Cybersecurity
Security practice now runs in both directions at once: AI systems and the autonomous agents wired into enterprise tooling are themselves a new attack surface (prompt injection, data poisoning, over-privileged agent credentials, model and connector supply chains), while defenders increasingly work from machine-generated triage, detection rules, and incident timelines they must verify rather than trust. The course therefore keeps its enterprise-assurance identity and adds securing AI-enabled systems and verifying AI-assisted defensive work as first-class topics.
Current catalog prerequisites — (CSC 250 or 2050).
Current description → proposed description
This course is a survey and overview of methods to safeguard the computer and information technology employed today. Computer and information systems are increasingly under attack and therefore knowledge of attacks, protection, and counter-measures is important. Students will understand and manage assurance and security measures within the enterprise. Topics include operational issues, policies and procedures, attacks and related defense measures, risk analysis, backup and recovery, and the security of information.
Prerequisites: (CSC 250 or 2050).
This course surveys the methods used to safeguard computing and information systems in an enterprise where both attackers and defenders increasingly operate at machine speed. Students examine security policy and procedure, physical and administrative controls, identity and access management, risk analysis, regulatory compliance, security awareness across an organization, and provisions for backup, recovery, and incident response, studying common attack patterns alongside the defenses and counter-measures that answer them. The course extends that foundation to the security of AI-enabled systems, where students analyze prompt injection, data poisoning, and supply-chain risk in models and agent connectors. Students also examine the credentials granted to autonomous agents and the provenance records needed to reconstruct what an automated system did and why. Machine-generated alert triage and detection rules receive the same scrutiny, since an analyst remains answerable for work the analyst did not write. Throughout, the course treats the security professional as a steward of information entrusted by others, a responsibility examined from a Lutheran Christian understanding of vocation.
What changes
- Least-privilege credentialing for autonomous agents and the tools they invoke
- Prompt injection, data poisoning, and model/connector supply-chain analysis
- Verification of machine-generated triage summaries and detection rules
- Provenance and audit logging sufficient to reconstruct automated actions
- Stewardship and accountability for agent-taken actions, framed by Lutheran Christian vocation
6 proposed outcomes, mapped to 10 program outcomes
Each outcome below is written to be observable and assessable, and each is mapped to the program learning outcomes for which it produces evidence.
Students will be able to analyze an organization's information assets, threat landscape, and existing controls to produce a prioritized risk assessment that accounts for conventional attack vectors and for AI-enabled ones such as automated reconnaissance and synthetic-media social engineering.
PLO 4.1: the CLO requires critical analysis of a data-driven threat picture and weighs the societal harm of AI-enabled social engineering against conventional attack vectors. PLO 6.2: producing a prioritized assessment forces students to combine quantitative likelihood and impact estimates with qualitative judgment about attacker capability.
Students will be able to design a layered set of administrative and technical safeguards for a defined enterprise scenario, including access management, least-privilege credentialing for autonomous agents and the tools they invoke, logging, and backup and recovery provisions, under the constraints of a named regulatory regime such as HIPAA, FERPA, or PCI DSS.
PLO 3.2: designing safeguards under a named regulatory regime such as HIPAA, FERPA, or PCI DSS requires students to apply legal and policy knowledge alongside technical controls to a human-centered enterprise system. PLO 2.3: bounding what an autonomous agent may do, by credentialing the agent and the tools it invokes at least privilege, is responsible AI usage and ethical management of the AI resources an organization deploys.
Students will be able to evaluate the security posture of an AI-enabled or agent-integrated system against prompt injection, data poisoning, excessive agency, and model and connector supply-chain compromise to determine proportionate mitigations.
PLO 4.1: evaluating a system against data poisoning and prompt injection is direct critical analysis of data-driven outcomes and the societal consequences of their failure. PLO 2.3: the CLO's treatment of excessive agency and the scope of agent credentials is precisely responsible AI usage and ethical management of the AI resources an organization deploys.
Students will be able to critique machine-generated security artifacts such as detection rules, alert triage summaries, and reconstructed incident timelines by verifying each claim against primary log and telemetry evidence before acting on it.
PLO 6.1: checking detection rules, triage summaries, and reconstructed timelines against the primary telemetry is the skilled interpretation and explanation of AI-produced data outcomes that 6.1 names. PLO 4.1: judging whether a machine-generated security claim is actually supported by the underlying logs, and what harm follows if it is acted on unchecked, is critical analysis of a data-driven outcome and its consequences.
Students will be able to present an incident response and recovery plan, translating its findings and residual risk for both technical staff and non-specialist organizational leadership.
PLO 5.1: presenting findings and residual risk demands transparent, accurate communication of technical security content in written and spoken form. PLO 5.3: the CLO explicitly requires translating the plan for non-specialist leadership so they can make informed decisions.
Students will be able to articulate, from a Lutheran Christian understanding of vocation and stewardship, the ethical responsibilities of the security professional as a steward of data entrusted by others, defending decisions about disclosure, privacy, and accountability for actions an autonomous agent took on the organization's behalf.
PLO 1.2: the CLO has students reason about disclosure, privacy, and accountability explicitly from a Lutheran Christian understanding of vocation, which is the Christian grounding of ethical decisions that 1.2 requires. PLO 1.3: naming the security professional a steward of data entrusted by others, from that same Christian understanding of vocation, is the evaluation of one's work as a calling that serves others. PLO 6.3: defending who remains accountable when an autonomous agent acts on the organization's behalf is a moral assessment of an AI development's impact.
Program outcomes this course reaches
Filled cells are program learning outcomes with at least one supporting course learning outcome in this course. Sparse coverage is expected — no single course carries all eighteen.