Over 160 production-ready skill documents (SKILL.md) for LLM coding agents like Claude Code, Cursor, and Codex.Each skill contains expert-level configs, CLI commands, troubleshooting guides, and ready-to-run scripts spanning six domains: DevOps (CI/CD, Kubernetes, observability), Security (vulnerability scanning, secrets, hardening), Infrastructure (AWS/Azure/GCP, networking, databases, local inference stacks), LLM engineering (agent evals, RAG infrastructure, inference scaling), Compliance (SOC2, HIPAA, GDPR, ISO 27001), and IT operations. Skills install in under a minute via the `npx skills add` CLI or a simple git clone, and the agent discovers, matches, and activates them on demand.
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This article discusses the importance of integrating responsible AI practices with security measures, particularly within organizations like Grammarly. It emphasizes treating responsible AI as a product principle, securing the AI supply chain, and the interconnectedness of responsible AI and security. It also touches on the future of AI customization and control.
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The LinkedIn article, “Leading With Trust: When Responsible AI and Security Collide,” by Grammarly’s CISO Sacha Faust, argues that responsible AI isn’t just an ethical or compliance issue, but a critical security imperative.
**Key takeaways:**
* **Responsible AI as a Product Principle:** Organizations should integrate responsible AI into product design, asking questions about values alignment, employee enablement, and proactive risk identification.
* **Secure the AI Supply Chain:** Organizations must trace AI model origins, evaluate vendors, and control key components (moderation, data governance, deployment) to mitigate risks.
* **Blur the Lines:** Responsible AI and AI security are intertwined – security ensures systems *work* as intended, while responsible AI ensures they *should* behave a certain way.
* **Certification & Transparency:** Frameworks like ISO/IEC 42001:2023 can signal commitment to AI governance and build trust.
* **Future Focus: Customization vs. Control:** Leaders need to address policies and safeguards for increasingly customized and autonomous AI systems, balancing freedom with oversight.
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The LinkedIn article, “Leading With Trust: When Responsible AI and Security Collide,” by Grammarly’s CISO Sacha Faust, argues that responsible AI isn’t just an ethical or compliance issue, but a critical security imperative.
**Key takeaways:**
* **Responsible AI as a Product Principle:** Organizations should integrate responsible AI into product design, asking questions about values alignment, employee enablement, and proactive risk identification.
* **Secure the AI Supply Chain:** Organizations must trace AI model origins, evaluate vendors, and control key components (moderation, data governance, deployment) to mitigate risks.
* **Blur the Lines:** Responsible AI and AI security are intertwined – security ensures systems *work* as intended, while responsible AI ensures they *should* behave a certain way.
* **Certification & Transparency:** Frameworks like ISO/IEC 42001:2023 can signal commitment to AI governance and build trust.
* **Future Focus: Customization vs. Control:** Leaders need to address policies and safeguards for increasingly customized and autonomous AI systems, balancing freedom with oversight.
CHOROLOGY.ai automates data compliance mandates like CCPA and GDPR through automated data discovery, classification, mapping, and risk assessment. It supports various data types and repositories, both on-premise and in the cloud.
Chorology's AI-based Compliance Engine is the first to automatically identify, classify, and contextualize sensitive data at scale, breaking compliance industry barriers and future-proofing enterprises for emerging regulations.
