# COURSE SUMMARY & ENROLLMENT PARAMETERS

**Course:** AP Cybersecurity (AI-Assisted)
**Duration:** 1 Academic Year
**Target Level:** Grades 11–12
**Credit Allocation:** 1.0 (full-year credit)
**Prerequisite:** None

AP Cybersecurity is the College Board's new defense-oriented course (first exam May 2027), taught through the **Code Auditor** lens. The course requires zero active coding and is assessed via a fully digital exam. Students learn to analyze risk, mitigate threats, and detect attacks by reasoning about encryption, network architecture, and cyber defense — with AI used as both an offensive case study and a defensive analysis tool. The course culminates in readiness for the AP Cybersecurity exam and a portfolio of security audits.

# INSTITUTIONAL RUNWAY & PACING STRATEGY

## The Four Thematic Modules (25% each)

| Module | Thematic Focus | Runtime Share |
|---|---|---|
| Module 1 | Security Fundamentals & the Threat Landscape | 25% |
| Module 2 | Physical & Network Security | 25% |
| Module 3 | Device, Application & Data Security | 25% |
| Module 4 | Incident Response & Risk Management | 25% |

Each module occupies exactly one quarter of the runway, independent of the calendar. This guarantees full coverage of the AP Cybersecurity framework whether the school year runs 32 or 36 weeks.

## Systemic Buffer Block (The Flex Zone)

A closing **Systemic Buffer Block** absorbs schedule variance. On a 32–36 week year, modules run 8–9 weeks each and the Flex Zone holds the residual 0–4 weeks for exam drilling and capstone defense. Under compression, the Flex Zone contracts first; core defense content never yields.

# SYSTEMIC MODULE DECONSTRUCTIONS

## Module 1: Security Fundamentals & the Threat Landscape

- **Core Objectives:** Classify adversaries, social engineering, and the CIA triad; understand the dual use of AI in cybersecurity.
- **Architectural Concepts:** Confidentiality/integrity/availability, attack vectors, and threat modeling.
- **The AI-Assisted Classroom Workflow Strategy:** Students use AI to research and classify a real breach, then fact-check every claim into a threat brief — auditing the AI's sources, not just its prose.

## Module 2: Physical & Network Security

- **Core Objectives:** Identify physical vulnerabilities and secure network architecture via segmentation and firewalls.
- **Architectural Concepts:** Network segmentation, firewall placement, wireless security, and indicators of compromise (IoCs).
- **The AI-Assisted Classroom Workflow Strategy:** Students audit a network configuration with AI-assisted log analysis, flagging weak points and justifying each recommendation.

## Module 3: Device, Application & Data Security

- **Core Objectives:** Protect devices, applications, and data using authentication, anti-malware, access controls, and cryptography.
- **Architectural Concepts:** Authentication models, encryption at rest and in transit, and application attack signatures.
- **The AI-Assisted Classroom Workflow Strategy:** Students analyze device and application logs with AI tools, producing a documented detection report of common attacks.

## Module 4: Incident Response & Risk Management

- **Core Objectives:** Execute an incident-response workflow and reason about risk mitigation.
- **Architectural Concepts:** Incident lifecycle, risk assessment, and defense-in-depth strategy.
- **The AI-Assisted Classroom Workflow Strategy:** Students run a simulated incident response with AI-assisted triage, then defend their playbook in a written post-mortem.

# PERFORMANCE EVALUATION & GRADING SCHEME

- **System Integration (30%):** Coherent defense architecture across physical, network, and application layers.
- **Code Auditing Sheets / Security Audits (35%):** Structured logs documenting threat detection, IoC identification, and remediation rationale.
- **Capstone Portfolio (35%):** Incident-response playbook and threat-analysis briefs assessed on reasoning and evidence.

# PROGRESSIVE ACADEMIC INTEGRITY & AI POLICY

AI is a **defensive instrument** and an **audit subject**. Students must log every AI-assisted analysis (tool, prompt, output, verification). The course explicitly examines how AI is used to both attack and defend — so using AI is not merely permitted but pedagogically central. Un-audited, copy-pasted analysis that a student cannot defend is penalized as plagiarism. *You may let the AI flag the anomaly, but you must explain why it matters.*