# SAST Published articles for SAST. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## When to use SAST versus an LLM security scanner DevFeed: [When to use SAST versus an LLM security scanner]() Original publisher: [Read original article]() Author: Chris Widstrom Published: 2026-09-16T00:00:00Z Content type: comparison Language: en Sources: [GitLab]() Topics: [Security](), [LLM security](), [Large Language Model](), [Application Security](), [Testing](), [Frontier Model](), [audit trail](), [business logic]() Tags: [application-security](), [audit-trail](), [features](), [frontier-model](), [llm](), [llm-security](), [product](), [sast](), [security](), [vulnerabilities]() ### AI overview This comparison explains when to use static application security testing (SAST) and when to use LLM-based security review. SAST provides predictable, repeatable, low-cost scanning across commits, while LLMs can reason about context and identify some intent-based or novel issues. The article recommends using both according to the task and scan scope. ### Source excerpt You're probably running some version of this experiment already: Point a frontier model at a merge request and ask it to double as a vulnerability scanner. On a single merge request, it often works well. The model reads the code, reasons about what it's supposed to do, and catches real issues, sometimes ones a pattern-based scanner misses entirely. So the next thought is reasonable: If a model reviews one merge request this well, why not let it replace the scanner across your whole pipeline? That's where it breaks down. Running a frontier model as your primary scanner on every commit across an enterprise codebase costs more and behaves less predictably, than asking it to review one merge request. If you run application security for a platform or product team, the decision was never mutually exclusive: static application security testing (SAST) or LLM scanners. It's which one runs where. Get that split right and you keep deterministic coverage on every commit, add reasoning where it earns its place, and avoid both an unpredictable inference bill and gaps in your audit trail. Here's how the two compare: Deterministic scanner (SAST)AI-based review (LLM)CostBetter Fractions of a cent per scan, fixed and predictable. A paid inference call per scan; cost scales with code volume and is hard to forecast.ConsistencyBetter Flags the same vulnerabilities, every time. Can flag or miss different vulnerabilities on the same code, run to run.Audit evidenceBetter Reproducible: same fixed method every time, mapped to a CWE Output can vary between runs, harder to use as standalone audit evidence.Vulnerability typesDepends on the vulnerability type Pattern-based, traceable flaws (injection)Depends on the vulnerability type Intent-based and novel flaws: missing auth checks, broken ownership logic, and issues not yet mapped to a CVE or CWE. Could reason about business logic using context from issues, epics, and docs.Validating a finding Flags potential vulnerabilities; can't confirm whe ## How to Catch Security Vulnerabilities in Code Before They Reach Your Pull Requests DevFeed: [How to Catch Security Vulnerabilities in Code Before They Reach Your Pull Requests]() Original publisher: [Read original article]() Author: Umair Mirza Published: 2026-09-14T22:23:01Z Content type: tutorial Language: en Sources: [freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More]() Topics: [Application Security](), [Security](), [Vulnerabilities](), [Secret Scanning](), [Git](), [pull-requests](), [ci](), [Command-line interface](), [.NET](), [Python](), [coding]() Tags: [application-security](), [ci](), [cli](), [code](), [devsecops](), [git](), [net](), [pull-requests](), [python](), [sast](), [secret-scanning](), [security](), [security-vulnerabilities](), [software-development](), [testing](), [vulnerabilities]() ### AI overview This tutorial explains how to shift security checks earlier in development by running SAST locally with Git pre-commit hooks. It demonstrates using the DevSkim CLI to detect insecure coding patterns, pairing it with Gitleaks for dedicated secret scanning, validating the setup with an intentional failure, and enforcing the checks in CI. ### Source excerpt Security reviews are most effective when developers receive feedback while the code is still fresh in their minds. Waiting until a pull request, CI build, or penetration test to find exposed credentia ## Harness Announces AI-Assisted Security Capabilities for Vulnerability Scanning, Triage, Remediation, and Response DevFeed: [Harness Announces AI-Assisted Security Capabilities for Vulnerability Scanning, Triage, Remediation, and Response]() Original publisher: [Read original article]() Author: Rahul Sood Published: 2026-08-19T00:00:00Z Content type: release Language: en Sources: [Harness Blog]() Topics: [Security](), [Vulnerabilities](), [Artificial Intelligence](), [Large Language Model]() Tags: [ai](), [automated](), [blog](), [report](), [sast](), [scanner](), [security](), [vulnerabilities]() ### AI overview Harness announces security capabilities using AI SAST, automated vulnerability triage, remediation, zero-day response, and virtual patching. The article discusses how LLM-based scanners can increase vulnerability findings while introducing false positives, latency, and cost concerns. ### Source excerpt Harness enables security at machine speed with AI SAST, automated vulnerability triage, remediation, zero-day response, and virtual patching. | Blog ## AI SAST: Challenges for Application Security Teams DevFeed: [AI SAST: Challenges for Application Security Teams]() Original publisher: [Read original article]() Author: Nicole Morgan Published: 2026-08-05T00:00:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [Application Security](), [Artificial Intelligence](), [AI Development](), [software composition analysis]() Tags: [ai](), [ai-coding](), [application-security](), [sast](), [sca](), [vulnerabilities]() ### AI overview This article discusses how AI-assisted development increases pressure on static application security testing (SAST) programs. It identifies false positives, vulnerability backlogs, low remediation rates, and limited pipeline coverage as obstacles to helping developers address findings quickly. ### Source excerpt Learn how AI is transforming SAST, reducing security friction, and helping teams secure AI-generated code without slowing delivery. | Blog ## From 750 Hours to 2 Hours: AI-Powered Security Triage at Razorpay DevFeed: [From 750 Hours to 2 Hours: AI-Powered Security Triage at Razorpay]() Original publisher: [Read original article]() Author: Prathamesh Joshi Published: 2026-06-09T14:56:35Z Content type: article Language: en Sources: [Razorpay Engineering - Medium]() Topics: [Security](), [Artificial Intelligence](), [Vulnerabilities](), [Architecture & Design](), [Code](), [API](), [GitHub](), [Sanitization](), [API keys]() Tags: [ai](), [api](), [architecture](), [code](), [credentials](), [github](), [hardcoded-credentials](), [sast](), [security](), [vulnerabilities]() ### AI overview Razorpay describes an AI-powered security triage system built to reduce the manual workload caused by large volumes of SAST, dependency, and secret-detection alerts. Its first live layer retrieves issue context, source code, data flows, and sanitization logic to distinguish genuine vulnerabilities from false positives, with reported accuracy of 75-80%. ### Source excerpt Co-authors: Mahlaqahaque Mh, Keertiv, Hari Prasad Pujari How we taught AI to read code like a senior security engineer Every day, Razorpay engineers ship thousands of lines of code. Every line births new security findings. SAST scanners flag suspicious patterns. Dependency checkers find vulnerable libraries. Secret detection tools catch hardcoded credentials. The alerts pile up. Hundreds become thousands. The backlog becomes noise. We hit a breaking point. Developers faced security ticket counts climbing into the thousands, with most of them turning out to be false positives. The classic "alert that cried wolf" scenario played out daily. When everything is marked critical, nothing is. Developers stopped trusting security findings altogether. Security engineers weren't having a better time. Validating issues manually while fielding constant ad-hoc requests from frustrated developers. Trying to stop a waterfall with a teaspoon. The human bottleneck became the limiting factor in our security posture. The core problem was simple. Traditional static analysis tools excel at finding patterns that might be vulnerabilities. They lack context. They can't distinguish between a properly sanitized SQL query and a vulnerable one. Between a test API key and a production secret. Between a dangerous data flow and one protected by business logic. For every 10 alerts, 7-8 were false positives. Manual triage became the bottleneck. Security couldn't scale with engineering velocity. That's when we built what we call the Autonomous Security Special Ops system. An AI-powered engine that handles the heavy lifting so humans can focus on what actually matters. The Three-layer Intelligence System Rather than throwing more human hours at the problem, we built an AI architecture operating in three layers. L1: Context-Aware AI Triage (Live). Our intelligent first responder. Powered by 29 specialized sub-skills , it reads code context like a senior security engineer. When a SAST finding lands, L1 ## AI-driven zero-day combinations are challenging software security and open-source consumption DevFeed: [AI-driven zero-day combinations are challenging software security and open-source consumption]() Original publisher: [Read original article]() Published: 2026-05-28T00:00:00Z Content type: opinion Language: en Sources: [Chainguard: Unchained]() Topics: [Artificial Intelligence](), [open-source-security](), [Security](), [Open Source](), [Machine Learning, Security Attacks](), [Critical Infrastructure](), [openssf](), [sigstore]() Tags: [ai](), [anthropic-mythos](), [chainguard-containers](), [critical-infrastructure](), [fork](), [mfa](), [mythos](), [open-source](), [open-source-security](), [openssf](), [oss](), [project-glasswing](), [rust](), [sast](), [secure-open-source](), [security](), [sigstore]() ### AI overview The article argues that Mythos represents a potential new class of software-security threat: AI-driven combinations of existing issues that can produce more serious attacks than individual scanner findings. It discusses the limits of government regulation and calls for stronger trust infrastructure, coordinated disclosure, and safer open-source consumption. ### Source excerpt Mythos is changing software security fast. AI-driven zero-days demand new trust infrastructure, coordinated disclosure, and secure open source consumption. ## Security at scale: Preventing thousands of vulnerabilities without developer friction DevFeed: [Security at scale: Preventing thousands of vulnerabilities without developer friction]() Original publisher: [Read original article]() Author: Rodrigo Fernandez Apas Published: 2026-01-06T13:28:53Z Content type: article Language: en Sources: [Mercado Libre Tech]() Topics: [Security](), [Vulnerabilities](), [Application Security](), [Microservices](), [Architecture & Design]() Tags: [application-security](), [developer](), [framework](), [javascript](), [microservices](), [paved-road](), [sast](), [security](), [vulnerabilities](), [vulnerability]() ### AI overview Mercado Libre describes centralizing security protections in development frameworks and platform layers across an environment of around 20,000 developers and 35,000 microservices. The company reports that about 95% of SSRF, XSS, and CSRF vulnerability reports are now unexploitable without extra developer work. ### Source excerpt At Mercado Libre's scale, with around 20,000 developers and 35,000 microservices, security can't depend on every developer remembering flags, settings, or patterns. That approach simply doesn't scale. To tackle this, over the past few years, we've centralized our defenses within our development frameworks (such as HTTP clients and frontend frameworks) and underlying platform layers (like the HTTP routing layer). This strategy has let us achieve broad protection against common vulnerabilities such as SSRF (Server-Side Request Forgery), XSS (Cross-Site Scripting), and CSRF (Cross-Site Request Forgery). As a result, about 95% of these types of vulnerability reports are now unexploitable, without extra work from developers. In this post, we'll share our strategy, development pipeline, case studies, and key learnings from rolling out these protections. From "shift left" to "paved road"The root of the issue Many developer tools and APIs aren't designed with security in mind. For example, Java's `DocumentBuilderFactory` for XML parsing is, by default, vulnerable to XML External Entity (XXE) attacks. Preventing XXE requires configuring several additional settings: That's quite a lot to remember! Expecting every developer to recall and apply all these configurations at every stage is a heavy burden. On the other hand, Golang's XML package makes XXE vulnerabilities much less likely. You really need to make an effort to be vulnerable to XXE. You can use it without worrying about adding extra settings. These examples highlight the complex reality developers face: Unexpected insecurity: It's really easy to burn yourself without realizing it. Choice overload: On top of development and design choices, you also have to make the right security decisions. Shifting responsibilities: Developers often get blamed for insecure choices, even when these could be addressed by the platform. Additionally, common security practices pose their own challenges: In our complex environment, SAST (St ## AI Agents and Multi-Agent Systems: A Practical Case for Analyzing and Fixing Code Vulnerabilities DevFeed: [AI Agents and Multi-Agent Systems: A Practical Case for Analyzing and Fixing Code Vulnerabilities]() Original publisher: [Read original article]() Author: redmadrobot (red\_mad\_robot) Published: 2025-10-16T15:18:12Z Content type: tutorial Language: ru Sources: [Redmadrobot EN](), [Redmadrobot RU]() Topics: [Artificial Intelligence](), [Natural language processing](), [Large Language Model]() Tags: [agents](), [ai](), [ai-agents](), [graph](), [llm](), [nlp](), [sast](), [systems](), [tag-1605473766c5](), [tag-4d5248fd6bfe](), [workflow]() ### AI overview The article explains the defining characteristics and autonomy levels of AI agents, focusing on workflow agents and multi-agent systems. It presents a practical case involving a multi-agent environment for automatically analyzing and fixing code vulnerabilities. ### Source excerpt Привет! Меня зовут Егор Козлов, я работаю NLP-инженером в red_mad_robot. Мы активно внедряем в бизнес AI-агентов -- автономных и полуавтономных программных сущностей, которые самостоятельно выполняют задачи и принимают решения в интересах бизнеса. В статье расскажу о принципах работы AI-агентов -- с особым вниманием к workflow-агентам и мультиагентным системам (MAS). И поделюсь практическим кейсом внедрения мультиагентной среды для автоматического анализа и исправления уязвимостей в коде. Читать далее ## Snyk Named a Leader in the 2025 Forrester SAST Wave: SAST Solutions, Q3 2025 DevFeed: [Snyk Named a Leader in the 2025 Forrester SAST Wave: SAST Solutions, Q3 2025]() Original publisher: [Read original article]() Author: Ben Desjardins Published: 2025-09-09T04:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [snyk](), [Application Security](), [Testing](), [Securing AI](), [Developer experience](), [Vulnerabilities]() Tags: [2025](), [ai](), [ai-security](), [application-security](), [awareness](), [blog](), [code-security](), [developer-experience](), [executive](), [recognition](), [sast](), [snyk](), [vulnerabilities]() ### AI overview Snyk announces that Forrester recognized it as a Leader in the 2025 Forrester Wave for Static Application Security Testing Solutions. The article attributes this position to Snyk's innovation, AI Security Platform, developer experience, enterprise analytics and visibility, broad Application Security Testing capabilities, and customer impact. It also highlights AI-powered prioritization, autonomous fixes, and the role of Snyk in shift-left security strategies. ### Source excerpt We're excited to announce that Snyk has been recognized as a Leader in the Forrester Wave™: Static Application Security Testing (SAST) Solutions, Q3 2025. ## Building AI Trust with Snyk Code and Snyk Agent Fix DevFeed: [Building AI Trust with Snyk Code and Snyk Agent Fix]() Original publisher: [Read original article]() Author: Liqian Lim (林利蒨); Brendan Hann Published: 2025-06-23T04:00:00Z Content type: news Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [snyk](), [snyk-code](), [ai security](), [Application Security](), [ai-governance](), [Securing AI](), [Pull Request](), [ide]() Tags: [ai](), [ai-coding](), [ai-governance](), [ai-security](), [application-security](), [auto-remediation](), [autonomous](), [blog](), [code-security](), [compliance](), [developer](), [devops](), [engineering](), [pmm](), [pull-requests](), [sast](), [security](), [snyk](), [snyk-code](), [snyk-platform]() ### AI overview Snyk describes its AI Security Platform, Snyk Code, and Snyk Agent Fix for governing AI-assisted development. Snyk Agent Fix autonomously generates and validates code-security fixes, offering auto-remediation in IDEs and pull requests, while Snyk Code provides SAST, visibility, prioritization, and policy controls. ### Source excerpt Secure and accelerate your adoption of AI coding with pre-screened auto-fixes and autonomous code security for modern, developer-loved SAST. ## Snyk Security Solution Now Integrated into Google Cloud's Gemini Code Assist DevFeed: [Snyk Security Solution Now Integrated into Google Cloud's Gemini Code Assist]() Original publisher: [Read original article]() Author: Liqian Lim (林利蒨) Published: 2025-04-09T04:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [snyk](), [Artificial Intelligence](), [Application Security](), [code security](), [ide](), [AI Development]() Tags: [ai](), [application-security](), [awareness](), [blog](), [code-security](), [executive](), [gemini](), [google](), [pmm](), [related-content](), [sast](), [snyk](), [snyk-apprisk](), [snyk-code]() ### AI overview Snyk's security solution is integrated into Google Cloud's Gemini Code Assist, enabling developers to use Snyk security capabilities through natural-language prompts and the coding assistant's chat interface. ### Source excerpt Secure AI coding with Snyk and Google Gemini. Learn how Snyk Code's SAST integrates with Gemini Code Assist for seamless, secure development workflows. ## Сравнение AI-систем для глубокого анализа данных и исследований DevFeed: [Сравнение AI-систем для глубокого анализа данных и исследований]() Original publisher: [Read original article]() Author: kekslop (red\_mad\_robot) Published: 2025-03-18T12:40:03Z Content type: comparison Language: ru Sources: [Redmadrobot EN](), [Redmadrobot RU]() Topics: [Artificial Intelligence](), [Large Language Model](), [Retrieval Augmented Generation (RAG)](), [Langgraph](), [vllm](), [OpenAI](), [Claude]() Tags: [ai](), [ai-6560afdd46fd](), [deepresearch](), [langgraph](), [llm](), [openai](), [rag](), [research](), [sast](), [structured-output](), [tag-1cd610c0e518](), [tag-5e27d5297aca](), [tag-8e9a901cca08](), [tag-baf5012a40ff](), [vllm]() ### AI overview The article compares OpenAI, Grok, Perplexity, and the author's NDT Deep Research system on five analytical tasks, including financial-market analysis, technology research, and identifying promising AI startups. NDT Deep Research uses LangGraph, Tavily, multiple language models, Structured Output, vLLM, retrieval-augmented generation, and source analysis. The author reports that system changes reduced generation costs threefold and latency by 40%. ### Source excerpt Как я сравнил топовые AI-модели для глубокого анализа данных и собственную разработку Привет! Меня зовут Валера Ковальский, я CEO NDT by red_mad_robot. Недавно я протестировал ведущие AI-системы, которые способны проводить глубокие исследования, и делюсь с вами результатами. Читать далее ## Understanding the EU's Cyber Resilience Act (CRA) DevFeed: [Understanding the EU's Cyber Resilience Act (CRA)]() Original publisher: [Read original article]() Author: Ben Desjardins Published: 2025-01-22T05:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [cyber resilience act](), [supply-chain-security](), [Security](), [sdlc](), [Application Security](), [snyk]() Tags: [awareness](), [blog](), [compliance](), [cyber-resilience-act](), [eu](), [executive](), [megawatt](), [sast](), [sbom](), [sca](), [sdlc](), [secure-by-design](), [security](), [snyk](), [software-supply-chain-security](), [supply-chain-security]() ### AI overview The article explains how the EU Cyber Resilience Act (CRA) establishes cybersecurity requirements for products with digital elements. It discusses secure-by-design practices, vulnerability handling throughout the product lifecycle, upcoming reporting and compliance deadlines, and the role of SAST, SCA, and software supply chain security. ### Source excerpt Find out how the Cyber Resilience Act (CRA) sets new security standards for the EU and how Snyk can help simplify compliance with its developer-friendly tools. ## Top 5 SAST Auto-fixing Tools and How They Compare DevFeed: [Top 5 SAST Auto-fixing Tools and How They Compare]() Original publisher: [Read original article]() Author: Liqian Lim (林利蒨) Published: 2024-10-29T15:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Vulnerabilities](), [snyk-code](), [AI Development](), [Security](), [Development](), [Large Language Model]() Tags: [ai](), [awareness](), [blog](), [code](), [development](), [llms](), [pmm](), [sast](), [security](), [security-tools](), [self-hosted](), [snyk](), [snyk-code](), [vulnerabilities](), [vulnerability-detection]() ### AI overview This article examines five SAST auto-fixing tools and explains how AI-powered remediation can reduce the time developers spend fixing vulnerable code. It highlights Snyk Agent Fix, a self-hosted language model trained for vulnerability remediation and integrated with Snyk Code. ### Source excerpt Speed Up Code Remediation with AI-Powered Tools. Learn about the top 5 SAST auto-fixing tools and their features to streamline your development workflow. Discover how Snyk Agent Fix can slash your remediation time by 84% or more. ## A security expert's view on Gartner's generative AI insights - Part 2 DevFeed: [A security expert's view on Gartner's generative AI insights - Part 2]() Original publisher: [Read original article]() Author: Liqian Lim (林利蒨) Published: 2024-08-08T05:00:00Z Content type: opinion Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Security](), [Application Security](), [snyk-code](), [snyk](), [Generative AI](), [LLM security](), [Code quality](), [Code]() Tags: [ai](), [application-security](), [awareness](), [blog](), [code-quality](), [devops](), [executive](), [interest](), [pmm](), [sast](), [security](), [snyk](), [snyk-code](), [speed]() ### AI overview This opinion article discusses Gartner's recommendations for specialized generative AI models in cybersecurity. Snyk argues that its security-focused model, used in Snyk Agent Fix and Snyk Code SAST, produces more reliable code fixes because it is trained specifically for security rather than general code functionality. ### Source excerpt A recent Gartner report shows that AI transparency is a common issue in many organizations. Snyk Code's hybrid AI-powered SAST is the secret ingredient for AI security & coding. ## Three reasons to invest in an ASPM solution in 2024 DevFeed: [Three reasons to invest in an ASPM solution in 2024]() Original publisher: [Read original article]() Author: Julia Kraut Published: 2023-12-12T12:55:00Z Content type: opinion Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Application Security](), [snyk-apprisk](), [snyk](), [Security](), [snyk-platform](), [snyk-code](), [snyk-container](), [snyk-iac](), [snyk-open-source]() Tags: [acquisition](), [application-security](), [aspm](), [blog](), [executive](), [pmm](), [sast](), [sca](), [security](), [snyk](), [snyk-apprisk](), [snyk-container](), [snyk-iac](), [snyk-open-source](), [snyk-platform]() ### AI overview This guide presents three reasons to invest in an application security posture management solution: consolidating security vendors, improving application security tool coverage, and managing security and compliance controls. It specifically promotes Snyk AppRisk and its integration with the Snyk platform. ### Source excerpt Learn three compelling reasons to invest in an application security posture management (ASPM) solution like Snyk AppRisk. ## Improving product reliability by imposing constraints as a part of CI/CD process DevFeed: [Improving product reliability by imposing constraints as a part of CI/CD process]() Original publisher: [Read original article]() Author: Pedro Moura Published: 2023-11-03T14:26:59Z Content type: article Language: en Sources: [Revolut Engineering]() Topics: [CI/CD](), [Application Security](), [Vulnerabilities](), [Data analysis]() Tags: [application-security](), [blocking](), [ci-cd](), [data-analysis](), [devsecops](), [engineering](), [sast](), [sca](), [scanners](), [security](), [security-vulnerabilities](), [software-development]() ### AI overview Revolut describes a CI/CD approach that uses automated risk calculation, data analysis, security scanning, and deployment constraints to improve product reliability. The approach aims to identify vulnerabilities and bugs early, mitigate them, and potentially block high-risk applications from reaching production. ### Source excerpt At Revolut, we always aim to consistently provide efficient, high-quality, and secure services. Our primary goals are to enhance product quality, increase customer satisfaction, and reduce business risk. To achieve those goals, we should aim to be as proactive -- rather than reactive -- as possible. This means fixing any point of failure before it reaches production by identifying, reporting, and potentially blocking deployments that can impact the reliability of our products. In this article, we'll explain how we achieve those goals using automated risk calculation, data analysis, and imposing constraints to push for the mitigation of open vulnerabilities and the fix of reported bugs identified in our products. Challenges faced by Revolut Each application/service has its own specifications, with different technology stacks and architectures, bringing a diversity of security challenges and different vulnerabilities. That being said, application risk cannot be based only on the reported security vulnerabilities but also in the context of that specific application. Continuous scanning, reporting, visibility, and risk evaluation are essential to providing the best security advice and automated security controls. Nowadays there are several types of security scanners (SAST, SCA, DAST, IaC, etc.) that help security professionals to identify and report vulnerabilities, where each of them can belong to a different third-party provider. For further information on these, read our article on continuous security. Data scraped from different sources creates friction when it's needed to group findings and provide mitigations. This can also decrease mean time to identify vulnerabilities and mean time to provide mitigations. Also, to avoid such negative workflow impact, security must be shifted left. Without a centralised source of truth for security-related application data, most of the time is wasted on searching for information/findings in segmented platforms and finding the corre ## Snyk is named a Strong Performer as a first-time entrant in the Forrester Wave™: Static Application Security Testing (SAST) Q3 2023 DevFeed: [Snyk is named a Strong Performer as a first-time entrant in the Forrester Wave™: Static Application Security Testing (SAST) Q3 2023]() Original publisher: [Read original article]() Author: Peter McKay Published: 2023-09-20T12:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Application Security](), [snyk](), [DevSecOps](), [Testing](), [AI-assisted coding]() Tags: [ai](), [application-security](), [aspm](), [awareness](), [blog](), [code-security](), [devops](), [devsecops](), [enablement](), [executive](), [interest](), [pmm](), [sast](), [security](), [snyk](), [snyk-platform]() ### AI overview Snyk says it was recognized as a Strong Performer in the Forrester Wave: Static Application Security Testing (SAST) Q3 2023, its first year participating. The article highlights Snyk's developer-first application security approach, DevSecOps practices, developer experience, actionable results, remediation guidance, integrations, and IDE plugins. It also describes how the growing use of AI code generation increases development speed and security challenges. ### Source excerpt We're thrilled that Snyk has been recognized as a Strong Performer in our first year participating in the Forrester Wave™: Static Application Security Testing (SAST) Q3 2023.