external/anthropic-cybersecurity-skills/skills/conducting-cyber-risk-assessment-with-nist-800-30/SKILL.md
Conduct a defensible cybersecurity risk assessment using the NIST SP 800-30 Rev 1 methodology: prepare scope and a risk model, identify threat sources and threat events, identify vulnerabilities and predisposing conditions, determine likelihood and impact, compute risk, and communicate results as a prioritized risk register. Use when an organization needs an actual risk *assessment* (not a maturity score), when a control framework (CSF, ISO 27001, RMF, SOC 2, PCI) requires a documented risk analysis as input, when leadership asks "what are our top risks and how bad are they", when assessing risk for a new system or major change, or when building a risk register from scratch. This is the methodology that feeds framework selection, ATO packages, and treatment decisions. Keywords: risk assessment, NIST 800-30, threat modeling, likelihood and impact, risk register, risk analysis, threat sources, vulnerabilities, risk determination, qualitative risk, risk matrix, residual risk, risk treatment.
npx skillsauth add seikaikyo/dash-skills conducting-cyber-risk-assessment-with-nist-800-30Install this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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references/standards.md).NIST SP 800-30 Rev 1 defines four steps. Steps 1 and 4 bookend the assessment; Step 2 is the analytic core.
Define and document:
Work through the analytic tasks in order. The 800-30 appendices provide the reference taxonomies (D–I).
2a. Identify threat sources (Appendix D). Classify by type: Adversarial (individuals, groups, nation-states — characterize capability, intent, targeting), Accidental (user error), Structural (equipment/software failure), Environmental (natural disasters, infrastructure outages).
2b. Identify threat events (Appendix E). The specific actions a source could take (e.g., "adversary exfiltrates credentials via phishing then moves laterally"). Map adversarial events to MITRE ATT&CK techniques for traceability.
2c. Identify vulnerabilities and predisposing conditions (Appendix F). Weaknesses (missing MFA, unpatched service) and conditions that make exploitation more or less likely (internet exposure, flat network, lack of segmentation).
2d. Determine likelihood (Appendix G). Assess the likelihood that a threat event is initiated (adversarial) or occurs (non-adversarial), and the likelihood it results in adverse impact given the vulnerabilities. Combine into an overall likelihood on the agreed scale.
2e. Determine impact (Appendix H). Magnitude of harm if the event succeeds — to operations, assets, individuals, other organizations, or the nation. Express against the agreed scale and in business terms.
2f. Determine risk (Appendix I). Risk is a function of likelihood and impact. Plot each threat event on the risk matrix (e.g., likelihood × impact → Very Low … Very High). Record the risk level, the contributing factors, and uncertainty/assumptions.
Produce the risk register and an executive briefing. For each risk: the threat event, affected assets, likelihood, impact, risk level, key contributing vulnerabilities, and a recommended treatment. Rank by risk level so decision-makers see the top risks first.
Risk is not static. Define a refresh cadence and the triggers that force re-assessment (new system, major architecture change, significant incident, new threat intel). Track risk-acceptance decisions and treatment progress over time.
Hand the ranked register to risk owners. For each risk choose a treatment — mitigate (add/strengthen controls), transfer (insurance, contractual), avoid (stop the activity), or accept (document residual risk with an authorizing signature). Re-score residual risk after planned controls to show the post-treatment position.
| Concept | Definition | |---|---| | Threat source | The cause of a threat event: adversarial, accidental, structural, or environmental. | | Threat event | A specific action or occurrence that could cause harm (mapped to ATT&CK for adversarial cases). | | Vulnerability | A weakness that a threat event can exploit. | | Predisposing condition | A condition that increases or decreases the likelihood of adverse impact (e.g., internet exposure). | | Likelihood | The chance a threat event initiates/occurs and results in adverse impact. | | Impact | The magnitude of harm if the event succeeds. | | Risk | A function of likelihood and impact; the expected harm to the organization. | | Inherent vs residual risk | Risk before vs after planned/implemented controls. | | Risk tolerance / appetite | The level of risk leadership is willing to accept. | | Risk register | The prioritized record of risks, scores, owners, and treatments. |
Produce a Risk Assessment Report using assets/template.md, containing:
Use scripts/process.py to score the register from a risk-input JSON (likelihood × impact → risk level on a configurable matrix), rank risks, and emit the register table.
tools
Conduct comprehensive GDPR compliance assessments by evaluating data processing activities against EU Regulation 2016/679, including Article 30 records of processing, lawful basis validation, data subject rights implementation, Data Protection Impact Assessments (DPIAs) under Article 35, breach notification procedures, international transfer safeguards (SCCs, adequacy decisions), and technical/organizational measures under Article 32. Use when processing personal data of EU residents, preparing for supervisory authority audits, implementing privacy-by-design for new systems, scoping compliance gaps for M&A due diligence, assessing third-party processors, or responding to data subject access requests at scale. Incorporates 2026 guidance from ICO, EDPB, and post-Data (Use and Access) Act 2025 UK-GDPR considerations. Do not use for implementing specific Article 32 controls — use implementing-gdpr-data-protection-controls; or for DSAR automation — use implementing-gdpr-data-subject-access-request.
tools
Parse Windows forensic artifacts—$MFT/$J (MFTECmd), Prefetch (PECmd), registry hives (RECmd), shellbags, and Amcache—into normalized CSV/JSON with Eric Zimmerman's EZ Tools, then load results into Timeline Explorer for analysis. Use during DFIR/incident-response investigations, after triage collection (e.g. with KAPE), to establish program execution, file/folder access, and persistence evidence from acquired forensic images.
development
Build automated multi-turn adversarial attacks against conversational LLM targets using Microsoft PyRIT's RedTeamingOrchestrator, CrescendoOrchestrator (gradual escalation), and TreeOfAttacksWithPruningOrchestrator (adaptive branching), with scorer feedback loops and persisted conversation memory. Use when single-shot LLM scanning is insufficient and you need multi-turn, scorer-driven AI red-team campaigns against a chatbot or agent.
testing
Stand up MISP, enable and cache curated threat feeds (CIRCL, abuse.ch, Feodo Tracker), apply warninglists to suppress false positives, query indicators with PyMISP, and export attributes as auto-generated Suricata/Sigma/Wazuh detection rules. Use when maturing a MISP instance to actively drive detection, curating threat feeds with quality controls, or automating IOC-to-detection pipelines for the SIEM/IDS.