CIS1533 AI-DRIVEN LOG ANALYSIS & TROUBLESHOOTING
3 Credit Hours
Student Level:
This course is open to students on the college level in either Freshman or Sophomore year.
Catalog Description:
CIS1533 - AI-Driven Log Analysis & Troubleshooting (3 hrs.)
This course introduces students to the analysis and troubleshooting of system, network, and security logs using artificial intelligence (AI) assisted techniques. Emphasizing applied operational skills, students will examine how logs are generated, collected, analyzed, and correlated to identify performance issues, security events, and system anomalies. Through hands on activities and real world scenarios, learners will explore AI supported methods for pattern recognition, anomaly detection, root cause analysis, and decision support. The course prepares students with practical skills applicable to both networking and cybersecurity operational environments.
KRSN: n/a
Course Classification:
Lecture
Prerequisites:
None
Controlling Purpose:
This course prepares students to analyze and interpret log data generated by information systems, networks, and security tools using AI assisted techniques. Focusing on operational troubleshooting and analysis, students learn how artificial intelligence supports the identification of anomalies, correlation of events, and determination of root causes across diverse computing environments. The course emphasizes practical, transferable skills applicable to both networking and cybersecurity pathways while promoting responsible and informed use of AI technologies.
Learner Outcomes:
Upon completion of this course, the student will have the ability to interpret and analyze log data generated by systems, networks, and security technologies in order to identify operational issues and abnormal behavior. The student will apply AI-assisted techniques to recognize patterns, correlate events across multiple data sources, and support troubleshooting and root-cause analysis in real-world scenarios. Through applied analysis, the student will evaluate the accuracy, limitations, and reliability of AI-driven insights while demonstrating responsible handling of operational data and ethical consideration of automated analysis within networking and cybersecurity environments.
Unit Outcomes for Criterion Based Evaluation:
The following outline defines the minimum core content, not including the final examination period. Instructors may add other material as time allows.
UNIT 1: INTRODUCTION TO LOGS AND OPERATIONAL MONITORING
Outcomes: Upon completion of this unit, students will be able to develop a foundational understanding of logs and their role in operational monitoring and troubleshooting.
- Describe the purpose and function of logs in information technology environments.
- Identify common types of logs, including system, application, network, and security logs.
- Explain how logs support monitoring, troubleshooting, and operational decision making.
- Recognize challenges associated with large volumes of log data.
UNIT 2: LOG COLLECTION, ORGANIZATION, AND INTERPRETATION
Outcomes: Upon completion of this unit, students will be able to examine how log data is collected, organized, and interpreted for operational use.
- Explain basic log collection and aggregation concepts
- Organize and interpret log entries to identify relevant operational information.
- Gain insight into the study of attention, memory, thought, decision-making, and problem-solving processes
- Distinguish between normal system behavior and indicators of potential issues.
- Apply structured approaches to reviewing and filtering log data
UNIT 3: AI ASSISTED LOG ANALYSIS AND ANOMALY DETECTION
Outcomes: Upon Completion of this unit, explore how artificial intelligence supports log analysis and anomaly detection.
- Explain AI concepts related to pattern recognition and anomaly detection
- Identify how AI tools assist in analyzing large and complex log datasets
- Analyze log data using AI supported techniques to detect unusual or abnormal behavior
- Evaluate the effectiveness of AI assisted analysis compared to manual methods
UNIT 4: CORRELATION, TROUBLESHOOTING, AND ROOT CAUSE ANALYSIS
Outcomes: Upon Completion of this unit, students will be able to apply AI assisted techniques to correlate events and support troubleshooting.
- Correlate log events across multiple systems or sources
- Apply AI supported analysis to identify potential root causes of operational issues
- Use structured troubleshooting methods supported by AI insights
- Document findings and communicate troubleshooting conclusions effectively
UNIT 5: ETHICAL USE AND LIMITATIONS OF AI IN LOG ANALYSIS
Outcomes: Upon Completion of this unit, students will be able to evaluate ethical considerations and limitations associated with AI driven log analysis.
- Identify risks such as bias, false positives, and over reliance on automated analysis
- Discuss privacy and data handling considerations related to log analysis
- Evaluate limitations of AI assisted troubleshooting approaches
- Apply responsible and ethical practices when using AI for operational analysis
Projects Required:
Varies, refer to syllabus.
Textbook:
Contact Bookstore for current textbook.
Materials/Equipment Required:
Attendance Policy:
Students should adhere to the attendance policy outlined by the instructor in the course syllabus.
Grading Policy:
The grading policy will be outlined by the instructor in the course syllabus.
Maximum class size:
Based on classroom occupancy
Course Time Frame:
The U.S. Department of Education, Higher Learning Commission, and the Kansas Board of Regents define credit hour and have specific regulations that the college must follow when developing, teaching, and assessing the educational aspects of the college. A credit hour is an amount of work represented in intended learning outcomes and verified by evidence of student achievement that is an institutionally-established equivalency that reasonably approximates not less than one hour of classroom or direct faculty instruction and a minimum of two hours of out-of-class student work for approximately fifteen weeks for one semester hour of credit or an equivalent amount of work over a different amount of time. The number of semester hours of credit allowed for each distance education or blended hybrid courses shall be assigned by the college based on the amount of time needed to achieve the same course outcomes in a purely face-to-face format.
Refer to the following policies on the Cowley Policies and Procedures webpage:
402.00 - Academic Code of Conduct
263.00 - Student Appeal of Course Grades
403.00 - Student Code of Conduct
Accessibility Services Program:
Cowley College, in recognition of state and federal laws, accommodates all students with a documented disability. If a student has a disability that will impact their ability to be successful in this course, please contact the Student Accessibility Coordinator for the needed accommodations.
DISCLAIMER: THIS INFORMATION IS SUBJECT TO CHANGE. FOR THE OFFICIAL COURSE PROCEDURE CONTACT ACADEMIC AFFAIRS.
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