End-To-End Application Security Program For A Global Ecommerce Brand

Built a mature AppSec program from scratch — integrating SAST, DAST, and SCA tooling into CI/CD pipelines and reducing critical vulnerabilities by 85% within six months.

End-To-End Application Security Program For A Global Ecommerce Brand

Technologies:

  • GitHub Actions
  • SonarQube
  • Snyk

Overview

A global ecommerce brand processing millions of transactions a day had no formal application security programme. Penetration tests ran once a year and produced hundreds of findings, but most were never fixed, and developers had no security tooling in their daily workflow to catch problems earlier. Leadership knew that something had to change.

We designed and built a complete application security programme around the way the brand's nine product teams already worked. Automated code scanning, dependency checks and dynamic testing were added to every pipeline, developers were trained to fix issues themselves, and a single vulnerability management process gave leadership a clear, honest view of risk across every application. Everything was designed to fit existing workflows.

Within six months, critical vulnerabilities across the portfolio fell by 85%, and new issues were being found and fixed in hours instead of months. Security stopped being an annual audit that everyone dreaded and became a normal, measurable part of how the brand builds and ships software to customers around the world. The programme is now run fully in-house, with our team providing specialist testing and advice whenever it is needed.

Website and mobile app screens shown as floating mockups
Location
London, United Kingdom
Industry
Global Ecommerce Brand
Duration
Jun 2024 to Dec 2024

Services Used

  • Application Security
  • CI/CD Security
  • Penetration Testing
  • Secure Code Review
  • Developer Training
  • Vulnerability Management

“Collaborating with ENCYPHERS was a great experience. They brought deep technical expertise and a genuinely practical mindset, building security into our pipelines without slowing our teams down. Critical vulnerabilities dropped dramatically, our developers now fix issues themselves, and security finally feels like part of how we build rather than a blocker.”

Donald MuskFounder & CEO, Ecommerce Brand

Market Opportunity & Challenges

To build a developer-friendly application security programme that finds and fixes vulnerabilities before they reach production, across nine core applications serving millions of shoppers every day.

Market Opportunity

  • Catch vulnerabilities early, when they are cheapest to fix
  • Protect millions of customer accounts and payment details
  • Turn annual pen test findings into continuous improvement
  • Give developers security feedback inside their own tools
  • Build a single, trusted view of application risk
  • Strengthen customer trust as the brand expands globally

Challenges Faced

  • Annual pen tests produced hundreds of findings with low fix rates
  • No SAST, DAST or SCA tooling in any CI/CD pipeline
  • OWASP Top 10 issues present in 6 of 9 core applications
  • Security team working entirely reactively
  • Nine product teams with different stacks and release cycles
  • Developers had little or no secure coding training
  • No consistent way to track or prioritise vulnerabilities

The Encyphers Solution

We started by understanding how each of the nine product teams actually built and shipped software. Interviews, pipeline reviews and a look at past pen test results showed where security effort would make the biggest difference with the least disruption to delivery. The findings shaped a realistic plan for the months ahead.

From that picture, we designed a programme covering tooling, processes, roles and metrics. Each team was assigned a security champion, clear standards were agreed for what must be fixed before release, and a simple maturity model gave leadership a way to track progress across every application over the six months that followed.

The design was deliberately practical, focusing on a small number of high-impact changes rather than a long list of rules. It gave every team a clear picture of what good looked like and how their own work fitted into the wider programme from the very first week. Progress against the maturity model is now reviewed every quarter, keeping leadership engaged and teams motivated.

Security programme plan on a laptop

We added static application security testing to every repository, scanning code on each pull request for issues like injection flaws, insecure configuration and hard-coded secrets. Results appear directly in the code review, next to the exact line that needs attention. Scans run in under five minutes, so they never slow reviews.

To avoid overwhelming developers, we tuned the rules carefully for each language and framework and removed checks that produced mostly false positives. New code is held to a strict standard, while existing issues are tracked separately and worked through over time, so teams were never blocked by years of old technical debt on day one.

Within three months, the number of new vulnerabilities introduced into the codebase fell sharply. Developers began fixing most static analysis findings before their code was even merged, often in minutes, and security reviews became faster because the obvious problems had already been dealt with. Existing issues in older code have also been steadily reduced, with each team clearing its backlog sprint by sprint.

Static code analysis results on a laptop

Some vulnerabilities only appear when an application is running, so we added dynamic testing against every staging environment. Automated scans explore the site like an attacker would, probing forms, APIs and authentication flows for weaknesses after every deployment. Test data was used throughout, so no customer was ever affected.

Scans were configured with authenticated test accounts, so they could reach the parts of the site real customers use, including checkout, account settings and order history. Findings are automatically deduplicated and linked to the responsible team, so developers see a short list of real problems instead of a long, noisy report nobody reads.

Dynamic testing caught several serious issues before release, including an access control flaw that would have let one customer view another customer's orders. Each was fixed in staging, long before it could affect a real shopper or damage the brand's reputation. Dynamic scans now run automatically after every staging deployment, so new issues are caught within hours of release.

Dynamic application scan on a laptop

Modern applications are built mostly from open-source libraries, and the brand's nine applications relied on thousands of them. We introduced software composition analysis to track every dependency and flag known vulnerabilities as soon as they are published. Every repository was covered within the first two weeks.

Automated pull requests now suggest safe upgrades for vulnerable libraries, complete with release notes and test results, so updates take minutes rather than days. Licence checks were added at the same time, protecting the brand from accidentally shipping code with licences that conflicted with its commercial terms or distribution model.

When a major vulnerability was disclosed in a widely used logging library mid-project, the brand identified every affected application within an hour and patched them all the same day. Previously, the same exercise would have taken weeks of manual investigation across teams. Keeping libraries current has also improved performance and stability, giving developers another reason to stay up to date.

Dependency scan report on a laptop

Automated tools catch a lot, but not everything. For the highest-risk areas, including checkout, payments and account management, our consultants carried out detailed manual code reviews alongside the developers who owned them. Sessions were run with the owning developers in the room.

Reviews focused on business logic flaws that scanners cannot find, such as discount codes that could be reused, race conditions in stock reservations and weaknesses in password reset flows. Every finding was explained in context, with example fixes written in the team's own language and coding style so they could be applied straight away.

The reviews uncovered several high-impact issues that no previous pen test had spotted. Just as importantly, the developers involved learned how to spot similar problems themselves, spreading security knowledge through the teams far faster than a written report ever could. Manual reviews are now scheduled for every major feature that touches payments, accounts or personal customer data.

Code review session on a laptop

Tools only work if developers understand and trust them. We ran hands-on secure coding workshops for every team, built around real vulnerabilities from the brand's own applications rather than generic textbook examples. Workshops were short, practical and built around real code.

Each team's security champion received deeper training and a direct line to our consultants, becoming the first point of contact for security questions within their team. A shared internal guide covers common fixes, approved libraries and secure design patterns, so developers can find answers quickly without waiting for the central security team to respond.

Developer satisfaction with security tooling rose significantly over the programme, and the time taken to fix vulnerabilities dropped by more than half. Security became something teams felt confident handling themselves, rather than something imposed on them from outside. The champions network still meets every month to share lessons, tools and examples of good secure design.

Developer training materials on a laptop

Findings from every source, including scanners, code reviews and pen tests, now flow into a single vulnerability management process. Each issue is automatically deduplicated, scored for real business risk and assigned to the right team with a clear deadline. Duplicate findings from different tools are merged automatically.

Service-level targets were agreed with engineering leadership: critical issues fixed within seven days, highs within thirty. A shared dashboard shows each team's open vulnerabilities, fix times and trends, creating healthy visibility without turning security into a blame exercise or a source of friction between teams.

Six months in, critical vulnerabilities across the portfolio were down 85%, and over 90% of new criticals were fixed within their target window. Leadership now has an accurate, up-to-date picture of application risk instead of a once-a-year snapshot from an external report. Engineering leaders now review these metrics monthly alongside delivery, quality and reliability figures.

Vulnerability management dashboard on a laptop

With the foundations in place, we shifted the annual pen test into a continuous assurance model. Smaller, focused tests now run throughout the year, targeting new features and high-risk changes as they are released rather than all at once. Test scopes are agreed with each product team in advance.

Each test is scoped with the product team beforehand, and findings are fed straight into the existing vulnerability process instead of arriving as a separate report. Retesting happens automatically once a fix is deployed, so issues are confirmed closed quickly and nothing is quietly forgotten between testing cycles or team changes.

The brand now gets far more value from its security budget, with testing effort matched to real risk throughout the year. Security has become a steady, predictable part of delivery, supporting the brand's growth into new markets rather than slowing it down. Testing results are shared with the board every quarter, showing clear, steady progress across the whole portfolio.

Continuous assurance report on a laptop
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Technologies & Platforms we used

TensorFlow
Tensor Flow
Keras
Keras
PyTorch
Pytorch
Lisp
Lisp
NLTK
NTKL
spaCy
Spacy
OpenAI
Open AI
Plotly
Ploty
Matplotlib
Matplotlib
Pandas
Pandas
OpenCV
Opencv
NumPy
Numpy
TensorFlow
Keras
PyTorch
Lisp
NLTK
spaCy
Keras
NumPy
OpenAI
Plotly
Matplotlib
Pandas
OpenCV
Technology
TensorFlow
Keras
PyTorch
Lisp
NLTK
spaCy
Keras
NumPy
OpenAI
Plotly
Matplotlib
Pandas
OpenCV
Technology

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