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If you’ve ever worked on secure coding or DevSecOps pipelines, you’ve probably come across the term SAST  — Static Application Security Testing. These are the tools that scan your source code for vulnerabilities, misconfigurations, or insecure patterns before  your application even runs. One of the most popular and lightweight tools in this space is Semgrep  — a static analysis tool that’s fast, open-source, and surprisingly easy to customize. Let’s talk about what makes Semgrep stand out and why it’s becoming a go-to for developers and security engineers. -------------------------------------------------------------------------------------------------------- What is Semgrep? Semgrep is an open-source static analysis engine   It’s designed to analyze source code across multiple programming languages, helping you find potential vulnerabilities or risky code patterns. Unlike many bulky enterprise scanners, Semgrep is: Lightweight  – It’s easy to set up and run locally or in CI/CD pipelines. Extensible  – You can easily write your own custom rules. Multi-language  – It supports a wide variety of languages, including: Go, Java, JavaScript, Python, Ruby, TypeScript, C#, and even markup formats like JSON and YAML. That’s pretty rare for an open-source static analysis tool. -------------------------------------------------------------------------------------------------------- How Semgrep Works Semgrep doesn’t just perform simple text searches like grep. Instead, it parses your code into Abstract Syntax Trees (ASTs)  — which basically means it understands  the structure of your code. This allows Semgrep to detect complex issues with a high level of accuracy. Semgrep’s scanning logic is powered by rules , which are written in YAML format. These rules describe specific code patterns or behaviors you want to find. The cool part? You can easily write, test, and publish your own rules using the Semgrep Playground . -------------------------------------------------------------------------------------------------------- The Semgrep Registry If you don’t want to start from scratch, the Semgrep Registry  has you covered. It’s a large community-driven collection of over 1,000 rules , all publicly available at semgrep.dev/explore . Some of the most popular rulesets maintained by r2c are: r2c-ci  – Focused on high-confidence, low-noise rules for CI/CD pipelines. r2c-security-audit  – A more in-depth security audit ruleset for catching subtle vulnerabilities. You can think of these like plug-and-play templates that help you quickly integrate security scanning into your workflow. -------------------------------------------------------------------------------------------------------- Running Semgrep from the Command Line Running a Semgrep scan is incredibly simple. Once you’ve installed it (via CLI or Docker), you can run a full security audit of your codebase with just one command: semgrep --config "p/r2c-security-audit" This command downloads and runs over 200 security-focused rules from the Semgrep registry against your code. By default, results are printed directly to your terminal (stdout). -------------------------------------------------------------------------------------------------------- Semgrep Command-Line Options If you want to customize your scans, Semgrep’s CLI gives you several options. Here are the most useful ones: Option Description -f or -c Specify a YAML configuration file, folder of rule files, or a ruleset from the registry -e Run a custom search pattern directly from the command line -l Restrict scanning to a single language -o Save results to a file or send them to a remote endpoint You can explore all options by running: semgrep --help -------------------------------------------------------------------------------------------------------- Writing Custom Rules One of Semgrep’s biggest strengths is how easy it is to write custom detection rules . You can do this using the Semgrep Playground  ( semgrep.dev/editor ), which lets you test your rules interactively. For example, let’s say you want to identify any AWS policy file that gives full S3 access (s3:*). Here’s a simple rule written in YAML: rules: - id: s3_wildcard_permissions pattern: | { "Effect": "Allow", "Action": "s3:*" } message: Semgrep found a match severity: WARNING This rule will flag any JSON configuration file where Action: "s3:*" appears — a sign of an overly permissive IAM policy. What’s amazing here is that the same rule syntax can be used to scan source code, cloud configs, or even Kubernetes YAML files . So, whether you’re a developer or a cloud engineer, you can use one tool and one query language to identify risky patterns across your entire DevOps pipeline. -------------------------------------------------------------------------------------------------------- CI/CD Integration Semgrep was designed with automation in mind. It fits seamlessly into CI/CD pipelines using: CLI commands Docker containers GitHub Actions This makes it perfect for embedding into your build pipelines , so you can automatically catch vulnerabilities before code is merged — without slowing down developers. -------------------------------------------------------------------------------------------------------- Final Thoughts Semgrep is a great example of how open-source security tools  can compete with — and often outperform — commercial solutions. It’s fast, flexible, and transparent, giving you the power to scan code in your own way. Whether you’re a security engineer trying to enforce secure coding standards or a developer looking to clean up risky code, Semgrep is a tool worth adding to your toolkit. If you want to try it out, here are the official resources: 🔗 GitHub: returntocorp/semgrep 🌐 Semgrep Registry 🧰 Semgrep Playground --------------------------------------------Dean----------------------------------------------------

Configuration code and application code both need to be treated with the same rigor as any other software. Bugs in configuration can be especially dangerous because they can create operational outages, scalability issues, or security vulnerabilities at scale . Unfortunately, reviewing this code often requires specialized knowledge of the underlying platforms and tools, which makes mistakes easier to miss. This is where static analysis  comes in. Static analysis tools help detect common errors, enforce best practices, and highlight potential security issues—before they impact production. Static Analysis for Configuration Management Chef RuboCop  – Ruby style and code quality checks. Cookstyle  – Chef’s official linting tool, powered by RuboCop. Foodcritic (Sunsetted 2019)  – legacy tool, no longer maintained. Puppet Puppet-lint  – syntax and style checks. puppet-lint-security-plugins  – additional security rules. Puppeteer  – automated testing for Puppet code. Ansible Ansible-lint  – ensures playbooks follow best practices. KICS  – infrastructure-as-code security scanner. AWS CloudFormation cfn_nag  – scans templates for insecure patterns. cfripper  – evaluates IAM and security risks. cfn-python-lint  – syntax validation. CloudFormation Guard  – policy-as-code validation. Checkov  – scans IaC for misconfigurations. Static Analysis for Application Code Java Code Quality & Bugs : FindBugs (legacy) , SpotBugs , PMD , Checkstyle . Security : Find Security Bugs , fb-contrib . Advanced Analysis : Error Prone (Google) , Infer (Meta) , SonarSource . .NET / C# FxCop  – legacy, built into Visual Studio. StyleCop  – style enforcement. Puma Scan  – security plugin. Security Code Scan  – Roslyn-based security checks. Roslynator  – analyzers & refactorings. SonarSource . JavaScript Style & Quality : ESLint , JSHint , JSLint . Security : NodeJsScan . Others : Closure Compiler , Flow , SonarSource . PHP Phan  + Taint Check Plugin . PHP CodeSniffer , PHP Mess Detector . Progpilot , Exakat . RIPS (commercial) , SonarSource . Ruby Brakeman  (security), Dawnscanner . RuboCop , RubyCritic . Railroader , SonarSource . Python Bandit , Dlint . Pylint , Pytype . Pyre , Pyright . SonarSource . C / C++ Cppcheck , Clang Analyzer , OCLint . Flawfinder , Infer , SonarSource . Objective-C / Swift Xcode Analyze . SwiftLint , OCLint . Infer , Faux Pas , SonarSource . Android Android Lint , Qark . Custom Lint Rules . Infer . Go Vet , Lint . errcheck , dingo-hunter . Gosec , SafeSQL . SonarSource . Multi-Language Static Analysis For teams working across multiple languages and frameworks: GitHub CodeQL  – semantic code analysis. Semgrep  – fast, rule-based multi-language scanner. Best Practices for Using Static Analysis Integrate Early  – run lightweight checks in CI/CD pipelines to catch issues before deployment. Balance Depth & Speed  – use incremental scans during commits, and schedule deep scans out-of-band. Triage Findings  – security teams should filter false positives and prioritize high-confidence issues. Automate Feedback  – push findings directly into developer workflows (IDE plugins, backlog tickets). Combine Tools  – no single tool covers everything; use a combination for better coverage. Conclusion Static analysis is not just about checking code quality—it’s about catching vulnerabilities early, reducing technical debt, and preventing misconfigurations from becoming large-scale risks . With the right mix of tools and practices, development and security teams can collaborate more effectively, building software that is both reliable and secure. ---------------------------------------------Dean----------------------------------------------------

When a developer pushes code, it kicks off the Commit phase  of the DevOps pipeline. This is where the magic of automation happens: builds, tests, scans, and packaging all before the code goes anywhere near production. But here’s the trick: we need to build security into this phase —without slowing developers down. What Security Checks Fit Here? Think of the Commit phase as speed security . We don’t have hours; we have minutes. So our checks need to be lightweight but effective: Unit Tests  → Catch coding mistakes immediately. Incremental SAST (Static Analysis Security Testing)  → Scan only the code that changed. Lightweight Linting & Style Checks  → Make code easier to review and maintain. If all tests pass, we sign the build and store it in a secure artifact repository . This guarantees that what gets deployed later hasn’t been tampered with. -------------------------------------------------------------------------------------------------------- The Goal of SAST in CI/CD Here’s the important mindset shift: Traditional SAST  tries to find all  the vulnerabilities in the codebase. That’s slow and overwhelming. Modern SAST in CI/CD  focuses on catching new vulnerabilities introduced by the latest changes . This way, developers get feedback right away and can fix issues before moving on. -------------------------------------------------------------------------------------------------------- Deep vs. Incremental Scanning Not all scans are created equal: Full scans  → Take hours or even days. Perfect for scheduled jobs (nightly, weekly). Incremental scans  → Only look at the code that changed. Perfect for CI/CD, fast feedback in a few minutes. -------------------------------------------------------------------------------------------------------- Tuning Scanners: Take the Hit for the Team Static analyzers are notorious for false positives . If every scan blocks developers with noise, the pipeline will get ignored—or worse, bypassed. That’s why the security team must “take the false positive hit.” Tune scanners to focus on high-risk, high-confidence findings . Store configuration in version control  so changes are audited. Work with engineers to agree on what’s important enough to block the pipeline. Pro tip: Fail the pipeline if any critical/high findings show up. Save the low-confidence stuff for deeper out-of-band scans. -------------------------------------------------------------------------------------------------------- Writing Your Own Checkers Sometimes tools don’t catch everything. That’s where custom checkers  come in: Simple: grep for dangerous functions or hardcoded secrets. Advanced: write custom plugins for SAST tools like PMD or SonarQube. Examples: Flag eval() or exec() in Python. Detect insecure crypto functions (like MD5, SHA-1). Catch hardcoded AWS keys or passwords. -------------------------------------------------------------------------------------------------------- Different Tools, Different Strengths Not all static analysis tools are equal. Here’s a cheat sheet: Coding style tools  → Checkstyle, PMD, StyleCop, ESLint Not security-focused, but make code easier to read and review. Bug pattern detectors  → FindBugs, FxCop Improve reliability and catch some security issues. Security-focused scanners  → Find Security Bugs, Puma Scan, Fortify, SonarQube Security Rules Look for vulnerabilities using data flow, control flow, and taint analysis. Important: There’s very little overlap between these tools. If you want good coverage, you’ll probably need more than one scanner . To handle duplicates across multiple tools, use aggregators like: OWASP Defect Dojo ThreadFix Code Dx -------------------------------------------------------------------------------------------------------- How to Fit This into CI/CD Here’s a practical Commit phase workflow: Developer pushes code → CI kicks in. Unit tests  + lint checks  run in parallel. Incremental SAST scan  runs in parallel. If time limit breached, send an alert + break scans into smaller chunks. If all checks pass → build is signed & pushed to artifact repo. If critical issues found → block the pipeline until fixed. Meanwhile, schedule nightly full scans  with all checkers enabled. The security team reviews those results, filters out noise, and creates tickets for real issues. -------------------------------------------------------------------------------------------------------- The Bigger Picture Security in the Commit phase isn’t about finding everything . It’s about: Catching mistakes early Giving fast, actionable feedback Building security into the team’s Definition of Done ---------------------------------------------Dean--------------------------------------------------

Every codebase has certain files that you just don’t want anyone to casually edit. Think about: Authentication and login logic Password handling and crypto libraries Admin panels and backend controls Code that touches private user data Deployment scripts and pipelines If these pieces of code are changed without proper review, the result could be security vulnerabilities, downtime, or even a data breach . That’s why we need a system to detect high-risk code changes and enforce extra approvals. This is where Code Owners files, custom alerts, and pull request templates  come into play. What are Code Owners? A Code Owners file  is a simple text file you place in the root of your repository. It tells GitHub, GitLab, or Bitbucket who “owns” which parts of the code. Think of it like a digital lock: If someone changes a sensitive file, the lock requires approval from the person or group listed as the code owner. Without approval, the change can’t be merged. Rules that appear later override earlier ones. For example: All developers can edit code by default. But if someone touches AccountController.cs, the security team must approve it. Even the   Code Owners file itself is protected (because changing it could bypass the whole system). Why This Matters Let’s say a junior developer accidentally modifies a password hashing function. Without code owners, the change might slip through with just a casual peer review. With code owners, the security team must approve  it before it gets merged. This gives you: Control  – High-risk code can’t be changed by mistake. Visibility  – Security teams see changes that matter. Accountability  – The right people approve sensitive updates. Detecting Risky Code Changes with Alerts One of the security teams took this a step further. Instead of only depending on code owners, they hash sensitive files  and write unit tests to ensure nothing changes silently. Here’s a C# example: [Theory] [InlineData("/Web/AccountController.cs", "2aabf33b66ddb07787f882ceed0718826af897q7")] [InlineData("/Shared/Services/Cryptography/Hash.cs", "87f88d137d37a7ed908737552eed0c524133b6")] public void HighRiskCode_ChecksumTest(string file, string checksum) { bool match = checksum.Equals(Hash.GetChecksum(file)); if(!match) NotificationService.RequestCodeReview(file); Assert.True(match); } What this does: Each high-risk file has a checksum (unique fingerprint). If the file changes, the test fails. When it fails, the system notifies the developer, their manager, and the security team. This runs automatically in Continuous Integration (CI) . So if someone modifies crypto code, you’ll know immediately. Security Checklists with Pull Request Templates What about developer awareness? That’s where pull request templates  come in. A template is just a Markdown file that gets pre-filled when someone opens a pull request. For example: ### Security Checklist - [ ] Have I reviewed this change for security risks? - [ ] Am I touching authentication, crypto, or private data? - [ ] Have I notified the security team if needed? Developers must check these boxes before submitting. It’s not automatic enforcement, but it creates awareness and accountability . Separation of Duties in Approvals The final piece of the puzzle is workflow separation : Developer opens a pull request. Code owners are automatically assigned. Security team (or other owners) review the changes. Once approved, the merge triggers build pipelines and deployments. This ensures no single person can sneak in a risky change. Approvals must come from the right group of people. ------------------------------------------------------------------------------------------------------------ Putting It All Together This combination gives you: Prevention (owners block unapproved changes)0 -- CODEOWNERS   Detection (unit tests catch file modifications) -- Add checksum-based unit tests   Awareness (checklists guide developers) -- Leverage templates   Governance (audit trail of approvals) -- Require approvals By combining these, you create a secure GitFlow workflow  where high-risk code is guarded, reviewed, and deployed with confidence.

Branches in Git are like “lanes” of code. Some lanes are safe for experiments (feature branches), but others are critical highways (main, develop, release branches). If someone pushes bad code straight into main, it could break production in seconds. That’s why branch protections  exist: to put guardrails around your most important branches. ------------------------------------------------------------------------------------------------------------- GitHub Branch Protections On GitHub, branch protections are very customizable . Here are the most important options: Require Pull Request Reviews (✅ Always Enable) At least 1–2 people must review before merging. Stops “lone wolf” coding straight into production. Include Administrators (✅ Enable this!) By default, admins can bypass protections. If their account or SSH key is compromised, attackers can push directly into main. Enabling this forces even admins to follow the rules. Require Signed Commits (Optional, but Strong) Developers must sign commits with GPG keys. Prevents impersonation or “mystery commits” from unverified users. Disallow Force Pushes (✅ Keep Disabled) Prevents someone from overwriting history with git push --force. Protects from lost work or sneaky backdoor commits. Disallow Deletions (✅ Keep Disabled) No one should be able to delete main or develop. Period. In short:  On GitHub, always enforce reviews, admin restrictions, no force pushes, no deletions . Signed commits are a bonus layer. ------------------------------------------------------------------------------------------------------------- GitLab Branch Protections GitLab’s rules are simpler but still powerful: Allowed to Merge: Decide which group(s) can merge changes. Typically: “Maintainers” for main, “Developers” for develop. Allowed to Push: Should always be set to No One  for protected branches. This forces Merge Requests only , which means reviews and checks must pass. Require Code Owner Approval (Premium Feature): Lets you require approvals from the owner of a specific file or directory . Example: Only security engineers can approve changes to auth/. Great for granular control, but requires GitLab Premium. In short:  In GitLab, enforce No direct pushes, only MRs, proper role-based merging, and (if available) CodeOwner approvals. ------------------------------------------------------------------------------------------------------------- Azure DevOps Branch Policies Azure DevOps calls them Branch Policies , and they’re pretty flexible: Require Minimum Number of Reviewers: Define how many approvals are needed before merge. Typically 2 reviewers for critical branches. Disallow Self-Approval: Prevents developers from approving their own PRs. Helps enforce “separation of duties.” Block Last Pusher from Approving: The person who made the last commit can’t count as a reviewer. Stops someone from sneaking in changes and approving themselves. Reject if Any Reviewer Votes No: Default: if someone rejects, the PR is blocked. You can override this, but generally safer to block unless explicitly approved. Reset Votes on New Commits: If new code is pushed after a review, approvals reset. Ensures fresh eyes on fresh changes. In short:  Azure DevOps gives you more knobs to tune — use them to enforce real reviews, prevent self-approvals, and reset approvals if new code is added. ------------------------------------------------------------------------------------------------------------- Comparing GitHub vs GitLab vs Azure DevOps Feature GitHub GitLab Azure DevOps Require Reviews ✅ Yes ✅ Yes (via MRs) ✅ Yes Admin Restrictions ✅ Yes ❌ Limited ✅ Yes Signed Commits ✅ Yes ❌ Not built-in ✅ Yes (via policy) Force Push Protection ✅ Yes ✅ Yes ✅ Yes Branch Deletion Protection ✅ Yes ✅ Yes ✅ Yes Code Owner Rules ✅ Yes ✅ Premium only ✅ Yes Advanced Review Rules ⚠️ Basic ⚠️ Limited ✅ Very Flexible ------------------------------------------------------------------------------------------------------------- Best Practices (No Matter the Platform) Protect main and develop (and release branches if used). No direct pushes — all changes via PR/MR . Always require at least 1–2 reviewers , preferably more for sensitive code. Don’t let devs approve their own changes. Restrict admins — they should follow the same rules. Disable force pushes and deletions on critical branches. Use Code Owners  for sensitive areas (auth, payments, infra). Branch protections = “guardrails” for your repo .They don’t just enforce process, they reduce insider threats, mistakes, and even admin account compromises . ---------------------------------------------------Dean---------------------------------------------------

When we talk about security in DevSecOps, a lot of risks start right in the repo  before code ever hits CI/CD pipelines. This is where Git commit hooks, pre-commit frameworks, and branch protections come into play. ------------------------------------------------------------------------------------------------------ Git Hooks – Security’s First Gate Think of Git hooks  like little security guards that live inside your Git repository. They’re scripts that run when certain Git events happen (like committing, pushing, or merging code). Local hooks (on developer’s machine): pre-commit: Runs before the commit is made . Perfect spot to check for secrets or run linters. commit-msg: Ensures the commit message follows your org’s rules (e.g., must have a Jira ID). post-commit: Runs right after a commit . Useful for notifications/logging. Server-side hooks (on remote repo): pre-receive: Runs before changes are accepted on the remote repo. Stops bad commits at the gate. update / post-receive: Good for enforcing team policies or kicking off workflows. Why it matters:  Hooks let you catch issues early  (like hardcoded AWS keys, unsafe configs, bad commit messages) before they ever hit CI/CD. ------------------------------------------------------------------------------------------------------ Pre-Commit Frameworks Yes, you could  write your own Git hook scripts, but that’s messy. Instead, teams often use frameworks that manage hooks across multiple languages and repos. Two popular ones: Yelp’s Pre-Commit : You create a .pre-commit-config.yaml in your repo. Add rules (e.g., check for YAML syntax errors, remove trailing spaces, scan for secrets). Every developer who clones the repo gets the same hooks. Overcommit : Another Git hook manager with built-in checks. Widely used in security-focused teams. Example (.pre-commit-config.yaml): repos: - repo: https://github.com/pre-commit/pre-commit-hooks rev: v2.0.1 hooks: - id: check-yaml - id: end-of-file-fixer - id: trailing-whitespace - repo: https://github.com/psf/black rev: 24.3.0 hooks: - id: black With this: Every commit will check if your YAML is valid. Remove trailing spaces automatically. Format Python code using Black. Run pre-commit install once, and boom — your repo now has built-in security and quality checks before anyone can commit bad code. ------------------------------------------------------------------------------------------------------ Peer Code Reviews Automated checks are great, but nothing beats human eyes . That’s where code reviews  come in. Big companies never  let code go straight into production without review. Why? Transparency  – At least one other person knows what’s being changed. Accountability  – Developers are more careful if they know someone else will review their work. Security  – Reviewers can spot “code smells” like: Hardcoded passwords/API keys Custom (and unsafe) cryptography Suspicious or obfuscated logic Incorrect handling of sensitive data Pro tip: Create a security checklist  for reviewers (data validation, error handling, proper logging, etc.). Make high-risk code (auth, payments, encryption, APIs)  require senior or security-team review. Randomly assign reviewers to reduce risks of collusion. ------------------------------------------------------------------------------------------------------ Branch Protections (Your Repo’s Guardrails) Even with hooks and reviews, you don’t want people pushing code directly into main or develop. This is where branch protections  come in. All major platforms (GitHub, GitLab, Azure DevOps, Bitbucket) support this. What branch protections do: ❌ Prevent deleting critical branches (like main). ❌ Prevent direct pushes to release branches. ✅ Require pull/merge requests  for changes. ✅ Force code reviews/approvals before merging. ✅ Allow you to define who  can approve changes (e.g., security must approve auth changes). Example: On GitHub, you can require that: At least 2 reviewers  approve before merge. All checks (tests, scans, pre-commit) must pass. No one can merge unless those conditions are met. This ensures security isn’t optional — it’s baked into the workflow . ------------------------------------------------------------------------------------------------------ Putting It All Together Even if one layer misses something, the others catch it. ✅ Hooks = automatic checks ✅ Pre-commit frameworks = easy setup ✅ Peer reviews = human judgment ✅ Branch protections = safety nets Together, they make your repo a security-first environment .

Hey everyone 👋 So here’s the deal: I’m not a DevOps engineer. I come from the Incident response/Forensic side. But in my current organization, we’re working on DevSecOps , and that means I had to start learning how security fits into the DevOps world. I thought, instead of keeping all this in my notes, why not share it here? I’m trying to simplify things as much as possible. If you find mistakes or think I should add something, just let me know. This is a journey, and I’m still learning too. Where Does Security Fit in DevSecOps? When we talk about DevOps pipelines (Continuous Integration and Continuous Delivery), security can’t just be an afterthought. We have to weave it into every stage. CI (Continuous Integration):  Fast cycle, quick checks. Mostly useful for catching small mistakes like hardcoded passwords, dangerous functions, or dependency issues. Think of it as your “first line of defense.” CD (Continuous Delivery):  This is where the deeper stuff happens—like scanning, penetration testing, and verifying that everything is safe before going live. Bottom line: CI is for quick wins  in security, while CD is for serious checks . Step 1: Pre-Commit (Before the Code Even Lands) This is the stage where developers are still writing or committing code. From a security perspective, this is where we want to stop problems before  they even enter the repo. Here’s what to focus on: Data classification:  Figure out which data is sensitive (personal info, passwords, financial data, etc.). Different data types need different protection levels. Platform risks:  Choosing a cloud, OS, database, or framework? Each has its own security risks. Understand them before locking in your choice. Tool support:  Make sure your toolchain supports security scans (SAST, DAST, IAST). 💡 Tip:  Treat this stage like a health check . If something feels risky—new API, new user role, new database—it’s worth reviewing from a security lens. Quick Threat Modeling (Don’t Panic, It’s Simple) “Threat modeling” might sound heavy, but think of it like asking common-sense questions: Did we just change the attack surface (e.g., opened a new port, exposed an API)? Did we add/upgrade any major library or framework? Are we touching sensitive areas like authentication, encryption, or access control? Are we handling new sensitive data? Are we touching critical or high-risk code? If the answer is yes  to any of these, slow down and do a deeper review. Tools That Can Help (So You’re Not Doing This Alone) Good news: you don’t have to do everything manually. There are plenty of tools and plugins that can catch security issues right as developers are coding. Here are some handy ones: VS Code Plugins: Semgrep  – smart code scanning Checkov  – IaC (Infrastructure as Code) scanning cfn_nag  – scans AWS CloudFormation IntelliJ / Eclipse Plugins: Built-in code inspections (Java, etc.) FindBugs / SpotBugs  – bug detection Find Security Bugs  – security-focused checks For .NET / Visual Studio: Puma Scan Security Code Scan Microsoft DevSkim Commercial Options (if your org wants premium tools): Checkmarx Coverity Fortify Veracode Greenlight Think of these as “spell checkers for code security.” They highlight issues in real time while developers type or when they hit save. ------------------------------------------------------------------------------------------------------------- Example: Spotting Risks in a Repo Let’s say you scan your repo with a tool like SCC (Sloc Cloc and Code) , and it tells you that the main language being used is Dockerfile. As a security person, that’s an “aha” moment. Why? Because Docker means containers, and containers bring their own security challenges—like image scanning, Dockerfile best practices, and proper configuration. So just by knowing the tech stack, you already know what security areas to focus on. ------------------------------------------------------------------------------------------------------------- Wrapping It Up The main point is this: Security in DevOps doesn’t have to be scary or complex. Start small: Catch issues early with pre-commit checks and IDE plugins. Ask simple threat-modeling questions whenever something major changes. Use the right tools to automate scanning so developers get feedback fast. By doing this, security becomes part of the normal workflow—not a blocker at the end. ---------------------------------------------Dean------------------------------------------------------ Keep following and keep checking my best — we will proceed further in the next article.

When installing or running new software, your operating system’s security configuration can change behind the scenes — new services, registry keys, ports, or even accounts might get added. Tracking all of that manually is nearly impossible. That’s where Attack Surface Analyzer (ASA)  comes in. It’s a Microsoft tool that helps you capture and compare snapshots of your system’s state so you can see what changed before and after an installation. Super handy if you want to harden your system or just understand what software is really doing. ------------------------------------------------------------------------------------------------------------- Installing Attack Surface Analyzer Since ASA is built on .NET Core , we first need the .NET SDK : dotnet --version If you don’t have it, grab it from the .NET SDK download page . Step 1 – Install ASA via .NET CLI Once you’ve got .NET, open your terminal/command prompt and run: dotnet tool install -g Microsoft.CST.AttackSurfaceAnalyzer.CLI Some time you get error like below: Step 2 – Verify Installation After installing, check that ASA works by typing: asa.exe --help This will list all available commands. ------------------------------------------------------------------------------------------------------------- Fixing Installation Issues When I first tried, I hit an error because NuGet wasn’t set up properly . If the dotnet tool install command doesn’t work for you, here’s the fix: dotnet nuget add source https://api.nuget.org/v3/index.json --name nuget.org Then re-run: dotnet tool install -g Microsoft.CST.AttackSurfaceAnalyzer.CLI Still stuck? You can always download the binaries directly from the ASA GitHub releases page . 👉 Once installed, the tool gets placed under this folder: C:\Users\\.dotnet\tools So if asa isn’t recognized, just navigate there and run the commands directly. ------------------------------------------------------------------------------------------------------------- Using ASA – CLI Mode The core idea is simple: take a snapshot, install or change something, then take another snapshot and compare. 1. Collect a Snapshot To capture the current system state (baseline): asa collect -a This collects info about files, services, users, ports, etc. 2. Compare Snapshots After making changes (e.g., installing an app), run another collection. Then export and compare: asa export-collect 3. Explore Options If you’re curious about all the available commands: asa.exe --help ------------------------------------------------------------------------------------------------------------- Using ASA with GUI If you don’t love CLI, ASA also provides a web-based interface . To launch it: asa gui Then open your browser and go to: http://localhost:5000 You’ll see a dashboard where you can visualize results, compare data, and interact with snapshots more easily. ------------------------------------------------------------------------------------------------------------- Features Worth Highlighting Tracks file system changes Monitors services, ports, and firewall rules Keeps an eye on user accounts and permissions Works across Windows, Linux, and Docker Offers both CLI and GUI  options Supports rule authoring  for custom checks ------------------------------------------------------------------------------------------------------------- Wrapping Up Attack Surface Analyzer makes it way easier to see what’s going on under the hood of your OS. Whether you’re testing new software, checking for unwanted changes, or just geeking out about system internals, ASA gives you a clear before/after picture. I recommend starting with the CLI for automation, then switching to the GUI if you prefer visuals. And don’t forget — if installation gives you trouble, adding the NuGet source usually fixes it. --------------------------------------------Dean--------------------------------------------------------

When you’re in the middle of an incident response, memory analysis is one of the most powerful ways to uncover what really happened on a compromised machine . RAM is volatile—it disappears once the system is powered down—so examining it quickly and thoroughly can give you insights into malware, lateral movement, persistence, and more. This will walk you through examining RAM and dumping processes using Volatility (standalone) on Windows . It’s not exhaustive, but it will get you started with the essential plugins and workflow. ------------------------------------------------------------------------------------------------------------- Step 1: Identify the Operating System Before diving into analysis, determine the operating system of the memory image. windows.info This will give you basic information about the image and help guide which plugins will work properly. Step 2: Examine Processes List running processes windows.pslist.PsList > pslist.txt Do a process scan  (check running PIDs and PPIDs): windows.psscan.PsScan > processes.txt Look for hidden/rogue processes windows.psxview.PsXView > psxlist.txt List and analyze DLL handles of suspicious processes windows.dlllist.DllList > dlllist.txt Step 3: Network Connections Check for active or historical network connections: windows.netscan.NetScan > netscan.txt Step 5: Registry and Execution Artifacts UserAssist Keys windows.registry.userassist.UserAssist > userassist.txt Amcache windows.amcache.Amcache > amcache.txt Shimcache (AppCompatCache) windows.shimcachemem.ShimcacheMem > shimcache.txt These registry-based artifacts often reveal executed programs, including those that may not show up in process lists. Step 6: Dump Processes and DLLs Create a directory inside your Volatility standalone folder for process dumps. Dump all processes: --dump -Processes Or dump DLLs from suspicious processes: --dump -DLL Once dumped, scan them with multiple antivirus engines. A quick way: right-click the directory and run scans. Step 7: Look for Injected Code Use malfind to find embedded/injected code within processes: windows.malfind.Malfind > malfind.txt Dump these results to the same directory and scan with AV. Step 8: Search for IP Addresses Use strings or bstrings to extract potential network indicators from memory: strings memorydump.raw | findstr "IP" > IP.txt 📌 Guide: https://www.cyberengage.org/post/memory-forensics-using-strings-and-bstrings-a-comprehensive-guide Step 9: Explore More Plugins Volatility has many plugins beyond the basics. You can always check available options with: volatility -h Each case is different, so don’t limit yourself to just the above commands. Bonus: Alternative Tool – MemProcFS One of my favorite tools alongside Volatility is MemProcFS . Unlike Volatility, you don’t need to dump anything manually—everything is already “mounted” and accessible like a file system. 📌 Guide: https://www.cyberengage.org/post/memory-forensics-using-strings-and-bstrings-a-comprehensive-guide ------------------------------------------------------------------------------------------------------------- "Note: The commands shown above are not full commands but rather the plugins you can use. You’ll need to run them with Volatility 3 in the proper format The plugin names I’ve listed are to guide you through which modules are useful during analysis." ------------------------------------------------------------------------------------------------------------- Final Thoughts These steps and plugins are enough to get you started with memory analysis during an investigation. As you get deeper into cases, you’ll find yourself using other plugins or combining results with disk/timeline analysis. The main takeaway: Start broad with processes and network activity Narrow down to execution artifacts and persistence Always dump and scan suspicious processes Correlate memory findings with disk and event log evidence Memory doesn’t lie—if something malicious ran, you’ll find traces of it here. Happy hunting! -----------------------------------------------------------Dean---------------------------------------------

When I look back at all the articles, guides, and tool walkthroughs I’ve written, one question keeps coming up: “Where do we actually start?” It’s true—I’ve shown you dozens of tools, ways to parse artifacts, and countless steps for analysis. But an investigator or IR professional still needs a structured process . That’s why I decided to create this methodology. Think of it as a roadmap. Every investigation is different —you may skip some steps or add a few more depending on the case—but this gives you a clear starting point and flow to follow. Along the way, I’ll point to my detailed articles so you can dive deeper into each stage. ------------------------------------------------------------------------------------------------------------- 1. Mount Disk Image and Scan with Multiple AV Products This is the “low hanging fruit.” Always start simple. Mount your image with Arsenal Image Mounter (my go-to). Or collect the image in VHDX  format using KAPE  (which I always do). FTK Imager is another solid option. 📌 Guides: KAPE Series: https://www.cyberengage.org/courses-1/kape-unleashed%3A-harnessing-power-in-incident-response FTK Imager Quick Guide (PDF available under Resume → Quick Guides): https://www.cyberengage.org ------------------------------------------------------------------------------------------------------------- 2. Generate a Super Timeline You need context around suspicious events. A super timeline  shows you what happened before, during, and after. Use log2timeline/Plaso  (my personal choice). Or Magnet AXIOM  (great commercial option). 📌 Guides: Log2timeline article: https://www.cyberengage.org/courses-1/decoding-timeline-analysis-in-digital-forensics ------------------------------------------------------------------------------------------------------------- 3. Memory Analysis Memory tells the real-time story of execution. Focus on: Running processes and DLLs → dump and scan with AV Network connections Rogue or hidden processes Command history Malfind for injected code 📌 Guides: Memory Forensics Series: https://www.cyberengage.org/courses-1/mastering-memory-forensics%3A-in-depth-analysis-with-volatility-and-advanced-tools Recommended reads: Step-by-Step Guide to Uncovering Threats with Volatility MemProcFS/MemProcFS Analyzer: Comprehensive Analysis Guide ------------------------------------------------------------------------------------------------------------- 4. Process the Event Logs Events give timeline + context. Look for: Remote logins (Security, TerminalServices-LocalSessionManager%Operational) Service installs (System Event ID 7045) Type 3 logons around ransomware execution or PsExec installs Group policy changes AV being disabled/uninstalled Lateral movement Malicious PowerShell activity User account creation Event log clearing 📌 Guides: Lateral movement automation: https://www.cyberengage.org/post/lateral-movement-analysis-using-chainsaw-hayabusa-and-logparser-for-cybersecurity-investigations Hayabusa log analysis tool: https://www.cyberengage.org/post/hayabusa-a-powerful-log-analysis-tool-for-forensics-and-threat-hunting ------------------------------------------------------------------------------------------------------------- 5. File System and MFT Check: Suspicious file creations (batch scripts, malware samples, attacker directories) When encryption started (for ransomware) Suspicious compressed files → possible exfiltration 📌 Guides: MFT parsing with MFTECmd: https://www.cyberengage.org/post/mftecmd-mftexplorer-a-forensic-analyst-s-guide NTFS Journaling series: https://www.cyberengage.org/courses-1/ntfs-journaling ------------------------------------------------------------------------------------------------------------- 6. Malicious File Executions Key artifacts: Amcache ShimCache Prefetch UserAssist (NTUSER.DAT) 📌 Guides: Windows Forensic Artifacts: https://www.cyberengage.org/courses-1/windows-forensic-artifacts ------------------------------------------------------------------------------------------------------------- 7. Persistence Mechanisms Always check: SOFTWARE\Microsoft\Windows\CurrentVersion\Run SOFTWARE\Microsoft\Windows\CurrentVersion\Runonce NTUSER.DAT Run keys C:\Windows\System32\Tasks WMI Activity Operational.evtx 📌 Guides: Registry Forensic Series : https://www.cyberengage.org/courses-1/mastering-windows-registry-forensics%3A ------------------------------------------------------------------------------------------------------------- 8. USN Journal One of my favorite artifacts—great for file creations & deletions. 📌 Guide: USN Journal parsing with MFTECmd: https://www.cyberengage.org/post/ntfs-journaling-in-digital-forensics-logfile-usnjrnl-parsing-of-j-logfile-using-mftecmd-ex ------------------------------------------------------------------------------------------------------------- 9. Link Files See what files were accessed by a compromised account. 📌 Guide: https://www.cyberengage.org/courses-1/windows-forensic-artifacts ------------------------------------------------------------------------------------------------------------- 10. Shellbags Shows which folders were accessed. 📌 Guide: https://www.cyberengage.org/courses-1/windows-forensic-artifacts ------------------------------------------------------------------------------------------------------------- 11. Log Analysis Go beyond Windows: Firewall/VPN logs IIS logs IDS/IPS logs DNS logs SIEM-correlated logs 📌 Guide: Network Forensics: https://www.cyberengage.org/courses-1/network-forensic ------------------------------------------------------------------------------------------------------------- 12. Lateral Movement and Exfiltration Check: NTUSER.DAT → Terminal Server Client WinSCP.ini (shows remote connections & staging folders) OpenSSH logs Prefetch for sshd.exe SRUM DB for large transfers 📌 Guide: SRUM DB analysis: https://www.cyberengage.org/courses-1/srum%3A-unveiling-insights-for-digital-investigations ------------------------------------------------------------------------------------------------------------- 13. On-System Email Analysis Look for phishing origins: Original email Suspicious attachments Malicious documents 📌 Guide: Identifying malicious software: https://www.cyberengage.org/post/identifying-malicious-software-a-guide-for-incident-responders ------------------------------------------------------------------------------------------------------------- 14. Internet History Critical for phishing & exfil evidence. 📌 Guide: Browser forensics series (open-source tools): https://www.cyberengage.org/courses-1/introducing%3A-browser-forensics-%E2%80%93-your-ultimate-guide-to-manual-analysis ------------------------------------------------------------------------------------------------------------- 15. Data Carving Recover deleted or hidden items. 📌 Guide: Data carving series: https://www.cyberengage.org/courses-1/data-carving%3A-advanced-techniques-in-digital-forensics ------------------------------------------------------------------------------------------------------------- 16. Index Searching Search for IOCs in slack space & unallocated clusters. 📌 Guide: Windows forensic artifacts (indexing section): https://www.cyberengage.org/courses-1/windows-forensic-artifacts ------------------------------------------------------------------------------------------------------------- Modern Tools Worth Adding (2025) Velociraptor  – IR at scale, timeline generation, artifact parsing. KAPE  – rapid artifact collection. Eric Zimmerman’s Tools (EZ Tools)  – Amcache, Registry, Prefetch, etc. Timesketch  (Tmeline Explorer) – timeline review. Volatility3  – modern memory analysis framework. ------------------------------------------------------------------------------------------------------------- Final Thoughts This methodology isn’t about rigid rules. It’s about giving you a process to start with . Each case is different—sometimes you’ll skip steps, sometimes you’ll go deeper in certain areas. The key takeaway: Start broad (AV scans, timelines) Narrow down (memory, logs, persistence, artifacts) Always document and correlate across multiple data sources Use this roadmap, explore the linked guides, and adapt it to your investigations. Stay sharp, and happy hunting!

You know that old principle we all learned in programming — divide and rule ? Break the big problem into smaller pieces, solve those, and the whole thing becomes manageable. Well, guess what? That same idea is a lifesaver in incident response . A massive breach can feel overwhelming, but if you chop it down into standard, repeatable tasks , suddenly it’s not this monster anymore. It’s just a list of jobs to get done. Why Standard Tasks Matter Here’s the trick: don’t let the investigation depend on who  is working the case. If Investigator A does a host triage one way and Investigator B does it completely differently, you’ll end up with confusion and wasted time. But if everyone runs tasks the same way — and documents them properly — then anyone can pick up where the last person left off. That means: You can swap people in and out without breaking the flow. You can bring in someone fresh for a few hours and know exactly what they’ll deliver. You don’t lose speed when people rotate shifts. And here’s something I love: sometimes when you rotate people, that fresh pair of eyes notices something new that others missed. That’s the magic of collaboration. Resources: More Than Just People Now let’s talk about resources . Most folks think resources = analysts. But in IR, it’s bigger than that. Sure, you need responders, malware reversers, and intel analysts . But in big cases? You might also need enterprise architects (to help with recovery) or even negotiators (for ransomware). Don’t forget storage, bandwidth, and processing power. Tools are easy to buy but not easy to integrate. You can’t just toss in a new platform mid-incident and expect magic. Good processes and content take time to develop and test. Here’s the kicker: resources don’t scale easily in IR . That’s why the IR lead’s job isn’t just running the case technically — it’s also managing these resources like a chess game. Standardization Keeps the Chaos Out This is where standardization saves your life. Don’t just throw bodies at the problem — that creates noise and costs money. IR companies overstaff cases to cover. That only makes things worse. Instead, plan tasks and shifts carefully. Here’s a simple example: Three analysts on 8-hour shifts. A host triage takes ~5 hours(for full image) ~2 hours (kape image) → each analyst can do two a day. Persistence or evidence stacking takes ~2.5 hours. Onboarding a new analyst? Budget ~2.5 hours. If you manage the case close to this level of planning, the whole engagement runs smoother, faster, and cheaper. Deviations happen, but the goal is control, not chaos . The Power of Task-Driven Questions Here’s something : don’t deep-dive without a clear question. If you throw an analyst at a hard drive with no direction, they’ll dig for days and still not know when to stop. That burns time, money, and morale. Instead, assign tasks based on questions . For example: ❌ Inefficient:  “Analyze this entire hard drive.” ✅ Efficient:  “Find evidence of what data was exfiltrated from this host.” The moment the question is answered, the task is complete. Of course, analysts should still look a little left and right for context, but they shouldn’t drift aimlessly. This is how you keep investigations sharp, measurable, and efficient. ------------------------------------------------------------------------------------------------------------- Wrapping It Up Do that, and even the biggest, nastiest breach starts to look manageable. You’re not just firefighting anymore — you’re running a structured, controlled investigation. Because at the end of the day, IR isn’t about looking busy. It’s about restoring order in the middle of chaos .

When people hear incident response , they often picture someone hammering away at a terminal, pulling artifacts, and cracking malware. And yes, the technical side is critical. But in reality, IR is just as much about people, communication, and coordination as it is about tools and commands. ------------------------------------------------------------------------------------------------------------ Technical Mastery Still Matters Even though IR isn’t only about technology, make no mistake: you need people who know their craft . As an lead, when you assign a task, you expect results—not excuses. That means analysts must not only know how to operate their tools but also understand what the artifacts mean. Misinterpretation can be as dangerous as missing data entirely. Why do some of the best responders come from penetration testing backgrounds ? Simple: they understand the attacker’s playbook from the inside. Knowing how an adversary thinks makes it easier to spot their tracks. But the bar keeps rising. Modern enterprise networks are sprawling, with features like Active Directory forests, Azure AD, and cloud integrations. To hunt attackers effectively, you need more than endpoint knowledge —you need to understand how enterprises are actually built and where attackers can exploit trust relationships. This knowledge isn’t just for detection. It’s also essential for remediation . When you recommend rebuilding or rearchitecting systems, your suggestions have to be realistic in the context of a large enterprise. “Tools help you spot the smoke. But only experience tells you whether it’s a campfire or a forest fire.” ------------------------------------------------------------------------------------------------------------ Documentation: The Unsung Hero If visibility is the lens of IR, documentation is the map . Without it, you’re wandering in circles. Here’s why it’s indispensable: Tracking progress  – With multiple analysts working a case, you need to know what’s already been done, what failed, and what still needs attention. Stakeholder communication  – Clear documentation lets you brief management, legal, and external partners confidently at any point in the investigation. Intelligence integration  – While you document, threat intel teams can map artifacts against known adversaries, often giving you new leads mid-investigation. Future learning  – Every incident becomes a training case for the next. Documentation preserves lessons learned. Liability protection  – A clear record of what was done, when, and why is invaluable if the response ever comes under legal or regulatory scrutiny. ------------------------------------------------------------------------------------------------------------ Soft Skills: The Glue Holding It Together For the breached organization, a cyberattack is usually an exceptional crisis . For the IR team Bridging that emotional and professional gap requires soft skills —especially from the incident lead. The lead : Translate complex technical issues into language the board can act on. Support corporate communications and legal teams. Keep IT and SOC teams aligned and working toward the same goal. Reassure customers and partners that the situation is under control. Keep morale up—sometimes literally by ordering pizza and making coffee. In short: the IR lead is not just a commander, but a translator, negotiator, and motivator. ------------------------------------------------------------------------------------------------------------ The Anatomy of an IR Team A strong IR team isn’t just a handful of analysts— it’s a collection of specialized roles working in harmony: IR Lead  – Orchestrates the entire response, maintains the “big picture,” manages the artifacts and spreadsheets, and acts as the primary point of contact for external stakeholders. Analysts  – Carry out host triage, log sweeps, threat hunting, and containment tasks. They are the boots on the ground. Malware Analysts  – Dissect malicious code to uncover capabilities, extract C2 addresses, and provide in-memory IOCs such as YARA rules. Their work often determines how wide and deep the compromise goes. Threat Intelligence Analysts  – Correlate evidence with known threat actor behavior, enrich the case with context, and distribute curated IOCs to the right channels. Each role is critical, but the magic happens in how they collaborate . Clear tasking, shared documentation, and open communication are what transform a group of individuals into a coordinated response force. ------------------------------------------------------------------------------------------------------------ Final Thoughts IR may be a technical field, but technical skills are only part of the story . Documentation keeps everyone aligned, soft skills keep stakeholders calm, and specialized roles ensure no stone is left unturned. Because at the end of the day, incident response is about restoring control in the middle of chaos—and that takes more than just tools.