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November 20 2022 | 3 min read

Stop wasting your time on irrelevant changes while developing software

Technical | November 20 2022 | 3 min read


If you are a developer, software engineering manager, or deal with application security in any capacity, you probably faced the challenge of reading commit diffs, Pull Requests or entire repository histories in order to determine if something material (more on that later!) was introduced. If you manage a team of three developers, this might be manageable, but as your team grows to dozens, or thousands of developers – changes are flying all around and it is impossible to find needles in haystacks.

As a developer, I read code much more than I write code. After many years, I still find it very hard to understand the real semantic changes in a Pull Request, let alone the developer’s intent behind them. This is amplified by the possible implications of adding or omitting even just one character.

When I review Pull Requests I use GitHub’s diff tool which shows modified lines. When I dive into a repository history, I explore commit diffs, and when I see something suspicious in code, I might run git blame, or go over the git history of that specific file. All of these tools lack the semantic understanding of the code. They can’t distinguish between a logical change and a simple refactoring, and they can’t pinpoint the commit where a certain RESTful API was introduced, or tell me if a new OSS package was introduced in a 300 file change Pull Request.

Another limitation of these tools is that they are limited to one repository. Often, my perspective includes multiple repositories or modules within repositories that together assemble an Application. The Application, in its broader definition, includes external systems like ticketing systems, CI/CD pipelines, API Gateways and Cloud infrastructure, and even the developers themselves – all of which are missing from the diffing tools’ context. This is especially problematic when I want to perform a security code review, threat model, or decide if a certain change warrants a pen test

Material Changes

A material change is an update that has the potential to introduce significant risk into application, infrastructure or open source code, but it cannot be identified by looking at the code alone. It requires analyzing a number of contextual factors, from developer metadata to the business impact of the change. Apiiro analyzes and correlates data from Design to Code to Cloud, including:

  • Code (e.g., application code, Infra-as-Code, open source code, configuration files)
  • Contributors (e.g., developer experience, behavioral patterns, permissions and locations)
  • Metadata (e.g., User stories, commit messages, Pull Request discussions)
  • SSDLC Processes (e.g., Risk Assessments, Security Code Reviews, and Pen Tests)
  • Third-party tools (e.g., SAST, SCA, DAST, IAST)
  • Runtime (e.g., API Gateway, Kubernetes)

Why do I care?

In order to determine if some risk is introduced, organizations tend to choose either of the following:

  1. Slow down – all changes need to go through a review, where someone reads the code and determines whether it needs further inspection and changes, or not.
  2. Aggregate inspections to once every month (or any other time frame), where the inspection is much harder (as the “diff” is much bigger, and might span various areas of a code base). 
  3. Have developers answering manual Risk Assessment Questionnaires, hoping that developers would know that a Material Change was introduced.

With Apiiro’s Material Change detection, you can trigger workflows (like opening a ticket with the relevant context, assigned to the relevant person) or even block a PR or break a build – only when a Material Change is introduced. A good example is referring or automatically assigning the risk team to Pull Requests – only if they introduce new PII, or any PII is exposed in an API – otherwise, they are free to focus on other tasks, and not block developers from bringing value to customers!

Tools → GitHub Diff Apiiro Material Changes
Processes ↓
Pen-Testing Manual, time consuming, contextless Automatically trigger  when necessary, provides context for the pen-tester
Risk Assessment Manual, time consuming, error prone Fully automatic
Threat Modeling Manual, time consuming, contextless Automatically trigger when necessary, provides context
Security Code Review Manual, time consuming Automatically trigger when necessary, provides context for the reviewer

The following table will help a pen-tester, AppSec engineer, or software engineering manager choose the right tool when performing pen-testing, security code review, or threat modeling:

Tools GitHub Diff Apiiro Material Changes
Features ↓
Track all changes (e.g., spaces, dots)
Identify code owners per line

Using git blame – contextless
(e.g., space, dots)

Identify code owners per code component (e.g., API, DataModel)

Contextual (e.g., developer X changed the logic of an API POST /login/check-login)

API logic changes
Data Model logic changes
Docker files changes

(e.g., space, dots)

Contextual (e.g., Docker instructions and configurations)

OSS packages changes (e.g., downgrade, upgrade, added, removed)
Infra-as-Code changes
Identify logic changes across repositories
PII changes
OSS licenses changes
Secrets in code changes
Authentication changes 
Authorization changes
Input validation changes
Technologies usage changes (e.g., DB frameworks, Serialization frameworks)

Key Takeaways

  1. Stop the risk team from wasting your time by using manual Risk Assessment Questionnaires
  2. Automation is the key – move from reactive mode to proactive mode to increase velocity with confidence! Get automated notification at the commit / PR when your developers introduce material changes 
  3. Stop using contextless tools when conducting a security code review or threat model, or assessing the risk of a PR

Yonatan Eldar

Co-Founder & CTO