="Risk appetite increase – Q3 2024"
)

```

### Recording Decisions and Enforcing Compliance

Record decisions through `ContextGraph.record_decision()`, then validate compliance:

```python

# Persist the decision with full metadata

decision_id = graph.record_decision(
    category="lending",
    scenario="Loan request for $450k, credit_score=670",
    reasoning="Applicant meets credit score and loan amount limits",
    outcome="approved",
    confidence=0.92,
    metadata={
        "credit_score": 670,
        "loan_amount": 450_000,
        "documents": ["ID", "income_statement"]
    }
)

# Enforce policy; raises PolicyException if non-compliant

is_compliant = policy_engine.check_compliance(
    decision=graph.get_decision(decision_id),
    policy_id="loan_policy_01"
)

```

### Analyzing Proposed Rule Changes

Simulate the impact of stricter requirements before deployment:

```python
proposed_rules = {
    "min_credit_score": 680,
    "max_loan_amount": 800_000,
    "required_documents": ["ID", "income_statement", "employment_verification"]
}

impact = policy_engine.analyze_policy_impact(
    policy_id="loan_policy_01",
    proposed_rules=proposed_rules
)

print(f"Affected decisions: {impact['affected_decisions']}")
print(f"Compliance delta: {impact['compliance_impact']}")

```

### Recording Policy Exceptions

Document overrides with full justification for audit purposes:

```python
exception_id = policy_engine.record_exception(
    decision_id=decision_id,
    policy_id="loan_policy_01",
    reason="Strategic partnership agreement",
    approver="compliance_manager",
    justification="Partnership signed 2024-02-15 waives standard limits"
)

```

### Retrieving Historical Impact

Identify decisions affected by version transitions:

```python
affected = policy_engine.get_affected_decisions(
    policy_id="loan_policy_01",
    from_version="1.0",
    to_version="1.1"
)

for decision in affected[:5]:
    print(f"Decision {decision['decision_id']} requires review")

```

## Summary

- **Semantica's Policy Engine** in [`semantica/context/policy_engine.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/context/policy_engine.py) couples versioned rule storage with native graph evaluation, eliminating the gap between policy definition and enforcement.
- **Compliance evaluation** relies on declarative rule prefixes (`min_`, `max_`, `required_`) validated against decision metadata via `_evaluate_compliance()`.
- **Bi-temporal versioning** preserves complete policy history through `VERSION_OF` relationships, supporting rollback and audit requirements.
- **Impact simulation** via `analyze_policy_impact()` quantifies the downstream effects of rule changes before production deployment.
- **Exception handling** creates immutable audit trails linking decisions to overridden policies via `GRANTED_EXCEPTION` and `OVERRIDDEN_POLICY` edges.

## Frequently Asked Questions

### What is the primary purpose of the Policy Engine in Semantica?

The Policy Engine governs AI-driven decisions by storing versioned compliance rules in the Context Graph and evaluating decision metadata against those constraints in real-time. It ensures that automated decisions in regulated industries adhere to governance frameworks while maintaining full auditability.

### How does the engine handle policy versioning?

Each policy receives an immutable `policy_id` and a semantic `version` string. When updated, the `update_policy()` method creates a new node linked to the previous version via a `VERSION_OF` edge, preserving the complete historical chain for forensic analysis and rollback operations.

### What rule types can be defined for compliance checking?

Developers declare constraints using prefixed keys: `min_` and `max_` for numeric ranges, `required_` for mandatory fields or list items, `allowed_outcomes` for whitelisted decision states, and `min_confidence` for threshold-based approval. The `_evaluate_compliance()` helper interprets these prefixes dynamically against decision metadata.

### How can teams preview the effects of changing a policy?

The `analyze_policy_impact()` method accepts a `policy_id` and `proposed_rules` dictionary, then simulates compliance against all historical decisions linked to that policy. It returns metrics including `affected_decisions`, `compliance_impact`, and `risk_increase`, enabling governance teams to approve changes based on quantified risk exposure.