Ai Agents For Cybersecurity 2025

Automating vulnerability detection, prioritization, and remediation with AI agents.

What you'll learn

Understand AI Agent Fundamentals: Grasp the evolution from LLMs to autonomous AI agents and their pivotal role in cybersecurity.

Identify Core Components: Explain key elements such as planning, tool usage, memory, and prompt chaining that power effective AI agents.

Architectural Proficiency: Analyze popular agent architectures (ReAct, Chain-of-Thought, AutoGPT) and determine their applicability in cyber defense.

Integration with Cybersecurity Systems: Learn how to connect AI agents with SIEM, SOAR, and EDR tools to enhance security operations.

Real-Time Threat Detection: Develop the skills to use AI agents for processing log data, identifying anomalies, and detecting phishing or malicious content.

Mapping and Classification Techniques: Apply AI-based methods to classify cyber threats using frameworks like MITRE ATT&CK.

SOC Automation Strategies: Automate key security operations, including alert triaging, incident documentation, and threat hunting using AI agents.

Vulnerability Management Automation: Implement AI-driven solutions to continuously scan for vulnerabilities, prioritize risks, and recommend timely patching act

Practical Agent Development: Build a functional AI agent with hands-on coding exercises using frameworks like LangChain and integrate it with external APIs.

Security & Compliance: Ensure agents are designed with robust security measures to prevent data leakage, misuse, and maintain compliance with industry standards

Deployment & Integration: Deploy AI agents in enterprise environments by integrating them with collaboration tools like Slack or Microsoft Teams for real-time a

Capstone Project Implementation: Demonstrate comprehensive expertise by designing, building, and deploying a complete AI agent solution that handles practical c

Requirements

There are no requirements or prerequisites for taking this course.

Description

In today’s fast‑paced security landscape, manual vulnerability triage can’t keep up. This course empowers you to build fully autonomous AI agents that streamline every stage of vulnerability management—from data ingestion through remediation and alerting. You’ll gain hands‑on experience designing agents that fetch CVE records in real time, prioritize threats by severity, summarize issues with an LLM, and push actionable alerts to your team. By the end, you’ll have a cloud‑native, production‑grade pipeline that continuously secures your environment with minimal human intervention.Data Integration & Processing:- Source Aggregation:Learn how to pull vulnerability data from multiple feeds—NVD’s CVE API, Nessus scan results via pyTenable, and host‑specific insights from Shodan.- Data Normalization:Standardize severity scores, merge records, and filter out noise so your agent focuses on the highest‑impact threats.- Log Management:Build append‑only logs in JSON‑lines format to retain a complete audit trail of every fetched CVE for compliance and analytics.AI‑Powered Remediation:- Prompt Engineering:Use LangChain and OpenAI to frame your LLM as a vulnerability remediation specialist.- Summarization:Automatically transform raw CVE data into 1–2 sentence overviews.- Patch Recommendations:Generate 3–5 bullet‑step remediation guides complete with official advisory links, giving your security team clear, actionable next steps.Security, Validation & Sandboxing:- Input Validation:Enforce JSONSchema rules and strict type checks to block malicious or malformed input before it reaches your core agent logic.- Sandbox Isolation:Contain risky operations in jailed subprocesses or containers with CPU, memory, and system‑call restrictions.- Anti‑Hallucination:Implement retrieval‑augmented generation, response schema validation, and confidence thresholds so your agent’s outputs remain factual, reliable, and auditable.Deployment: Containerization & CI/CD:- Docker Multi‑Stage Builds:Craft lean, secure images that include only runtime artifacts and non‑root users for minimal attack surface.- Kubernetes Orchestration:Deploy agents as CronJobs and Deployments, configure health probes and autoscaling, and manage secrets via ConfigMaps.- CI/CD Automation:Use GitHub Actions to trigger pipelines on every commit: build images, run unit tests, push to your registry, and deploy seamlessly—ensuring rapid, error‑free releases.Real‑Time Monitoring & Alerting:- Metrics Collection:Instrument your agents with Prometheus exporters to gather performance and error metrics.- Visualization:Create Grafana dashboards that track CVE ingestion rates, processing latency, and alert volumes at a glance.- Alert Rules:Configure Alertmanager to fire notifications for newly detected critical CVEs.- Notification Channels:Integrate with Slack (or email) to deliver top‑five threat alerts directly to your team’s channels in seconds.Who Should Enroll?Cybersecurity Professionals wanting to automate vulnerability triage and response.DevOps Engineers seeking to integrate AI‑driven security into CI/CD pipelines.AI Enthusiasts aiming to apply large‑language models to real‑world DevSecOps challenges.Security Architects focused on building scalable, resilient, and autonomous security workflows.Equip yourself with the skills to transform manual vulnerability management into an AI‑powered, continuous process—keeping your organization safe, compliant, and ahead of emerging threats.

Overview

Section 1: INTRODUCTION: Welcome to AI Agents for Cybersecurity

Lecture 1 What You’ll Learn & Why It Matters?

Section 2: Fundamentals of AI Agents in Cybersecurity

Lecture 2 From LLMs to Autonomous Agents

Lecture 3 Components & Architectures of Cyber Agents

Lecture 4 Connecting to Cybersecurity Systems

Section 3: Autonomous Threat Detection

Lecture 5 Log Monitoring with Agents

Lecture 6 Detecting Phishing & Malicious Content

Lecture 7 Mapping Events to MITRE ATT&CK

Section 4: SOC Automation with AI Agents

Lecture 8 AI-Powered Alert Triage

Lecture 9 Auto-Documentation of Incidents

Lecture 10 Autonomous Threat Hunting

Section 5: AI-Driven Vulnerability Management & Agent Operations

Lecture 11 Vulnerability Management Agents

Lecture 12 Building & Securing AI Agents

Lecture 13 Deployment & Real-Time Monitoring

Section 6: Final Quiz & Practice Tests

Cybersecurity Engineers looking to integrate AI agents into their defensive strategies.,Security Architects who design scalable, AI-enhanced security systems.,AI Developers aiming to apply generative AI to real-world cybersecurity use cases.,SOC Analysts wanting to automate repetitive security operations and alert triage.,DevSecOps Professionals interested in embedding intelligent agents into CI/CD pipelines.,Penetration Testers who want to explore AI-powered red teaming and threat emulation.,IT Operations Specialists seeking proactive and automated cyber risk mitigation tools.,SIEM & SOAR Administrators looking to enhance their platforms with autonomous capabilities.,Security Researchers eager to experiment with LangChain, AutoGPT, and custom agents.,Incident Responders seeking faster, more efficient ways to manage and contain breaches.,Data Scientists working in cybersecurity domains who want to transition into agent-based automation.,Technology Consultants advising clients on AI and cybersecurity integrations.,Cloud Security Professionals exploring autonomous threat detection across hybrid environments.,Cybersecurity Instructors & Trainers who want to teach the next wave of AI-powered defense tools.,Security Operations Managers aiming to boost SOC performance and lower response times.,Tech Enthusiasts & Innovators with a solid foundation in AI or security, looking to explore cutting-edge agent applications.