PSC Flashcards
(11 cards)
- Led development of a desktop application used for training and educational resources for policy analysts using .NET and C#
I led the development of a desktop application designed to support training and educational needs for policy analysts, particularly in environments where secure, offline access to content was required.
We built it using .NET and C#, and leveraged Microsoft Reunion (now known as Windows App SDK) to modernize the desktop experience while still maintaining compatibility with existing Windows infrastructure. The goal was to make a lightweight, user-friendly tool that allowed analysts to easily browse training modules, access embedded documentation, and complete interactive exercises—even in settings with limited or no internet access.
From a development perspective, I handled both the front-end and back-end architecture. On the front end, I used WinUI 3 for a more responsive and modern interface. On the back end, I implemented a local SQLite database to manage user progress, session data, and downloadable training content. The app also included role-based access control, so different users would see different resources depending on their access level or training status.
I also collaborated with subject matter experts—policy leads and training coordinators—to ensure the content aligned with various development paths. For example, courses and resources for teams in crisis management would be different from DEI policy, each having their own appropriate resources.
Overall, it was a rewarding experience where I got to apply my technical skills in C# and the Windows ecosystem while also designing for real users with very specific needs.
- Developed an automated intelligence ingestion and triage system using Python, SQL, and MongoDB, transforming unstructured reports into actionable structured datasets.
I developed an automated ingestion and triage system designed to process unstructured intelligence reports and transform them into structured, queryable datasets. The goal was to reduce the manual effort analysts were spending reviewing and tagging reports, and to improve how quickly relevant information could be surfaced.
The pipeline was built primarily in Python, with MongoDB as our storage backend for unstructured and semi-structured data, and SQL for downstream structured analytics and reporting.
The system started by pulling in raw reports—mostly text-based documents from various sources. I implemented parsing logic to extract key metadata fields, like dates, locations, entities, and threat types.
Once parsed, the data was stored in MongoDB because of its flexibility with nested and inconsistent fields—which is common in the kind of intelligence data we were working with. Then, for reports that passed initial filters—based on categories and fields in the context of priority or urgency—the system triggered a triage step, which involved additional enrichment and formatting before being pushed to a relational database for easier querying by analysts.
The end result was a big reduction in manual data prep time and a more structured foundation for analysis and visualization.
- Developed Python libraries to automate entity scoring and prioritization using rule-based logic and statistical thresholds, reducing analyst workload by over 50%
I developed a set of Python libraries designed to automate the scoring and prioritization of entities—things like organizations, individuals, or events—based on a mix of rule-based logic and statistical thresholds.
The goal was to help analysts quickly focus on what mattered most, rather than manually reviewing large volumes of data.
To start, I worked with analysts to understand how they were manually assigning importance or risk levels to different entities. We mapped out a set of rules—things like frequency of appearance, co-occurrence with certain keywords, source reliability, and temporal patterns. I then codified those rules into reusable Python functions that could be applied consistently and at scale.
On top of the rule logic, I incorporated basic statistical methods—for example, flagging outliers based on frequency distributions, or dynamically adjusting thresholds depending on baseline activity over time. This helped reduce false positives.
In terms of impact, we saw a more than 50% reduction in manual workload related to triaging entities. Analysts could trust the scores to bubble up high-priority items.
Overall, it was a great example of how thoughtful automation—grounded in real workflows—can** save time and improve decision quality without overcomplicating things**.
- Proactively identified gaps in existing manual workflows and drove the development of automation tools that significantly reduced analyst workload, earning recognition from project leadership for initiative and problem-solving.
One of the things I try to bring to any role is an eye for inefficiencies—especially repetitive, manual tasks that could be handled more reliably through automation. In this case, I noticed that a lot of our analysts were spending a significant amount of time doing tasks like manually filtering, formatting, or tagging data from incoming reports before any real analysis could begin.
So I took the initiative to map out those workflows—step by step—by sitting down with a few analysts, observing their day-to-day process, and asking questions to understand the bottlenecks.
From there, I developed a set of Python scripts that automated key parts of their workflow. For example, one script handled initial triage of reports—automatically extracting key fields and tagging entities based on predefined rules—while another cleaned and normalized data formats for easier readability. I also implemented** logging and historical data logs so analysts can revisit pasts versions of reports.**
The impact was pretty clear: we saw a significant drop in manual workload—upwards of 40 to 50%—and the feedback from analysts was really positive.
Project leadership formally recognized the work, which was really encouraging. I think it was a good example of how taking the time to really understand a problem—then delivering a focused, lightweight solution—can make a big difference.
- Supported the implementation of secure DevSecOps practices, integrating automated security checks into CI/CD pipelines to enhance system security.
My role was to help integrate security checks directly into our CI/CD process, so that vulnerabilities could be caught early, without slowing down development.
To start, I worked closely with both the development and infrastructure teams to understand where security gaps might exist in our current workflows. One of the first things we did was define a set of baseline security checks—things like scanning for outdated dependencies, or potentially unsafe coding patterns like hardcoded credentials.
From there, I helped integrate these checks into our existing CI/CD pipeline using GitLab CI. For example, we introduced a static analysis step that would run automatically on every merge request and flag high-risk issues before code could be merged. We also added dependency checks to catch vulnerable libraries before they were deployed.
One thing I was careful about was making sure the checks were practical and non-disruptive. I coordinated with dev leads to set appropriate thresholds—so we didn’t block builds unnecessarily, but still enforced a baseline level of hygiene. Where possible, I also included links to documentation in the pipeline output to help developers understand and fix flagged issues quickly.
The outcome was a more security-aware development process without sacrificing speed or agility.
It was a great experience not just technically, but also in terms of cross-team collaboration. I learned a lot about how to embed security in a way that supports development rather than getting in the way of it.
- Participated in threat modeling for a sensitive database of terrorist activity records, identifying vulnerabilities (e.g., injection risks, misconfigured permissions) and proposing mitigation strategies under the guidance of senior engineers.
I was fortunate to participate in a threat modeling exercise focused on a sensitive database containing terrorist activity records.
My role involved examining the data flow and access patterns around the database—looking at how data was ingested, queried, and exposed through various internal tools. We used a combination of data flow diagrams and STRIDE methodology to systematically assess risks across different components.
One of the key vulnerabilities I helped identify was the risk of injection attacks, particularly in areas where user-supplied input could be used to construct database queries. I reviewed parts of the codebase with that in mind and flagged a few spots where proper parameterization wasn’t being used consistently. I also noted that some services had overly broad database permissions, which violated the principle of least privilege and could have been exploited if any single service was compromised.
I worked closely with the team to document these findings and propose concrete mitigation strategies, like enforcing prepared statements, input validation layers, and redesigning the permission model to be more granular.
What I appreciated most was how collaborative the process was. I wasn’t just handed a checklist—I was encouraged to ask questions, challenge assumptions, and contribute meaningfully to the discussion. It really helped sharpen my understanding of secure system design and how to balance usability, performance, and risk mitigation in environments where the consequences of failure are very real.
- Automated enforcement of security policies using Python scripts and YAML-based policy-as-code templates, streamlining compliance checks and reducing manual configuration errors.
This was part of a broader effort to improve our security posture by reducing the number of manual steps involved in configuration and compliance checks. I developed a system that used Python scripts in combination with YAML-based policy-as-code templates to automate the enforcement of security policies across various environments.
The idea was to codify our security requirements—things like password policies, and encryption standards—into a set of YAML templates that defined what “secure” should look like for a given resource or service. Then, I built Python scripts that could read these templates, check live system states or config files against them, and flag or even auto-correct deviations.
For example, in one use case, the script would scan deployed cloud resources and compare their configurations—like open ports, logging settings, or encryption flags—against what was defined in the YAML. If it found a mismatch, it would either generate a report for manual review.
This approach really streamlined our compliance checks. Instead of teams needing to interpret documentation and manually verify settings, we had a repeatable, testable way to validate compliance.
- Led the design and development of secure APIs for risk scoring and threat prioritization, ensuring input validation, rate limiting, and encryption were applied to prevent data breaches and mitigate security risks.
I led the design and development of a set of APIs that supported our risk scoring and threat prioritization workflows, which were critical for downstream systems and analyst tools to access scoring results and prioritize actions.
Because we were dealing with sensitive data, security was a top priority from the very beginning. I took a security-by-design approach throughout the development process, starting with input validation to protect against common injection attacks.
We also implemented rate limiting to defend against misuse. This was especially important because some of these endpoints were accessed by automated systems in near-real-time. I used token buckets with tunable thresholds based on usage profiles.
For encryption, all API traffic was served over HTTPS with TLS 1.2+, and we ensured any data at rest—especially related to scoring models or flagged entities—was encrypted using AES-256. Authentication was handled through API keys and OAuth tokens, with scoped access to ensure that consumers only had the minimum necessary permissions.
On top of that, I also included structured logging and audit trails within the APIs, so we could monitor access patterns, detect anomalies, and respond quickly if anything unusual occurred.
The end result was a secure, reliable set of services that became foundational to several of our security and intelligence tools. It was a great opportunity to bring together both my backend engineering and security knowledge in a way that had a direct impact on operational efficiency and data protection.
- Refactored an automated API-driven ingestion system, streamlining the processing of large intelligence datasets from multiple sources, reducing manual effort by 50% and ensuring secure access control and audit trails for sensitive data.
I was tasked with refactoring an automated ingestion system that pulled intelligence data from a variety of sources—some structured, some unstructured—and fed it into our internal processing pipeline. The original system had become frustrating over time as it didn’t follow best standards for software engineering.
I started by auditing the existing codebase and workflows to identify pain points. One major issue was that the logic for parsing, transforming, and storing data was neither **optimal or intuitive That made it fragile and hard to scale. **
So I redesigned it into a modular, API-driven architecture—separating data acquisition, parsing, and storage into cleanly defined stages with error handling and logging at each step.
In terms of impact, this refactor reduced manual intervention by around 50%, because it could now handle more edge cases and failures automatically. For example, if a source went offline or a file had schema drift, the system could isolate the issue, log it, and continue processing the remaining data.
Because we were working with sensitive datasets, I also incorporated audit trails to log every request and transformation for accountability. That gave both analysts and security teams better visibility into how the data was being handled and who was using it, which was really important for compliance and traceability.
- Conducted code reviews to ensure secure API development, enforcing best practices for authentication, authorization, and data encryption across all integrations, minimizing the risk of security vulnerabilities in deployed systems.
Yes, I was actively involved in conducting code reviews with a focus on security, especially around API development and integration points. These APIs were often handling sensitive data, so ensuring they were built securely was critical to minimizing long-term risk.
My approach to code reviews combined general quality checks with specific security best practices. I paid close attention to things like authentication and authorization logic—making sure roles and permissions were clearly scoped, avoiding hardcoded credentials, and ensuring tokens or keys were properly validated and rotated.
Beyond just finding issues, I tried to make the process collaborative—explaining why something might be risky and offering alternative solutions.
Over time, we also created checklists and reusable code patterns to make it easier for developers to get security right from the start. That really reduced repeat issues and helped maintain consistency across our APIs.
- Led documentation and knowledge transfer efforts for a risk scoring framework, ensuring long-term maintainability and onboarding support for future team members.
After developing and stabilizing our risk scoring framework, I led the documentation and knowledge transfer efforts to ensure that future developers—and analysts—could pick it up and understand not just how it worked, but why certain design decisions were made.
I started by creating clear, structured documentation covering everything from the data model and scoring logic to API endpoints and edge-case handling. This included diagrams, sample input/output payloads, and common troubleshooting steps.
One thing I was intentional about was capturing institutional knowledge—not just “what the system does,” but also things like prior decisions we considered and rejected, known limitations, and recommended practices for extending the system safely. That helped reduce the ramp-up time for new team members and minimized the chance of future changes introducing regressions.
