Enkrypt AI announced the launch of the Enkrypt AI Academy, a free, structured learning platform designed to strengthen AI security, compliance, and governance capabilities across the global AI ecosystem.
As organizations accelerate adoption of generative AI, large language models (LLMs), retrieval-augmented generation (RAG) systems, and agent-based workflows, security and compliance teams face an expanding threat landscape. At the same time, AI engineers and product teams often lack centralized, practical resources that address how to design and deploy secure AI applications from the outset.
The Enkrypt AI Academy was created to address this industry-wide gap — and to make AI security knowledge broadly accessible to the engineers building the next generation of AI systems.
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## Building AI Security as a Community Standard
The Academy isn’t just an enterprise training program. It is designed as an open resource for the global AI engineering community.
By offering the platform free of charge, Enkrypt AI aims to:
– Establish shared terminology across AI safety, governance, security, and risk
– Provide practical implementation guidance to developers shipping AI features
– Equip security teams with updated threat models for LLMs, RAG systems, and agents
– Support compliance leaders in operationalizing AI governance frameworks
As AI systems increasingly power customer-facing applications, regulated workflows, and automated decision systems, secure design practices must become a default standard — not an afterthought.
## Advancing Practical, Implementation-Focused Learning
The Academy provides a structured, production-informed curriculum for developers, security engineers, compliance leaders, and product teams.
The program includes:
– A structured curriculum covering the full AI security lifecycle
– Short, role-specific learning modules
– Video walkthroughs and applied demonstrations
– Practical guardrail and red teaming guidance
– Self-paced progress tracking
The curriculum spans six core domains:
– **Foundations** — AI architecture and threat modeling fundamentals
– **Guardrails** — Protections against injection attacks, data leakage, and unsafe outputs
– **Red Teaming** — Adversarial testing methodologies
– **Policies and Compliance** — Operationalizing governance controls
– **Endpoints and Integrations** — Securing models, APIs, and agent frameworks
– **MCP Security** — Protecting emerging protocol layers connecting AI to tools and data
Together, these domains reinforce secure-by-design development practices across the AI lifecycle.
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