Summer Associate Internship (Security Workflow Engineer)

The team is advancing from traditional process automation toward AI-enabled workflow engineering.

Our approach begins with the business workflow and its friction points, then identifies where AI can responsibly extract, classify, compare, summarize, or recommend information while preserving appropriate human validation and accountability.

Current and emerging areas include AI-assisted investigations, intelligent intake and triage, document and evidence analysis, decision support, workflow analytics, and continuous improvement based on reviewer feedback.

This aligns with the broader Advanced Cyber Initiatives mission of advancing intelligence-led, automated, and AI-driven security capabilities.

The Summer Associate will work alongside security automation engineers, business analysts, process owners, and AI and data partners to help move a selected AI use case from discovery through prototype and evaluation.

The associate will help define the problem, prepare and analyze relevant data, develop or configure an AI-assisted capability, integrate it into a governed workflow, and measure its potential business and security value.

The role will provide hands-on exposure to applied generative AI, security workflow design, low-code development, responsible AI practices, and enterprise solution delivery.

The Summer Associate Program is a 12-week internship program beginning in May 2027 and ending in August 2027.

Students will work on impactful projects and meaningful work during their internship.

To qualify for this position, applicants must be currently pursuing a degree from an accredited college or university and have an anticipated graduation date of December 2027 or later.

Potential assignments will be based on business priorities, data availability, and required governance approvals.

Projects may include one or more of the following:

• Intelligent Threat or Security Intake: Design an AI-assisted intake experience that summarizes submissions, identifies missing information, recommends categorization or routing, and creates structured case data while maintaining human oversight.
• Historical Intelligence and Case Comparison: Explore retrieval and comparison techniques that help investigators identify relevant prior cases, recurring entities, patterns, or related evidence without replacing the investigator’s decision.
• AI Quality and Feedback Framework: Develop a reusable approach for capturing reviewer corrections, measuring output quality, identifying common error patterns, and using feedback to improve prompts, workflow controls, and review guidance.
• Responsible AI Workflow Controls: Prototype reusable components for source traceability, confidence or uncertainty presentation, human validation, exception handling, audit logging, and escalation of higher-risk AI recommendations.
• AI Use-Case Evaluation and Prioritization: Help assess candidate workflows based on operational friction, data readiness, risk, feasibility, expected value, and the level of human accountability required.
• Workflow and Process Intelligence: Analyze workflow data to identify delays, repeated manual effort, rework, bottlenecks, or opportunities for AI-assisted automation and recommend measurable improvements.