Human Aspects & Analytics

Overview

Most security failures are not technical failures. Someone trusted an output they should have questioned, or worked around a control that made their job impossible, or approved a tool nobody had governed. Those are human and organizational problems, and they are now the main way enterprise risk actually arrives.

This certificate takes them seriously across two graduate courses.

The first examines how human behaviour, organizational culture and leadership decisions shape the secure and ethical use of artificial intelligence inside enterprise risk management. It works through trust, cognitive bias, over-reliance on automated output, communication failure, missing guardrails and policy gaps as genuine contributors to AI-related risk, then has students build AI governance strategies using NIST SP 800-30 and the Risk Management Framework to design guardrails at enterprise scale.

The second is an in-depth treatment of securing modern digital and physical environments: system accreditation and certification, lifecycle management of software, hardware and infrastructure from planning through to secure disposal, and the current edge of the field, AI-driven threat detection, zero trust architecture, quantum-safe cryptography, secure-by-design principles, supply chain security, through hands-on projects and real case studies.

Norwich teaches this as an institution designated by the National Security Agency and the Department of Homeland Security as a Center of Academic Excellence in Cyber Defense.

What you will learn

By the end of the certificate you will be able to:

• Explain how human behaviour, organizational culture and leadership decisions determine whether security controls hold
• Identify the human contributors to AI-related risk — misplaced trust, cognitive bias, over-reliance on model output, communication failure, missing guardrails, policy gaps
• Build an AI governance strategy using NIST SP 800-30 and the Risk Management Framework, and design guardrails at enterprise scale
• Apply current AI governance regulation to decisions about whether and how a system should be deployed
• Run system accreditation and certification, and manage the lifecycle of software, hardware and infrastructure from planning through to secure disposal
• Assess zero trust architecture, quantum-safe cryptography and secure-by-design as choices with trade-offs rather than as slogans
• Evaluate supply chain security exposure and AI-driven threat detection
• Work through hands-on projects and real case studies rather than scenarios