Forward-Chaining Engine

90 inference rules across 7 compliance frameworks with agenda-based evaluation

Forward-Chaining Engine

The ForwardChainEngine is the deterministic core of Quint’s scoring pipeline. It evaluates events against 90 inference rules using agenda-based forward-chaining — a Rete-style pattern matching algorithm that reaches a fixed point in under 1ms.

Algorithm

class ForwardChainEngine:
    def evaluate(
        self,
        initial_facts: list[Fact],
        tenant_frameworks: set[str] | None = None
    ) -> InferenceResult

Execution flow:

1. Initialize working memory

   Extract initial facts from the event using extract_facts(event, policies, baseline). This produces ~50+ facts covering action type, target classification, data sensitivity, bulk operations, input patterns, and behavioral signals.

2. Forward-chaining loop

   Until fixed point or MAX_ITERATIONS (50):

   1. For each rule (sorted by priority, lower = first):
      • Skip if already fired (single-fire per rule per evaluation)
      • Check framework requirement against tenant subscriptions
      • Check ALL conditions match facts in working memory
      • If conditions met: fire rule
   2. Execute rule actions: assert_fact, add_score, add_violation, add_mitigation
   3. Track newly fired rules
   4. Exit if no new rules fired (fixed point reached)
3. Compute result

   Aggregate score deltas, collect violations and mitigations, compute confidence from rule coverage and data completeness.

Complexity: O(R × I × F) where R = rule count, I = iterations, F = fact count

Rule Structure

@dataclass(frozen=True)
class InferenceRule:
    name: str                        # Unique rule ID
    priority: int                    # Execution order (lower = first)
    conditions: tuple[FactPattern]   # ALL must match
    actions: tuple[RuleAction]       # Fire when conditions met
    confidence_decay: float = 0.95   # Applied to derived facts
    explanation_template: str = ""   # Human-readable
    frameworks: tuple[str] = ()      # Related frameworks
    requires_framework: str | None   # Gate on tenant subscription

Example Rule

InferenceRule(
    name="bulk_external_exfiltration",
    priority=10,
    conditions=(
        FactPattern(fact_type="bulk_access"),
        FactPattern(fact_type="targets_external"),
    ),
    actions=(
        RuleAction(action_type="add_score", score_delta=50),
        RuleAction(
            action_type="add_violation",
            text="Bulk data export to external resource",
            articles=("SOC2 CC6.1", "ISO27001 A.13.2"),
            severity="high",
        ),
        RuleAction(
            action_type="assert_fact",
            fact_type="exfiltration_risk",
            predicate="bulk_external",
        ),
        RuleAction(
            action_type="add_mitigation",
            text="Restrict bulk exports to internal resources",
        ),
    ),
    explanation_template="Bulk data access + external target = potential exfiltration",
)

Rule Categories (90 Total)

Category	Count	Scope
Common	11	Universal rules (bulk access, external targets, PII)
GDPR	~15	Consent, data minimization, cross-border transfer
HIPAA	~12	PHI exposure, encryption, minimum necessary
SOC2	~12	Access control, change management, availability
PCI-DSS	~8	Cardholder data, encryption, network segmentation
PII	~6	Personal data access patterns
OWASP	~8	Injection, broken auth, security misconfiguration
ISO27001	~5	Information security management
OWASP LLM Top 10	~8	Prompt injection, training data poisoning, excessive agency
OWASP Agentic Top 10	~6	Tool poisoning, MCP attacks, multi-turn manipulation
Behavioral	~5	Novel resources, rare actions, off-hours, volume spikes

Fact Extraction

The extract_facts() function transforms raw events into ~50+ initial facts:

Action Type Facts

• action_is — canonical action type
• action_is_read, action_is_write, action_is_delete, action_is_execute

Target Classification Facts

• targets_external, targets_internal
• targets_production, targets_public_storage, targets_non_eu

Data Sensitivity Facts

• accesses_sensitive, accesses_phi, accesses_ssn
• accesses_pan, accesses_contact_info, accesses_special_category
• Detected from 51 predefined sensitive field names + custom policies

Bulk Operation Facts

• bulk_access — high row count or SELECT * patterns
• excessive_fields — touching many data fields

LLM/Agent Pattern Facts

• prompt_injection, system_prompt_extraction
• agent_code_execution, excessive_autonomy
• rag_poisoning, mcp_tool_poisoning
• multi_turn_attack, encoding_evasion

Behavioral Facts (from baseline)

• novel_resource — resource never accessed before
• rare_action — action type < 1% of historical
• unusual_field_access — touching new field types
• off_hours_activity, volume_spike

Tenant Framework Management

Frameworks are automatically inferred from customer policies:

# Default frameworks for all tenants
defaults = {gdpr, soc2, pii, owasp, owasp_llm, owasp_agentic}

# Auto-detected from policies
if has_phi_policies → add hipaa
if has_payment_policies → add pci_dss
if has_iso_policies → add iso27001

Rules with requires_framework only fire if the tenant subscribes to that framework. Framework subscriptions are cached with a 5-minute TTL per tenant.

Risk Level Mapping

Score Range	Risk Level
1-10	none
11-30	low
31-55	medium
56-80	high
81-100	critical