The Department of Defense requires that all information assurance personnel must become compliant with IT and security certification standards.
DoD 8570 training, also called Information Assurance training, is available through TechNow to provide you with the certification that is required. Your DoD 8570 training ( information assurance training ) at TechNow will provide you with all of the courses necessary to receive your DoD 8570.01-M certification.
Ongoing open enrollment through TechNow is available for our DoD 8570.01-M courses.
Please review the full & updated DoD approved IA baseline certifications aligned to each category & level of the IA workforce.
For further information or to schedule for classes, call us at 800-324-2294
The Implementing Cisco Enterprise Advanced Routing and Services(ENARSI) gives you the knowledge you need to install, configure, operate, and troubleshoot an enterprise network. This course covers advanced routing and infrastructure technologies, expanding on the topics covered in the N-415: Implementing and Operating Cisco Enterprise Network Core Technologies (ENCOR).
Attendees to N-425 : Implementing Cisco Enterprise Advanced Routing and Services ( ENARSI) will receive TechNow approved course materials, expert instruction, and prepare you to take the 300-410 Implementing Cisco® Enterprise Advanced Routing and Services (ENARSI) exam.
Dates/Locations:
No Events
Duration: 5 Days
Course Objectives:
Configure classic Enhanced Interior Gateway Routing Protocol (EIGRP) and named EIGRP for IPv4 and IPv6
Optimize classic EIGRP and named EIGRP for IPv4 and IPv6
Troubleshoot classic EIGRP and named EIGRP for IPv4 and IPv6
Configure Open Shortest Path First (OSPF)v2 and OSPFv3 in IPv4 and IPv6 environments
Optimize OSPFv2 and OSPFv3 behavior
Troubleshoot OSPFv2 for IPv4 and OSPFv3 for IPv4 and IPv6
Implement route redistribution using filtering mechanisms
Troubleshoot redistribution
Implement path control using Policy-Based Routing (PBR) and IP Service Level Agreement (SLA)
Configure Multiprotocol-Border Gateway Protocol (MP-BGP) in IPv4 and IPv6 environments
Optimize MP-BGP in IPv4 and IPv6 environments
Troubleshoot MP-BGP for IPv4 and IPv6
Describe the features of Multiprotocol Label Switching (MPLS)
Describe the major architectural components of an MPLS VPN
Identify the routing and packet forwarding functionalities for MPLS VPNs
Explain how packets are forwarded in an MPLS VPN environment
Implement Cisco Internetwork Operating System (IOS®) Dynamic Multipoint VPNs (DMVPNs)
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.
TN-575: Open Source Network Security Monitoring teaches students how to deploy, build, and run an NSM operation using open source software and vendor-neutral tools. No network is bullet proof and when attackers access your network, this course will show you how to build a security net to detect, contain, and control the attacker. Sensitive data can be monitored and deep packet and deep attachment analysis can be achieved. As organizations stand up a Security Operations Center (SOC) the enterprise NSM is the key ingredient to that SOC. This course not only teaches how to implement an NSM technologically, but how to effectively monitor an enterprise operationally. You will learn how to architect an NSM solution: where to deploy your NSM platforms and how to size them, stand-alone or distributed, and integration into packet analysis, interpret evidence, and integrate threat intelligence from external sources to identify sophisticated attackers. A properly implemented NSM is integral to incident response and provides the responders timely information to react to the incident. TN-575: Open Source Network Security Monitoring is a lab intensive environment with a cyber range that gives each student in-depth knowledge and practical experience monitoring live systems to include: Cisco, Windows, Linux, IoT, and Firewalls.
Attendees to TN-575: Open Source Network Security Monitoring class will receive TechNow approved course materials and expert instruction.
This Course is taught utilizing Security Onion or RockNSM as specified by the customer.
Dates/Locations:
No Events
Duration: 5 Days
Course Objective:
The focus of this course is to present a suite of Open Source security products integrated into a highly functional and scalable Network Security Monitoring solution.
Prerequisites:
Students should have a basic understanding of networks, TCP/IP and standard protocols such as DNS, HTTP, etc. Some Linux knowledge/experience is recommended, but not required
Course Outline:
Network Security Monitoring (NSM) Methodology
High Bandwidth Packet Capture Challenges
Installation of Security Onion
Use Cases (analysis, lab, stand-alone, distributed)
Resource Requirements
Configuration
Setup Phase I – Network Configuration
Setup Phase 2 – Service Configuration
Evaluation Mode vs. Configuration Mode
Verifying Services
Security Onion Architecture
Configuration Files and Folders
Network Interfaces
Docker Environment
Security Onion Containers
Overview of Security Onion Analyst Tools
Kibana
CapME
CyberChef
Squert
Sguil
NetworkMiner
Quick Review of Wireshark and Packet Analysis
Display and Capture Filters
Analyze and Statistics Menu Options
Analysis for Signatures
Analyzing Alerts
Replaying Traffic
3 Primary Interfaces:
Squert
Sguil
Kibana
Pivoting Between Interfaces
Pivoting to Full Packet Capture
Snort and Surricata
Rule Syntax and Construction
Implementing Custom Rules
Implementing Whitelists and Blacklists
Hunting
Using Kibana to Slice and Dice Logs
Hunting Workflow with Kibana
Bro
Introduction and Overview
Architecture, Commands
Understanding and Examining Bro Logs
Using AWK, sort, uniq, and bro-cut
Working with traces/PCAPs
Bro Scripts Overview
Loading and Using Scripts
Bro Frameworks Overview
Bro File Analysis Framework FAF
Using Bro scripts to carve out more than files
RockNSM ( * If Applicable)
Kafka
Installation and Configuration
Kafka Messaging
Brokers
Integration with Bro and FSF
File Scanning Framework FSF
Custom YARA Signatures
JSON Trees
Sub-Object Recursion
Bro and Suricata Integration
Elastic Stack
Adding new data sources in Logstash
Enriching data with Logstash
Automating with Elastalert
Building new Kibana dashboards
Production Deployment
Advanced Setup
Master vs Sensor
Node Types – Master, Forward, Heavy, Storage
Command Line Setup with sosetup.conf
Architectural Recommendations
Sensor Placement
Hardening
Administration
Maintenance
Tuning
Using PulledPork to Disable Rules
BPF’s to Filter Traffic
Spinning up Additional Snort / Suricata / Bro Workers to Handle Higher Traffic Loads
