Harden models for runtime blocking and agent safety.强化模型以支撑运行时拦截与智能体安全。
Use reinforcement learning on review trajectories to improve precision on hijacking, tool abuse, and high-risk execution paths.对审查轨迹强化学习,提升对劫持、工具滥用与高风险执行路径的识别精度。
Trains regulations, corporate bylaws, and industry standards from formal policy language into proprietary security-and-compliance small models. Delivers context-aware verdicts and auditable enforcement on every AI request.将法规、企业条例与行业规范等形式化制度语言,训入自研安全合规小模型;于请求时结合上下文完成策略判断,并执行可审计的合规处置。
Set P0–P3 priorities and preview enforcement outcomes.配置 P0–P3 优先级,预览处置效果。
At runtime, Policy Engine judges in context and drives risk-appropriate enforcement—with evidence on every decision.运行时结合上下文判断,按风险分级处置,并留存证据。
Also a standalone SKU: Train regulations, corporate bylaws, or industry standards only—supervise employee AI compliance without the full governance stack.亦可独立采购:仅训入法规、企业条例或行业规范,在不采购完整治理栈的情况下监管员工 AI 遵从。
Synthetic data generation, alignment, and reinforcement—applied across security, privacy, and compliance model families.造数据、对齐与强化学习——分别应用于安全、隐私与合规三类模型。
Use reinforcement learning on review trajectories to improve precision on hijacking, tool abuse, and high-risk execution paths.对审查轨迹强化学习,提升对劫持、工具滥用与高风险执行路径的识别精度。
Map PII definitions, trade-secret wording, and internal privacy rules to consistent verdict semantics for rewrite and redaction.将个人数据定义、商业机密表述与内部隐私规则映射为一致的改写与脱敏判断语义。
Scale review cases from sector standards—including edge conditions rarely seen in production logs.从行业标准规模化生成审查样本,覆盖生产日志中罕见的边界情形。
Purpose-built safety and compliance models outperform larger general systems on targeted benchmarks.面向安全与合规的专用模型,在针对性基准上优于更大参数的通用系统。
Evaluated against baseline models on public leaderboards including AgentDojo, HarmBench, and ASB. Contact us for methodology and reproducibility details.在 AgentDojo、HarmBench、ASB 等公开榜单上相对基线模型评测。如需方法与复现细节请联系我们。
Pain: Confidential business data leaves through AI prompts without matching internal classification rules.痛点:商业机密经 AI 提示词流出,却未命中内部分级规则。
Mode IODetect secrets in context; handled according to risk level with enterprise policy as the judgment basis.在上下文中识别机密,按风险等级匹配处置,以企业制度为判断依据。
Pain: Personal and regulated data enters AI workflows faster than manual privacy review can respond.痛点:个人与受监管数据进入 AI 工作流的速度,快于人工隐私审查的响应。
Mode IOPrivacy small models review data class and purpose in real time before external models process the request.隐私小模型在请求触达外部模型前,实时审查数据类型与使用目的。
Pain: Employees use AI tools without knowing whether prompts, files, or agent actions comply with internal bylaws.痛点:员工使用 AI 时,难以判断提示词、文件或智能体动作是否符合内部制度。
Mode IOMatch each request against versioned enterprise rules. Return risk-appropriate actions with cited policy basis.对照版本化企业制度审查每次请求,按风险等级返回处置并引用策略依据。
Ingest, train, version, enforce, and retain evidence—end to end.接入、训练、版本、处置、留证——端到端闭环。
Open the product demo or book a session to map Policy Engine to your AI Governance or cross-border workflow.打开产品演示或预约专场,将 Policy Engine 对接您的 AI Governance 或跨境合规流程。