菜单
Build or assess a security program strategy and roadmap — current-vs-target maturity, gaps, prioritized initiatives aligned to business objectives and risk appetite, with outcomes, metrics, and budget framing. Use for security program planning, a strategy refresh, or a maturity assessment.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| name | security-strategy |
| description | Build or assess a security program strategy and roadmap — current-vs-target maturity, gaps, prioritized initiatives aligned to business objectives and risk appetite, with outcomes, metrics, and budget framing. Use for security program planning, a strategy refresh, or a maturity assessment. |
A defensible security strategy: where the program is, where it needs to be (driven by business risk, not fashion), and the prioritized, resourced roadmap to get there.
threat-modeling, threat intel) — not "max everything."cyber-risk-quantification).A strategy document: business context · current vs. target maturity · gaps (with
risk) · prioritized initiative roadmap (phased, with outcomes/metrics/budget). Use
security-reporting; visualize the maturity and roadmap with security-diagramming.
Feed the headline into board-deck.
Anchor everything to business risk and appetite — a strategy that maximizes controls regardless of risk burns budget and credibility. Prioritize by risk-reduction-per- dollar, and make the roadmap outcome- and metric-driven so progress is measurable.
Statically review a Claude Code / AI-agent setup for security misconfigurations — risky hooks, over-broad permissions, untrusted or cleartext MCP servers, hardcoded secrets, endpoint redirection, over-privileged agents/skills, and prompt-injection in steering files. Use when asked to audit a `.claude/` directory, `settings.json`, `.mcp.json`, a plugin/marketplace, or `CLAUDE.md`, or to add a config-security gate to CI. Drives the `agentscanner` CLI.
Review the training / MLOps pipeline for security weaknesses — data-poisoning surface, feature-store and data-source trust, experiment-tracking and model-registry access control, secrets, and reproducibility/provenance. Use when assessing how models are built and promoted, not how they're served or consumed.
Review the provenance and integrity of models, datasets, and ML artifacts pulled from hubs/registries — unsafe deserialization (pickle, PyTorch/Keras/joblib), untrusted model sources, model/dataset tampering, and signing. Use when an app loads third-party model weights or datasets, or before promoting a model.
Harden a deployed model inference endpoint — authn/authz, rate limiting and abuse control, input validation, and exposure to model-extraction / inversion / membership-inference attacks. Use when reviewing how a model is served (REST/gRPC, Triton/TorchServe/KServe, or a hosted inference API), not how it was trained.
Export a finished security artifact (report, executive summary, diagram, spreadsheet of findings) to Google Drive — right folder, clear naming, and shareable with the intended audience. Use when a deliverable needs to be handed to stakeholders who live in Drive/Docs/Sheets rather than a wiki or tracker.
Turn a security finding (vuln, pentest issue, review item) into a well-formed Jira issue — mapped severity→priority, remediation/repro in the body, labels and components set, and dedup-checked against existing issues. Use when findings need to become tracked, assignable work in Jira.