Casky.AI

Black Hat Arsenal Lab 02 - Cybersecurity Skills for AI

Palo Alto Networks

VMware

Rajesh Jain

Cybersecurity Investigation Platform Builder @ Casky.AI

Use the largest Open source AI Skills repo to analyze a real vulnerabilities. 

Think like a Senior SOC Analyst

The multi-agent investigation pipeline helps you find the exact findings.

Use Multi Agent Investigation harness to pin the findings

Detailed CISO ready findings report with remediation plans, MITRE mappings, CVSS Scores. 

Create an investigation report to hand over to L2/L3

Build AI Agent Skills that Actually Work - 3 Hours Hands-on Workshop

Product Leader and Super IC at Google

Google

Disney+ Hotstar

Zynga

Anshumani Ruddra

Product Super IC at Google with 22+ years crafting experiences and blockbuster products

Cohort 06

Cohort 07

Most product builders use AI agents the same way they used search - typing queries and hoping for the best. That's leaving most of the value on the table.The teams pulling ahead aren't using better prompts. They're teaching their agents. Agent Skills are reusable, testable instruction sets that tell AI agents exactly how to handle specific workflows: your deployment process, your code review standards, your team's edge cases. Write one skill well, and every agent on your team benefits from it, every time, automatically.The problem: nobody is teaching product builders how to create them. Most documentation is written for a narrow technical audience, and the tooling is fragmented across Claude Code, Codex, and Gemini.In this hands-on workshop, you'll go from zero to a working skill installed in your own agent: one that reflects your real workflows, not generic templates. You'll also learn how to evaluate and improve skills systematically, so they get better over time instead of drifting.If you build or are hoping to build products with AI agents: this is the highest-leverage skill you can develop right now.

🤖 Learn to build procedural Agent Skills, transforming your professional mindset from solo prompter to manager of elite AI agents

Shift to an AI Management Mindset

Build Custom, Reusable Agent Skills

Leverage the Open-Source Ecosystem

Evaluate and Iterate for 10x Output

Product Managers: who use Claude Code or Codex daily but rely on ad-hoc prompts - and want repeatable, team-shareable AI workflows.

Entrepreneurs &amp; Founders: building AI-native products who need their agents to encode business logic, not just generate code on demand.

Tech Executives &amp; Product Leaders: who want their teams to stop reinventing prompts and start shipping a shared skills library.

Master AI & LLM security through CTF-style cohort, tackling real-world attacks, defenses, adversarial threats, and Responsible AI principles

Hands-On AI Attack & Defense Cohort.

Abhinav Singh

International Trainer & Speaker, security researcher & consultant.

Cohort 5

Cohort 6

Can prompt injections lead to complete infrastructure takeovers? Could AI applications be exploited to compromise backend services? Can data poisoning in AI copilots impact a company's stock? Can jailbreaks create false crisis alerts in security systems? This immersive, CTF-styled cohort in GenAI and LLM security dives into these pressing questions.Engage in realistic attack and defense scenarios focused on real-world threats, from prompt injection and remote code execution to backend compromise. Tackle hands-on challenges with actual AI applications to understand vulnerabilities and develop robust defenses. You’ll learn how to create a comprehensive security pipeline, mastering AI red and blue team strategies, building resilient defenses for LLMs, and handling incident response for AI-based threats. Additionally, implement a Responsible AI (RAI) program to enforce ethical AI standards across enterprise services, fortifying your organization’s AI security foundation.

Live, hands-on, CTF-style

Understand Injection Attacks and Jailbreaks on AI systems

Perform structured AI red teaming using industry frameworks

Execute and defend against adversarial & multi-agent attacks

Build and deploy LLM security tooling and enterprise-grade defenses

Operationalize AI SecOps, threat modeling, and incident response

CISOs &amp; Security practitioners looking to implement AI security best practices.

Security professionals seeking to update their skills for the AI era.

Red &amp; Blue team members.

Learn to design, monitor, and deploy working AI systems. This transforms them to build robust, real-world LLM apps using prove

Bootcamp cuts through AI noise — from foundations to evaluating, securing, and deploying AI systems

June AI Engineer 

CEO Hyperskill | Lecturer JetBrains, MIT

Nikolay Vyahhi

I'm passionate about using AI to make learning smarter and more accessible.

CTO Hyperskill | Founder TheOna 

Ruslan Davletshin

I’ve built and deployed LLM systems at scale in production environments. 

AI, ML Engineer & Teaching Expert

Nikalina Ogorodova

I enjoy bridging the gap between complex technology and clear understanding.

AI Engineer & Researher

Ivan Rodin

I have over 7 years of experience in Data Science and Machine Learning.

May AI Engineer

September AI Engineer

Are you an experienced engineer looking to build systems on top of LLMs, not just experiment with AI tools out of the box?In 2026, working with AI increasingly means designing and shipping products powered by LLMs: assistants, RAG pipelines, agentic workflows, evaluation systems. Many engineers use these tools but far fewer know how to structure them into reliable, production-grade systems.This program focuses on that 2026 skillset.The Hyperskill AI Engineering Bootcamp is a 10-week, hands-on program for engineers who want to move to architecting reliable AI systems. You’ll build end-to-end projects using LLMs as components: designing retrieval pipelines, working with vector databases, implementing agents, setting up evals and handling deployment, monitoring and security.The emphasis is on practical system design, trade-offs and failure modes.Modules include: • LLM pipelines &amp; system design • Agents and multi-agent patterns • Vector search &amp; RAG systems • Evaluation, monitoring, security • Deployment of LLM-based applicationsExpect ~10 hours/week of focused work.

10-week Agentic  AI Engineering from experienced educators & practitioners

Design and build functional LLM pipelines end-to-end

Build AI agents with real autonomy using tools, memory, and function calling

Design high-quality context & RAG systems

Build advanced multi-agent & multimodal AI systems

Deploy secure and privacy-first LLM applications

Evaluate, test & monitor AI systems

Comfortable with programming know some Python. New to Python? We’ll give prep materials and 1–2 weeks of free Hyperskill access.

Basic programming experience (Python preferred)

On average learning takes 10–12 hours weekly completing hands-on tasks, projects, and practice at their own pace

Weekly time commitment (10-12 hours)

Live sessions are held in small groups with flexible timing and typically run twice

Availability for live sessions

Software engineers who want to level up from basic LLM usage to building production-ready AI systems, agents, and RAG pipelines

Technical PMs and team leads who want to raise their company’s AI capabilities and confidently drive adoption of modern AI systems

Developers looking to switch jobs by gaining the modern, in-demand AI engineering skills companies hire for today

Everyone is learning to build AI agents. Few can prove one is safe to deploy.

Founder | Agentic AI Governance Advisor

Cyber Pros Training

Centene

ISACA

Trenton Health Team

François B. Arthanas

Founder of CyberProsAI | Agentic AI Governance Advisor | CISSP, CISA, AAIA™

Learn how to govern agentic AI systems before they create risk, fail audits, or get your org in trouble.

Cohort 1

AI agents are not chatbots. A chatbot answers a question. An AI agent retrieves customer records, calls tools, triggers workflows, issues refunds, sends emails, and acts sometimes before a human ever reviews the output.That means the question landing on YOUR desk is different:Can this agent leak PII? → Can it be prompt-injected through a poisoned document? → Can it call the wrong tool or the right tool with the wrong authority? → Can it issue an unauthorized refund? → Who owns the risk? And what evidence proves the controls actually work?Most professionals freeze at this question because they've only ever seen AI governance as policy slides and framework acronyms. They've never actually run an agent risk review end to end.In this one-day sprint, you will. You'll take a realistic enterprise AI agent through the complete practitioner loop scope → risk → control → test → evidence → decision and finish the day having written and defended a real deployment recommendation.You will watch an AI agent get broken live. Then you'll learn exactly how to make sure it never happens on your watch.

In one day, you'll run a real AI agent risk review start to finish and leave able to do it again at work, with the templates to prove it.

Scope a real enterprise AI agen a support agent that can access customer data

Break it: Run a live red-team session against the agent (no coding required)

Turn your findings into a one-page Trust Snapshot leadership can act on

Make the call and defend it in a live mock review board

You do not need to be an engineer. You do not need to know how to code. You do not need advanced AI expertise.

There are no strict prerequisites for this course

If you are curious about AI agents and want a practical way to think about oversight, this workshop will meet you where you are and move you

What you do need is an interest in understanding how AI is changing governance

This workshop is for professionals who know AI agents are becoming important and do not want to get left behind.

It is built for GRC, IT, cyber, privacy, legal &amp; consultants who need to understand how agentic AI changes the governance conversation

You should join this workshop if you want to understand how AI agents create risk &amp; how to contribute meaningful value inside your org

Agentic AI

Coding with AI

AI Workflows

Claude Code

OpenClaw

Vibe Coding

AI Evals

AI Transformation

RAG & Search

AI for PMs

AI for Engineers

AI for Designers

AI for Marketers

AI for Founders

Product Sense

Product Discovery

User Research

Prototyping

Growth

Analytics

Tech Foundations

Strategy

Influence

Leadership

Career Growth

Machine Learning

LLM Ops

Context Eng

Security

System Design

Figma

Design Systems

Visual Design

Design Strategy

Automation

Content Marketing

Demand Gen

Go-to-Market

Product Marketing

Positioning

Social Media

Brand

B2B Marketing

SEO & AEO

AI for Leaders

AI Governance

Communication

Management

People Operations

Exec Presence

Storytelling

Goal-setting

Personal Brand

Fundraising

Everyone

Operators

Data Scientists

Business Analysts

User Researchers

Customer Success

Project Managers

HR Professionals

Sales People

Lawyers

Finance

Investors

Real Estate

Educators

Creators

Engineers and engineering leaders who build, deploy, and scale software systems.

Applying AI tools and techniques to professional workflows — assign alongside more specific AI topics.

Building autonomous AI agent systems that take actions, use tools, and orchestrate multi-step workflows.

Securing software systems — application security, threat modeling, authentication, and infrastructure protection.

Automating professional workflows with AI tools — productivity, operations, and task automation.

Black Hat Arsenal Lab 02 - Cybersecurity Skills for AI

What you'll learn

Why this topic matters

You'll learn from

Rajesh Jain