Summer Associate Internship (Security Workflow Engineer)

Navy Federal Credit Union•Vienna, VA

2h•$26 - $47•Onsite

About The Position

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.

Requirements

Currently pursuing an associate, bachelor’s, master’s, or doctoral degree from an accredited college or university in Computer Science, Cybersecurity, Data Science, Artificial Intelligence, Machine Learning, Information Systems, Software Engineering, or a related field.
Foundational understanding of artificial intelligence, machine learning, or generative AI concepts.
Programming or scripting experience using Python or a comparable language.
Experience working with data through coursework, research, internships, or personal projects.
Understanding of common data structures and the ability to work with structured or unstructured information.
Demonstrated analytical and problem-solving ability, including the ability to break an ambiguous problem into smaller, testable components.
Ability to communicate technical concepts clearly through written documentation, presentations, and team discussions.
Ability to work both independently and collaboratively in an iterative project environment.
Interest in applying AI and automation to practical cybersecurity, fraud, risk, compliance, or operational problems.
Commitment to responsible technology use, including appropriate handling of sensitive information and recognition of the need for human oversight in consequential decisions.

Responsibilities

Collaborate with security automation engineers, business analysts, process owners, and other stakeholders to understand operational workflows, pain points, decision points, and desired outcomes.
Help identify use cases where generative AI, intelligent automation, or analytics can create measurable business or security value.
Develop proofs of concept using prompt engineering, or other approved AI and data tools.
Assist with integrating AI-generated outputs into Pega or other workflow solutions so results are structured, reviewable, traceable, and actionable.
Design AI-assisted experiences that allow users to inspect source evidence, validate outputs, correct inaccuracies, document rationale, and retain accountability for decisions.
Prepare, clean, transform, and analyze structured and unstructured data used for prototyping and evaluation.
Develop test scenarios and evaluation criteria for accuracy, relevance, consistency, explainability, security, usability, and operational value.
Conduct prompt testing, response analysis, error categorization, and controlled experimentation to improve the quality and reliability of AI-assisted capabilities.
Assist in implementing feedback loops that capture user corrections and identify opportunities to improve prompts, models, workflow rules, and review guidance.
Participate in design sessions, sprint planning, demonstrations, testing, project reviews, and stakeholder feedback sessions.
Support user acceptance testing and document findings, defects, risks, assumptions, and recommended improvements.
Measure potential outcomes such as reduced manual effort, faster time to context, improved consistency, reduced rework, increased throughput, improved decision support, or stronger auditability.
Create clear technical and business documentation, including solution designs, process flows, evaluation results, user guidance, and final recommendations.
Present project findings, prototype results, lessons learned, and recommended next steps to technical, business, and leadership audiences.
Follow applicable cybersecurity, data protection, privacy, secure development, and responsible AI requirements throughout the project lifecycle.