CONTACT FOR DEMO CLASS : (+91) 8951869553/52
CONTACT FOR DEMO CLASS : (+91) 8951869553/52
Course Start Date: 18th Decemeber 2025
Participants will master the principles of responsible AI, learn to audit security models for algorithmic bias, and implement governance frameworks like NIST AI RMF to ensure trustworthy and compliant cyber defense operations.
Understand core ethical principles, fairness metrics, and governance models essential for trustworthy security systems.
Hands-on exercises on auditing AI models, detecting algorithmic bias, and aligning defenses with the EU AI Act.
Learn to implement human-in-the-loop controls and explainable AI (XAI) to ensure accountability in threat detection.
Micro-sessions fit perfectly into your day
In this intensive micro session, cybersecurity professionals will learn how to deploy AI responsibly, ensuring that defense mechanisms are fair, transparent, and accountable. The session covers AI governance, bias detection, and ethical frameworks necessary for secure operations.
Through hands-on labs and live demonstrations, participants will explore how to audit AI models for security risks, implement Explainable AI (XAI) in threat detection, and align defenses with global regulations like the EU AI Act and NIST AI RMF. By the end of the training, you will be equipped to build trustworthy AI-driven defense systems.
EarlyRise's Ethical AI and Cyber Defense Micro Session Key Features
Deploy AI defenses responsibly and ethically
Audit security models for algorithmic bias
Implement NIST AI RMF governance frameworks
Ensure transparency with Explainable AI (XAI)
Align cyber operations with global regulations
Build trust in AI-driven security decisions
Customized to your team's needs
Key Learning Objective: Understanding the core pillars of responsible AI: Fairness, Accountability, and Transparency in security operations.
Key Learning Objective: Identifying ethical risks and potential harms in automated threat response systems.
Key Learning Objective: Identifying and measuring algorithmic bias in threat detection datasets to prevent discriminatory outcomes.
Key Learning Objective: Techniques to rebalance datasets and adjust model weights to ensure fair security decisions.
Key Learning Objective: Using Explainable AI (XAI) tools to interpret "black box" AI decisions during incident response.
Key Learning Objective: Communicating AI reasoning to stakeholders to validate automated defensive actions.
Key Learning Objective: Designing human-in-the-loop protocols for high-stakes AI decisions to ensure accountability.
Key Learning Objective: Navigating the EU AI Act and other regulations impacting AI in cybersecurity.
4 Hours
4 Hours
Upon successful completion of the course, participants will receive a certificate from EarlyRise. This certificate is widely recognized and signifies that the holder has acquired specialized skills.
Get In Touch
Our team will be happy to assist you make the right decision
Leading industry professionals who bring current best practices and case studies to sessions that fit into your work schedule.
Our Course fees are very nominal and competitive. We provide Scholarship up to 50% time to time for eligible candidates.
This session is ideal for cybersecurity professionals, compliance officers, risk managers, and AI engineers focused on secure and ethical AI deployment.
Basic understanding of cybersecurity concepts is recommended. While some labs involve tools, no deep coding expertise is required to understand the ethical frameworks.
You'll work with AI auditing tools (like IBM AI Fairness 360), XAI libraries (SHAP/LIME), and governance templates (NIST AI RMF).
Yes. The session is 100% practical with live demos, hands-on labs for bias detection, and simulation of governance audits.
You'll be able to audit AI models for bias, implement explainability in security ops, and design a responsible AI governance strategy for your organization.
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