Hybrid Cyber Defense Detection Analyst | AI-Driven Triage

Lincoln Motor Company

Dearborn (MI)

Hybrid

USD 85.000 - 193.000

Vollzeit

Vor 4 Tagen
Sei unter den ersten Bewerbenden

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Benefits dieser Stelle

Medical benefits
Dental & vision coverage
Paid time off
Vehicle discount program for employees

Zusammenfassung

Ford Motor Company is seeking a Cyber Defense Center (CDC) Detection Analyst in Southeast Michigan for initial threat triage and timely incident response. The role is hybrid with on-site work and requires weekend rotations, plus shift coverage.

You will perform triage, analyze incidents with SIEM/EDR/IDS, and document findings for both technical and non-technical audiences. The position emphasizes collaboration with Ford teams and customers, adherence to playbooks, and continuous learning in a

Qualifikationen

  • Bachelor’s degree in a computer related field
  • 2+ years of experience with SIEM tools and/or reviewing system log files, data correlation, and analysis (i.e. firewall, network flow, system logs, IDS)
  • 2+ years of experience in customer service including the resolution of escalations, incident handling, and response
  • In depth knowledge of servers, clients, various computer peripherals, network and/or storage technologies with various operating systems including Windows, Linux, and Mac
  • Experience in a fast paced, high stress, support environment, able to work with a sense of urgency and attention to detail
  • Must work well with others including peers and end-users
  • Strong interest in cyber security with an eagerness and willingness to learn
  • Strong deductive reasoning, critical and analytical thinking, problem solving, and prioritization skills
  • Disciplined approach utilized when completing work and adhering to procedure
  • Strong oral and written communications skills – able and willing to communicate technical items in non-technical terms
  • Demonstrate high level of independent initiative, drive for results and commitment to integrity
  • Ability to concurrently work on multiple assignments/projects and complete on schedule with high quality

Aufgaben

  • Perform initial triage of various security incidents to determine if a threat applies to Ford including phishing, malicious software, hostile probes, information theft, and misuse of computing facilities
  • Conduct daily analysis on the aforementioned incidents using a range of tools such as SIEM, EDR, IDS/IPS, Cloud, and Sandbox analysis
  • Collaborate with internal and customer teams to investigate and contain incidents
  • Respond to cyber security queries received from Ford personnel
  • Adhere to various playbooks/procedures to provide consistent and repeatable methods to resolve security incidents
  • Effectively document investigation details for both technical and non-technical audiences
  • Recognize attacker Tools, Techniques, and Procedures (TTPs) and Indicator of Compromises (IOCs) that apply to current and future investigations
  • Support Shift Lead rotation at least once per calendar quarter
  • Keeping up-to-date with emerging cybersecurity threats to proactively prevent potential attacks and improve Ford’s cyber security posture
  • Leverage AI-driven threat detection and analysis tools to enhance triage accuracy and accelerate identification of emerging attack patterns

Kenntnisse

SIEM experience
Customer service
Windows/Linux/Mac
Communication skills
Analytical thinking

Ausbildung

Bachelor’s degree in computer related field

Tools

Google Cloud Platform (GCP)
Microsoft Azure
Python scripting
AI/ML awareness

Jobbeschreibung

Ford Motor Company is seeking a Cyber Defense Center (CDC) Detection Analyst in Southeast Michigan for initial threat triage and timely incident response. The role is hybrid with on-site work and requires weekend rotations, plus shift coverage.

You will perform triage, analyze incidents with SIEM/EDR/IDS, and document findings for both technical and non-technical audiences. The position emphasizes collaboration with Ford teams and customers, adherence to playbooks, and continuous learning in a

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