Where every claim in SecProve
comes from.

Catalog of security and privacy controls for information systems and organizations. The foundation for federal security compliance.

Official guide to effective prompt engineering with Claude. Covers system prompts, chain-of-thought, few-shot examples, and best practices.

Computer security incident handling guide covering detection, analysis, containment, eradication, and recovery.

Data-driven model for estimating the probability that a vulnerability will be exploited in the wild. Uses ML to prioritize patching.

Knowledge base of adversary tactics and techniques based on real-world observations. The industry standard for threat modeling.

The most comprehensive open-source guide for web application security testing. Covers testing methodology, tools, and techniques.

Generic signature format for SIEM systems. Documentation on writing, testing, and deploying detection rules.

Comprehensive survey of ML applications in cybersecurity. Covers supervised/unsupervised approaches for intrusion detection, malware analysis, phishing detection. Maps ML techniques to security use cases with performance benchmarks.

Open-source testing framework and toolkit for AI governance. Helps organizations validate AI systems against governance principles.

Public database tracking real-world AI incidents and controversies. Invaluable for risk assessment and governance case studies.

Techniques for effective prompting including for safety and security use cases. Covers system prompts, chain-of-thought, and reducing hallucination.

Evaluates model capabilities for autonomous cyber operations at each AI Safety Level (ASL). Defines thresholds where AI capability in offensive security requires additional safeguards. Key reference for responsible AI in offensive security.

Research on using AI for penetration testing automation: reconnaissance, vulnerability discovery, exploit generation. Practitioner perspective on what's practical vs. theoretical.

Authoritative list of vulnerabilities actively exploited in the wild. Used for prioritizing remediation — required for federal agencies.

CISA's decision-tree approach to vulnerability prioritization. Considers exploitation status, automatable exposure, and mission impact.

Open-source platform for managing the end-to-end ML lifecycle. Covers experiment tracking, model registry, and deployment.

Open-source detection rules for Elastic Security. Covers a wide range of attack techniques mapped to MITRE ATT&CK.

Law enforcement perspective on how LLMs enable cybercrime (phishing, malware, social engineering) and how AI assists threat intelligence and investigation.

Open-source platform for managing cyber threat intelligence. Integrates with MITRE ATT&CK and STIX/TAXII.

Common Vulnerability Scoring System version 4.0. The standard method for rating the severity of security vulnerabilities.

Evaluation criteria for AI/ML platforms including security features. Good for questions about what to look for when evaluating AI security tooling.

Market categorization of AI security tools: model monitoring, adversarial robustness, privacy, compliance. Useful for understanding the vendor landscape without favoring specific vendors.

Sec-PaLM and Security AI Workbench for threat intelligence summarization and detection. Shows how LLMs are being applied to SOC workflows — not just pattern matching but contextual threat analysis.

Research on using LLMs for automated triage, alert correlation, and response orchestration. Includes studies on analyst productivity gains and error reduction.

Annual survey data on AI adoption in audit, risk, and compliance functions. Adoption rates, barriers, trust levels. Practitioner perspective on AI-augmented GRC.

LLM-powered security assistant. Technical docs cover prompt engineering for security, incident summarization, KQL generation. Useful for questions about practical LLM integration in SOC, not product features.

MITRE's automated adversary emulation platform. Runs pre-defined or custom attack sequences to test defenses.

Web-based tool for annotating and exploring the ATT&CK matrix. Useful for threat modeling, gap analysis, and red team planning.

Knowledge graph of cybersecurity countermeasures. Maps defensive techniques to the ATT&CK techniques they counter.

Workshop proceedings covering the bidirectional relationship between AI and security. Sections on automation risks (adversarial evasion of AI detectors, automation bias in SOC).

The U.S. government repository of standards-based vulnerability management data. Includes CVE entries, severity scores, and affected product references.

NVIDIA's open-source toolkit for adding programmable guardrails to LLM applications. Supports input/output validation and topic control.

International principles for responsible AI adopted by 46 countries. Covers inclusive growth, transparency, accountability, and security.

Official guidance on prompt construction, system prompts, and safety. The baseline for understanding prompt engineering before adding security-specific techniques.

Comprehensive guide covering AI security threats, privacy risks, and practical controls for AI-powered applications.

Detailed testing techniques for identifying web vulnerabilities. Practical, hands-on approach to security assessment.

SOAR platform with 800+ integrations. The playbook marketplace shows real-world automation patterns: phishing triage, enrichment, containment. Useful for understanding what's actually automatable vs. aspirational.

Practical governance framework providing guidance on deploying AI responsibly. Includes implementation checklists.

Bug bounty platform focused on AI/ML vulnerabilities. Real-world vulnerability data in ML frameworks and models. Good for grounding tool security questions in actual discovered vulnerabilities.

Comprehensive standard for penetration testing methodology. Covers intelligence gathering, threat modeling, vulnerability analysis, exploitation, and reporting.

Analysis of how NLP/LLMs are being used for automated threat intelligence: dark web monitoring, malware family classification, campaign attribution. Practical applications beyond the hype.

Library of tests mapped to the MITRE ATT&CK framework. Small, portable detection tests for validating security controls.

Practitioner-oriented guide to using LLMs in security workflows: log analysis, detection rule writing, incident triage, report generation. Practical prompt templates for security tasks.

Demonstrated GPT-4 exploiting real-world web vulnerabilities autonomously. 73% success rate on day-one CVEs. Key reference for questions about AI-augmented offensive capabilities and the asymmetry debate.

Analysis of how LLMs can be used for offensive security tasks and the implications for defensive guardrails. Covers the dual-use nature of security LLMs.

Comprehensive taxonomy of 58+ prompting techniques with effectiveness analysis. Covers chain-of-thought, few-shot, self-consistency, and adversarial prompting. Academic grounding for prompt engineering questions.

Annual threat landscape reports with empirical data on vulnerability exploitation timelines, patch adoption rates, and the efficacy of risk-based prioritization. Use for data-driven questions, not vendor comparisons.

Platform for ML experiment tracking, model versioning, and collaborative model development with security considerations.

U.S. Executive Order (Oct 2023) establishing AI safety requirements, red-teaming standards, and reporting obligations for frontier AI systems.