Certified Offensive AI Security Professional (COASP) validates the competencies required for practitioners who need to demonstrate offensive AI security skills, emulating adversaries, validating defenses, and leading red-team/blue-team exercises to keep AI resilient, reliable, and auditable

The Certified Offensive AI Security Professional (COASP) equips you to identify and neutralize AI-specific threats before attackers do. And Bridges security, engineering, and data science so controls exist across the full AI life cycle.

Participants will gain hands-on experience to perform end-to-end adversarial testing and deliver defensive validation evidence including the ability to simulate adversarial AI kill chains, Harden AI architectures by secure system prompts, context windows, tool integrations, RAG pipelines, and agent memory, Conducting AI security assessments aligned to MITRE ATLAS, OWASP LLM/ML Top 10, NIST AI RMF, and DoD Test & Evaluation practices , This course covers how to build SOC-ready capabilities for AI-focused detection logic, incident playbooks, and forensic procedures , & how to execute prompt injection, adversarial prompting , Assess AI supply-chain risk , Implement defensive engineering controls and Produce assurance and compliance artifacts.

By the end of the course, learners will be well-prepared to take the Certified Offensive AI Security Professional (COASP) exam and demonstrate the ability to exploit vulnerabilities in LLMs and agents, and build defense that survive real world attacks, learners will master offensive techniques that break AI before the attackers do.

 

Course Outline: 

01. Offensive AI and AI System Hacking Methodology

02. AI Reconnaissance and Attack Surface Mapping

03. AI Vulnerability Scanning and Fuzzing

04. Prompt Injection and LLM Application Attacks

05. Adversarial Machine Learning and Model Privacy Attacks

06. Data and Training Pipeline Attacks

07. Agentic AI and Model-to-Model Attacks

08. AI Infrastructure and Supply Chain Attacks

09. AI Security Testing, Evaluation, and Hardening

10. AI Incident Response and Forensics 

 

Prerequisites: 

TN-412: Artificial Intelligence Essentials (AI|E) 

 

Dates/Locations:

No Events

Course Overview:

Getting started with the Elastic Stack (ELK), optimizing search performance and building efficient clusters. Ingest and process data, writing complex search requests and response utilization, scaling of clusters up or down, managing indices in large clusters and multiple clusters, management of clusters and troubleshooting recommendations.

Attendees to TN-430: Elasticsearch Engineer (ELK) will receive TechNow approved course materials, expert instruction, and prepare you to take ELK exam.

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Dates/Locations:

No Events

Duration: 5 Days

Course Outline:

Data Management

  • Define an index that satisfies a given set of requirements
  • Define and use an index template for a given pattern that satisfies a given set of requirements
  • Define and use a dynamic template that satisfies a given set of requirements
  • Define an Index Lifecycle Management policy for a time-series index
  • Define an index template that creates a new data stream

Searching Data

  • Write and execute a search query for terms and/or phrases in one or more fields of an index
  • Write and execute a search query that is a Boolean combination of multiple queries and filters
  • Write an asynchronous search
  • Write and execute metric and bucket aggregations
  • Write and execute aggregations that contain sub-aggregations
  • Write and execute a query that searches across multiple clusters
  • Write and execute a search that utilizes a runtime field

Developing Search Applications

  • Highlight the search terms in the response of a query
  • Sort the results of a query by a given set of requirements
  • Implement pagination of the results of a search query
  • Define and use index aliases
  • Define and use a search template

Data Processing

  • Define a mapping that satisfies a given set of requirements
  • Define and use a custom analyzer that satisfies a given set of requirements
  • Define and use multi-fields with different data types and/or analyzers
  • Use the Reindex API and Update By Query API to reindex and/or update documents
  • Define and use an ingest pipeline that satisfies a given set of requirements, including the use of Painless to modify documents
  •   Define runtime fields to retrieve custom values using Painless scripting

Cluster Management

  • Diagnose shard issues and repair a cluster’s health
  • Backup and restore a cluster and/or specific indices
  • Configure a snapshot to be searchable
  • Configure a cluster for cross-cluster search
  • Implement cross-cluster replication

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TechNow,Inc is an Accredited Training Center. As an ATC we are authorized to conduct EC-Council curriculum courses. Students will receive official EC-Council Curriculum and access to iLabs and when applicable access to EC-Council Cyber Range.


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