This concentration was developed in conjunction with the U.S. National Security Agency (NSA) providing an invaluable tool for any systems security engineering professional. CISSP®-ISSEP is the guide for incorporating security into projects, applications, business processes, and all information systems. Security professionals are hungry for workable methodologies and best practices that can be used to integrate security into all facets of business operations. The SSE model taught in the IATF portion of the course is a guiding light in the field of information security and the incorporation of security into all information systems.
Attendees to TN-812: Information Systems Secuirty Engineering Professional (ISSEP) will receive TechNow approved course materials and expert instruction.
The introduction to SQL Databases training course is designed to train the learners on the fundamentals of database concepts. You will not only learn about the different types of databases, the languages and designs as well as describe important database concepts using SQL Server 2016. Anyone who is moving into a database role will benefit from taking this course.
Attendees to MS-5002: Introduction to SQL Databases will receive TechNow approved course materials and expert instruction.
This Python for Penetration Testing course is designed to give you the skills you need for maintaining or developing Python Penetration Testing tools oriented towards offensive operations. We have a suite of courses and certifications that help understand a problem, this course prepares the student to rapidly develop prototype code to attack or defend against it.
The course concludes with a Capture the Flag event that will test both your ability to apply your new tools and coding skills in a Python Penetration Testing challenge.
This course is not intended to be an Advanced Python course, but to exemplify penetration techniques utilizing Python. The course covers Threading, Sockets, OOP, and third party modules that facilitate the offensive operator’s objective.
This course utilizes the “Violent Python” text book.
Attendees to TN-345: Python for Penetration Testers Class will receive TechNow approved course materials and expert instruction.
CompTIA SecAI+ is the first certification in CompTIA’s expansion series, designed to help you secure, govern and responsibly integrate artificial intelligence into your cybersecurity operations. You’ll build the skills to defend AI systems, meet global compliance expectations and use AI to enhance threat detection, automation and innovation—so you can strengthen your expertise and help keep your organization’s systems and data secure.
SecAI+ helps you build practical AI security and automation skills on top of your existing expertise, so you can secure AI deployments, use AI‑assisted security tools with confidence, and stay ready for the next step in your cybersecurity career.
Course Objectives:
Apply AI concepts to strengthen your organization’s cybersecurity posture
Secure AI systems using advanced controls and protections to safeguard data, models, and infrastructure
Leverage AI technologies to automate workflows, accelerate incident response, and scale security operations
Navigate global GRC frameworks to ensure ethical and compliant AI adoption across industries
Defend against AI-driven threats like adversarial attacks, automated malware, and malicious use of generative AI
Integrate AI securely into DevSecOps pipelines and enterprise security strategies.
Dates/Locations:
No Events
Prerequisites: Recommended experience: 3–4 years in IT and 2+ years hands-on cybersecurity; Security+, CySA+, PenTest+, or equivalent recommended
SecAI+ (V1) exam objectives summary
Basic AI concepts related to cybersecurity (17%)
Explain core AI principles and terminology: Machine learning, deep learning, natural language processing, and automation.
Identify AI applications in security: Use cases for AI in threat detection, defense, and security operations.
Recognize AI-driven threats: Automated phishing, polymorphic malware, adversarial machine learning, and malicious use of generative AI.
Securing AI systems (40%)
Implement security controls: Protect AI systems, data, and models using robust technical safeguards.
Secure AI deployment environments: Apply best practices across on-premises, cloud, and hybrid infrastructures.
Mitigate adversarial risks: Defend against attacks targeting AI models, data pipelines, and inference layers.
AI-assisted security (24%)
Enhance detection and response: Use AI-driven tools to identify anomalies, detect threats, and accelerate incident remediation.
Automate security workflows: Integrate AI for event triage, alert correlation, and response orchestration.
Apply AI techniques in operations: Incorporate AI into threat modeling, behavior analysis, and continuous monitoring.
AI governance, risk, and compliance (19%)
Understand regulatory frameworks: Identify global governance requirements and their implications for AI adoption.
Integrate GRC into AI projects: Incorporate governance, risk management, and compliance practices throughout the AI lifecycle.
Ensure responsible AI use: Apply ethical guidelines, legal standards, and industry frameworks such as GDPR and NIST AI RMF.
