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Define and enforce policies, access controls, and compliance rules over VeritasReason knowledge graphs.
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基于 SOC 职业分类
| name | policy |
| description | Define and enforce policies, access controls, and compliance rules over VeritasReason knowledge graphs. |
Apply policy rules and checks. Usage: /veritasreason:policy <task> [args]
$ARGUMENTS = task + optional policy name, rule set, or target entity.
check [--rule <name>] [--target <id>]Run policy checks against the graph.
from veritasreason.policy import PolicyEngine
engine = PolicyEngine()
result = engine.check(rule_name=rule_name, target=target)
Output: compliance status, failing rules, and remediation guidance.
listList available policy rules and categories.
rules = engine.list_rules()
Return: rule name, description, severity, and category.
VeritasReason full-stack knowledge graph skill for context graphs, decision intelligence, explainability, extraction, reasoning, visualization, ontology, provenance, policy, and export workflows.
Analyze cause-and-effect relationships in the VeritasReason knowledge graph — causal chains, interventions, counterfactuals, and causal influence scores.
Track and inspect graph changes, diffs, temporal updates, and the impact of new data on VeritasReason knowledge graphs.
Full decision lifecycle in VeritasReason � record, query, find precedents (hybrid/advanced), analyze influence, explain, insights dashboard, list, and record exceptions. Uses AgentContext, ContextGraph, DecisionQuery, CausalChainAnalyzer, DecisionRecorder.
Detect duplicate entities, duplicate groups, and relationship duplicates in VeritasReason using fuzzy matching, schema heuristics, and graph similarity.
Generate, inspect, and use node/text embeddings in VeritasReason — compute Node2Vec embeddings, find similar nodes, score link predictions, batch similarity, and pairwise similarity. Uses NodeEmbedder, SimilarityCalculator, LinkPredictor, and AgentContext. Sub-commands: compute, similar, similarity, predict-link, top-links, batch, pairwise.