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# OWASP GenAI Data Security Initiative
<div align="center">

Part of the [OWASP GenAI Security Project](https://genai.owasp.org/) · [Data Security Initiative Page](https://genai.owasp.org/initiative/data-security/)
# 🛡️ OWASP GenAI Data Security Initiative

**Community-developed taxonomies, crosswalks, datasets, and tooling for securing data in generative and agentic AI systems.**

Part of the [OWASP GenAI Security Project](https://genai.owasp.org/) · [Initiative Page](https://genai.owasp.org/initiative/data-security/)

[![OWASP](https://img.shields.io/badge/OWASP-GenAI%20Security%20Project-1a4b8c?logo=owasp&logoColor=white)](https://genai.owasp.org/)
[![Crosswalk](https://img.shields.io/badge/Crosswalk-live%20webapp-2ea44f)](https://genai-security-project.github.io/crosswalk/)
[![DSGAI](https://img.shields.io/badge/DSGAI%202026-21%20risks-6d28d9)](https://genai.owasp.org/resource/owasp-genai-data-security-risks-mitigations-2026/)
[![Frameworks](https://img.shields.io/badge/frameworks-26-orange)](https://genai-security-project.github.io/crosswalk/#/frameworks)
[![Scanner](https://img.shields.io/badge/DSGAI%20Scanner-v0.3.0-16a34a)](dsgai_scanner_tool/README.md)
[![License](https://img.shields.io/badge/license-CC%20BY--SA%204.0-lightgrey)](https://creativecommons.org/licenses/by-sa/4.0/legalcode)
[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen)](CONTRIBUTING.md)

[**🌐 Explore the Crosswalk webapp →**](https://genai-security-project.github.io/crosswalk/)

[White Papers](#deliverables) · [Crosswalk](#crosswalk) · [Scanner](#scanner) · [Datasets](#datasets) · [Contribute](#contribute)

</div>

---

## Overview

The OWASP GenAI Data Security Initiative addresses the data security risks unique to Large Language Models, Generative AI, and Agentic AI systems. As AI introduces new data surfaces — prompts, context windows, embeddings, vector stores, agent traces, tool payloads — and new failure modes — prompt-driven extraction, cross-session bleed, inference attacks, plugin data drains — traditional data security frameworks no longer map cleanly to what needs protection.
The OWASP GenAI Data Security Initiative addresses the data security risks unique to Large Language Models, Generative AI, and Agentic AI systems. AI introduces new data surfaces — prompts, context windows, embeddings, vector stores, agent traces, tool payloads — and new failure modes — prompt-driven extraction, cross-session bleed, inference attacks, plugin data drains — that traditional data security frameworks no longer map cleanly onto.

This initiative produces community-developed, peer-reviewed guidance to help organizations understand and address these emerging challenges. All materials are released under [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/legalcode).
This initiative produces community-developed, peer-reviewed guidance, interactive tooling, and open datasets to help organizations understand and address these challenges. All materials are released under [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/legalcode).

### At a glance

| Deliverable | What it is | Where |
| --- | --- | --- |
| 📄 **DSGAI Risk Taxonomy 2026** | 21 GenAI data security risks with tiered mitigations | [White paper](https://genai.owasp.org/resource/owasp-genai-data-security-risks-mitigations-2026/) |
| 📄 **Data Security Best Practices** | Companion implementation guide | [White paper](https://genai.owasp.org/resource/llm-and-gen-ai-data-security-best-practices/) |
| 🌐 **Framework Crosswalk** | 51 risk entries × 26 frameworks, 3,800+ control mappings, interactive webapp | [Webapp](https://genai-security-project.github.io/crosswalk/) · [Repo](https://github.com/GenAI-Security-Project/crosswalk) |
| 🛡️ **DSGAI Scanner** | Deterministic compliance scanner for AI codebases (SARIF, CI-ready) | [`dsgai_scanner_tool/`](dsgai_scanner_tool/) |
| 📊 **Community Datasets** | Exploits, vulnerabilities, test cases, incidents, traces | [`datasets/`](datasets/) |
| ✅ **Data Validation** | Schemas and checks for contributed data | [`data_validation/`](data_validation/) |
| 📚 **Literature Review** | Categorized corpus of LLM-security research papers | [`literature/`](literature/) |

---

## Key Deliverables
<a id="deliverables"></a>

## 📄 Key Deliverables

### GenAI Data Security Risks and Mitigations 2026 (v1.0)

📄 [Download PDF](https://genai.owasp.org/resource/owasp-genai-data-security-risks-mitigations-2026/) · Released March 2026

A comprehensive enumeration of 21 data security risks specific to GenAI systems, each with tiered mitigations (Foundational → Hardening → Advanced) designed for organizations at different maturity levels. This is not a Top 10 — it is a structured risk taxonomy following data as it moves through a GenAI system.
A comprehensive enumeration of 21 data security risks specific to GenAI systems, each with tiered mitigations (Foundational → Hardening → Advanced) for organizations at different maturity levels. This is not a Top 10 — it is a structured risk taxonomy following data as it moves through a GenAI system.

Cross-referenced to the [OWASP Top 10 for LLM Applications](https://genai.owasp.org/llm-top-10/) and the [OWASP Top 10 for Agentic Applications 2026](https://genai.owasp.org/initiatives/agentic-security-initiative/).

Expand Down Expand Up @@ -61,66 +93,113 @@ The companion implementation guide covering data security principles, secure dep

---

## Framework Crosswalk
<a id="crosswalk"></a>

The initiative maintains a comprehensive crosswalk of GenAI data security risks against 21 widely recognized cybersecurity and AI frameworks. The crosswalk covers all 41 entries across the OWASP LLM Top 10 2025, Agentic Top 10 2026, and DSGAI Risk Taxonomy.
## 🌐 Framework Crosswalk

### 🌐 [Crosswalk →](https://genai-security-project.github.io/crosswalk/)
The web application provides an interactive interface for exploring crosswalk mappings, framework coverage, incident data, security tooling, implementation recipes, and a glossary of key terms.
The initiative's flagship interactive deliverable: **51 risk entries** across four OWASP source lists — LLM Top 10 2026, Agentic Top 10 2026, DSGAI 2026, and Agentic Skills Top 10 — mapped to **26 industry frameworks** through **3,800+ individual control mappings**, with **131 tracked AI security incidents**.

Crosswalk source files are maintained in the dedicated [`GenAI-Security-Project/crosswalk`](https://github.com/GenAI-Security-Project/crosswalk) repository.
> ### [🚀 Open the Crosswalk webapp](https://genai-security-project.github.io/crosswalk/)
>
> | Feature | What it does |
> | --- | --- |
> | [**Score Your Coverage**](https://genai-security-project.github.io/crosswalk/#/score) | Select your frameworks, see your GenAI risk coverage gaps, validate with Garak/PyRIT results |
> | [**Explorer**](https://genai-security-project.github.io/crosswalk/#/explorer) | Search and filter all 51 entries; view mapped controls across every framework |
> | [**Coverage Matrix**](https://genai-security-project.github.io/crosswalk/#/frameworks) | Interactive 51 × 26 matrix — click any cell for the specific controls |
> | [**Incidents**](https://genai-security-project.github.io/crosswalk/#/incidents) | Real-world AI security incidents, filterable by severity, year, and layer |
> | [**Submit a Standard**](https://genai-security-project.github.io/crosswalk/#/submit) | Propose any framework for automated mapping |

### Frameworks Covered
Crosswalk source data, per-framework compliance gap reports (Markdown, CSV, JSON, OSCAL), and enterprise exports (STIX 2.1, OSCAL Component Definition) are maintained in the dedicated [`GenAI-Security-Project/crosswalk`](https://github.com/GenAI-Security-Project/crosswalk) repository.

**Cybersecurity & AI Standards** — NIST CSF 2.0, NIST AI RMF 1.0, ISO/IEC 27001, ISO/IEC 42001, ISO/IEC 23894, ISO/IEC 5338
**Frameworks covered:**

**Threat Modeling & Adversarial** — MITRE ATT&CK, MITRE ATLAS, STRIDE, FAIR
- **AI governance & regulation** — NIST AI RMF 1.0 · ISO/IEC 42001 · EU AI Act · ENISA Multilayer Framework · AIUC-1 · CoSAI *(candidate)* · EU AI Act Code of Practice *(candidate)*
- **Security management & compliance** — ISO/IEC 27001 · NIST CSF 2.0 · SOC 2 · PCI DSS v4.0 · CIS Controls v8.1 · FedRAMP
- **Threat modeling & adversarial** — MITRE ATLAS · MAESTRO (CSA) · STRIDE · CWE/CVE
- **Testing & verification** — OWASP ASVS · OWASP AISVS 1.0 · OWASP AI Testing Guide
- **Secure SDLC, identity & maturity** — NIST SP 800-218A · OWASP SAMM · OWASP NHI Top 10
- **OT/ICS & financial resilience** — ISA/IEC 62443 · NIST SP 800-82 Rev 3 · DORA

**Application Security & Compliance** — CIS Controls, ASVS, SOC 2, PCI DSS, ENISA, CycloneDX ML SBOM
---

**Governance & Architecture** — COBIT, OPENCRE, SAMM, BSIMM
<a id="scanner"></a>

**OT / Industrial AI Security** — ISA/IEC 62443
## 🛡️ DSGAI Scanner

---
**v0.3.0** · [`dsgai_scanner_tool/`](dsgai_scanner_tool/) — audits GenAI and agentic codebases against all 21 DSGAI controls.

## Workstreams
A **deterministic engine** owns the pattern matching — 107 PCRE rules run via ripgrep produce identical findings on identical input, so you get a reproducible compliance artifact rather than an LLM opinion. An optional [Claude Code](https://www.anthropic.com/claude-code) skill orchestrates the run and writes the narrative report.

1. **Data Collection** — Public open call for real-world vulnerability data and incident reports related to LLMs and GenAI applications. Submit data through the Slack channel or by opening an issue in this repository.
- 🎯 **Deterministic & reproducible** — a compliance report you can diff; secrets never leave your machine
- 🌐 **Multi-language** — Python, JavaScript/TypeScript, Java, Kotlin, Go, plus credential coverage for C#, Rust, Ruby
- 🐛 **CVE enrichment without hallucination** — queries OSV (+ NVD for CVSS) per pinned dependency across 6 ecosystems
- 🧰 **Meets your toolchain** — SARIF 2.1.0 for GitHub Code Scanning, a [Semgrep rule-pack export](dsgai_scanner_tool/dist/dsgai.semgrep.yaml), and a [gitleaks pack](dsgai_scanner_tool/integrations/gitleaks/dsgai.toml) for pre-commit
- 💸 **$0 CI path** — the CLI needs only Python 3.10+ and ripgrep; no LLM, no account

2. **Framework Crosswalk** — Crosswalking the [OWASP Top 10 for LLM Applications 2025](https://genai.owasp.org/llm-top-10/) and [Agentic Top 10 2026](https://genai.owasp.org/initiatives/agentic-security-initiative/) to recognized cybersecurity and AI frameworks. See the [Crosswalk](https://genai-security-project.github.io/crosswalk/) and the [`GenAI-Security-Project/crosswalk`](https://github.com/GenAI-Security-Project/crosswalk) repository for current crosswalk data.
```bash
git clone --depth 1 https://github.com/GenAI-Security-Project/GenAI-Data-Security-Initiative
python GenAI-Data-Security-Initiative/dsgai_scanner_tool/cli/dsgai_scan.py scan . \
--sarif DSGAI-scan.sarif --json-out DSGAI-scan.json
```

3. **Data Security Risks & Best Practices** — Research, authoring, and maintenance of the initiative's two published white papers: the DSGAI risk taxonomy and the companion implementation guide. This workstream also maintains alignment with the broader OWASP GenAI Security Project deliverables.
<details>
<summary><strong>Sample report</strong></summary>

![DSGAI Scanner sample report](dsgai_scanner_tool/DSGAI-samplereport.png)

4. **Community Datasets** — Development of open, community-contributed datasets for research, benchmarking, and security testing. See the [`/datasets`](https://github.com/GenAI-Security-Project/GenAI-Data-Security-Initiative/tree/main/datasets) directory for current and proposed datasets.
</details>

5. **Data Validation** — Automated and peer-reviewed validation of all contributed data. Scripts in [`/data_validation`](https://github.com/GenAI-Security-Project/GenAI-Data-Security-Initiative/tree/main/data_validation) ensure integrity and accuracy.
See the [scanner README](dsgai_scanner_tool/README.md) for the full feature set, CI/CD integration, and the Claude Code skill.

---

## Datasets
<a id="datasets"></a>

## 📊 Community Datasets

Open, community-contributed datasets for research, benchmarking, and security testing — every entry mapped to the DSGAI taxonomy and validated before merge. See [`datasets/`](datasets/) and [CONTRIBUTING.md](CONTRIBUTING.md).

| Dataset | Contents | Status |
| --- | --- | --- |
| [Exploit Dataset](datasets/exploit_dataset/) | Documented exploit techniques targeting LLM applications, keyed to MITRE ATLAS | ✅ 59 entries |
| [Vulnerability Dataset](datasets/vulnerability_dataset/) | Real-world CVEs affecting LLM applications | ✅ 47 entries |
| [Risk Assessment Dataset](datasets/riskassessment_dataset/) | Mapped risk assessments for LLM deployments | ✅ 23 entries |
| [Prompt Injection & Data Extraction Test Cases](datasets/promptinj_dataextraction_testcases/) | Adversarial prompts and extraction techniques for red-teaming and regression testing | ✅ 300+ cases |
| [RAG Poisoning & Retrieval Integrity](datasets/rag_dataset/) | Synthetic poisoning fixtures for testing vector store integrity and retrieval filtering | 🌱 growing — contribute |
| [Incident Dataset](datasets/incident_dataset/) | Anonymized real-world GenAI data security incidents | 🙋 seeking contributors |
| [Agent Data Flow & Tool Exchange Traces](datasets/agentdataflow_toolexchange_traces/) | Sanitized traces of agent tool calls and plugin data exchanges (DSGAI06) | 🙋 seeking contributors |
| [Cross-Framework Mapping Dataset](datasets/crossframework_mapping_dataset/) | Machine-readable DSGAI-to-framework control mappings | ↗ maintained in the [crosswalk repo](https://github.com/GenAI-Security-Project/crosswalk) |

Contributions to every dataset are validated by the schemas and checks in [`data_validation/`](data_validation/) — see the [setup guide](data_validation/SETUP.md).

### Current
---

- **Vulnerability Dataset** — Real-world vulnerabilities affecting LLM applications
- **Exploit Dataset** — Documented exploits and attack techniques targeting LLMs
- **Risk Assessment Dataset** — Mapped risk assessments for various LLM deployments
## 🗺️ Repository Map

### Proposed Community Datasets
```text
├── datasets/ ← community datasets (8 tracks, one entry per file)
├── data_validation/ ← JSON schemas + validation pipeline for contributions
├── dsgai_scanner_tool/ ← DSGAI Scanner v0.3.0 (deterministic CLI + Claude Code skill)
├── literature/ ← categorized LLM-security literature corpus
├── CONTRIBUTING.md ← contribution paths by role and workstream
└── SECURITY.md ← vulnerability reporting policy
```

We are looking for contributors to help build datasets for the broader AI security community. Priority areas include:
---

- **GenAI Data Security Incident Database** — Structured, anonymized catalog of real-world data security incidents in GenAI deployments (leakage events, cross-tenant bleed, RAG exfiltration, agent credential exposure), focused on failure modes enumerated in DSGAI01–DSGAI21.
- **Prompt Injection & Data Extraction Test Cases** — Adversarial prompts and extraction techniques mapped to the DSGAI risk taxonomy for red-teaming and regression testing.
- **RAG Poisoning & Retrieval Integrity Dataset** — Benign and adversarial document sets for testing vector store integrity, retrieval-time redaction, and poisoning detection.
- **Cross-Framework Control Crosswalk Dataset** — Machine-readable crosswalk of DSGAI risks to controls across NIST CSF 2.0, NIST AI RMF, MITRE ATLAS, ISO/IEC 42001, and OWASP LLM Top 10 for automated compliance gap analysis.
- **Agent Data Flow & Tool Exchange Traces** — Sanitized traces of agentic AI tool calls, plugin data exchanges, and delegation chains supporting research into DSGAI06 and related agent security patterns.
## 🧭 Workstreams

If you are interested in contributing, join the Slack channel or reach out directly.
| # | Workstream | Focus |
| --- | --- | --- |
| 1 | **Data Collection** | Open call for real-world vulnerability data and incident reports — submit via [Slack](https://owasp.slack.com) or a GitHub issue |
| 2 | **Framework Crosswalk** | Mapping OWASP GenAI risk lists to industry frameworks — see the [webapp](https://genai-security-project.github.io/crosswalk/) and [crosswalk repo](https://github.com/GenAI-Security-Project/crosswalk) |
| 3 | **Risks & Best Practices** | Research, authoring, and maintenance of the initiative's white papers |
| 4 | **Community Datasets** | Building the open datasets above — schemas, curation, review |
| 5 | **Data Validation** | Automated and peer-reviewed validation of all contributed data |

---

## AI Risk Database Collaboration
## 🤝 AI Risk Database Collaboration

The initiative collaborates with leading AI risk authorities to consolidate efforts and avoid fragmented approaches to risk identification:

Expand All @@ -132,18 +211,20 @@ Community members are encouraged to report new GenAI data security risks to thes

---

## How to Contribute
<a id="contribute"></a>

- **Slack:** Join `#team-genai-data-security-initiative` on the [OWASP Slack workspace](https://owasp.slack.com) · [New to OWASP Slack? Join here](https://owasp.org/slack/invite)
- **GitHub:** Submit issues or pull requests to this repository — see [CONTRIBUTING.md](CONTRIBUTING.md) for detailed guidelines
- **Security:** Report vulnerabilities via [GitHub Private Vulnerability Reporting](https://github.com/GenAI-Security-Project/GenAI-Data-Security-Initiative/security/advisories/new) — see [SECURITY.md](SECURITY.md)
- **Contact:** Reach out to Emmanuel Guilherme Junior (Initiative Lead) via [Slack](https://owasp.slack.com) or [LinkedIn](https://www.linkedin.com/in/emmanuelgjr/)
## 🙌 How to Contribute

All contributions are welcome — from security practitioners, AI engineers, researchers, compliance professionals, and anyone working to secure GenAI systems.

- 💬 **Slack:** Join `#team-genai-data-security-initiative` on the [OWASP Slack workspace](https://owasp.slack.com) · [New to OWASP Slack? Join here](https://owasp.org/slack/invite)
- 🧑‍💻 **GitHub:** Submit issues or pull requests — [CONTRIBUTING.md](CONTRIBUTING.md) has starting points for every role and experience level
- 🔒 **Security:** Report vulnerabilities via [GitHub Private Vulnerability Reporting](https://github.com/GenAI-Security-Project/GenAI-Data-Security-Initiative/security/advisories/new) — see [SECURITY.md](SECURITY.md) · researchers are credited in [SECURITY-THANKS.md](SECURITY-THANKS.md)
- ✉️ **Contact:** Reach out to Emmanuel Guilherme Junior (Initiative Lead) via [Slack](https://owasp.slack.com) or [LinkedIn](https://www.linkedin.com/in/emmanuelgjr/)

---

## OWASP GenAI Security Project — Initiatives
## 🧩 OWASP GenAI Security Project — Initiatives

This initiative is one of several under the [OWASP GenAI Security Project](https://genai.owasp.org/):

Expand All @@ -160,15 +241,15 @@ This initiative is one of several under the [OWASP GenAI Security Project](https

---

## Acknowledgments
## 🙏 Acknowledgments

**Initiative Lead:** [Emmanuel Guilherme Junior](https://www.linkedin.com/in/emmanuelgjr/)

This initiative is made possible by the contributions of its authors, contributors, and reviewers from across the global AI security community. Thank you to everyone who has helped build and shape this community resource. Full contributor lists are included in each published document.

---

## License
## 📜 License

All materials produced by this initiative are licensed under [Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)](https://creativecommons.org/licenses/by-sa/4.0/legalcode).

Expand Down