AI-Native SAST that
understands your code
Corgea finds and fixes insecure code using LLMs at scale.
Detect the undetected
Find difficult to find business logic flaws, broken authentication, malicious code vulnerabilities usually found in pentests and bug bounty reports
Business & Logic Flaws
Detect complex vulnerabilities like business logic errors and misconfigurations with AI that understands your code’s unique context.
Broken Authentication
Find and fix authentication gaps that attackers exploit, ensuring your systems stay secure and reliable.
OWASP Top 10
Revolutionize static analysis with AI-driven precision, uncovering vulnerabilities others miss while reducing false positives.
A new breed in security
Corgea leverages AI and static analysis to change how security scanner should work.
Rewriting the
Rules of Scanning
Eliminate the complexity of manual configurations and outdated rulesets. Corgea's AI dynamically adapts to your environment, learning and evolving with every scan to uncover vulnerabilities others miss—all without the noise.
Deeper
Analysis
Go beyond traditional scanning with AI-powered insights that detect complex vulnerabilities like business logic flaws, missing authentication, and misconfigured security controls. Corgea doesn’t just scan your code—it understands it.
Reduce the
Noise
Corgea automatically reduces around 30% of tickets by triaging false positives.
We speak your language
Corgea supports with Java, JavaScript, TypeScript, Go, Ruby, Python, C#, C, C++, Kotlin, PHP, and their frameworks.
How it works
How BLAST Works
The
White Paper
We've put together a complete technical white paper that showcases how BLAST works.
Testimonal
When launching our new site, I ran about every static analyzer out there - Corgea came back with the most meaningful results, and it wasn't even close.
James Berthoty
Analyst @ Latio Tech
What does “AI-native SAST” mean?
AI-native SAST refers to static application security testing tools that are built from the ground up with artificial intelligence. Unlike traditional SAST (which relies on manual rules and pattern matching), AI-native SAST uses machine learning models – especially large language models – to understand code context and logic. This allows it to detect more complex security vulnerabilities and reduce false positives by analyzing code more like a human security expert. In short, AI-native SAST is a new generation of code scanners that “think” about code, rather than just applying regex rules.
How is AI-based SAST different from traditional SAST tools?
The main differences are in what they can find and how they report results. Traditional SAST scans for known patterns (e.g., dangerous function calls) and often produces many alerts, including lots of false positives, because it lacks context. AI-based SAST leverages AI to grasp the intent of code and the surrounding context, so it can catch things like business logic flaws that traditional tools miss. It also filters out benign cases that a simple pattern matcher would flag, dramatically cutting down noise. Additionally, AI SAST tools typically provide more detailed explanations and even fix suggestions, whereas legacy tools just point out the code and leave the rest to you. AI-native SAST is essentially more intelligent and developer-friendly in its analysis compared to classic static analyzers.
Can AI-native SAST completely eliminate false positives?
Not completely, but it can significantly reduce them. No automated tool will have zero false positives because determining exploitability can be tricky and sometimes context (like intended business behavior) might be unclear to the AI. However, AI-native SAST has shown the ability to cut false positives by a large margin – reports suggest reductions on the order of 30% up to 85% fewer alerts compared to legacy SAST. The AI’s understanding of context means it’s much less likely to flag something that isn’t truly a problem. You should still review findings, but you’ll have far fewer “noise” items to wade through. Over time, as the AI learns from your feedback, false positives can drop further, since the system adapts to your codebase.
What new types of vulnerabilities can AI-native SAST detect?
AI-native SAST can detect business logic and context-specific flaws that traditional tools often miss. Examples include Broken Access Control issues (like IDOR and authorization logic gaps), logic bombs or misuse of application features, mass assignment vulnerabilities (as in creating an admin account by adding isAdmin=true in a JSON payload), and complex injection scenarios that involve multiple steps or files (like an issue spanning a GraphQL resolver and a REST endpoint). AI can also adapt to emerging threats; for instance, some AI SAST tools are already catching things like prompt injections and insecure usage of AI APIs in code – issues that didn’t even exist a couple of years ago. Essentially, if a vulnerability’s signature isn’t a simple pattern but rather a logical oversight, an AI-based approach has a much better shot at finding it.
Are AI-driven code scanning tools reliable for enterprise use?
They’re rapidly getting there. Many enterprises are piloting or adopting AI-native SAST tools, especially to complement existing security testing. Early feedback is very positive about finding issues that were previously overlooked. That said, since this tech is relatively new, enterprises often start with a phased approach: run the AI scanner in audit mode alongside existing tools, validate its findings over a few months, and gradually increase trust. It’s also important to vet the vendor’s security (make sure they can handle your source code securely) and possibly opt for an on-premise version if required. Some AI SAST providers have achieved SOC 2 compliance and have notable customers, indicating a level of maturity in their operations. As with any critical tool, due diligence is needed – but the technology itself has proven effective, and with the proper safeguards and evaluation, it can be reliably used in an enterprise environment. Many organizations see the reduction in false positives and time savings as worth the leap, especially given the persistent shortage of AppSec experts.
What does “AI-native SAST” mean?
AI-native SAST refers to static application security testing tools that are built from the ground up with artificial intelligence. Unlike traditional SAST (which relies on manual rules and pattern matching), AI-native SAST uses machine learning models – especially large language models – to understand code context and logic. This allows it to detect more complex security vulnerabilities and reduce false positives by analyzing code more like a human security expert. In short, AI-native SAST is a new generation of code scanners that “think” about code, rather than just applying regex rules.
How is AI-based SAST different from traditional SAST tools?
The main differences are in what they can find and how they report results. Traditional SAST scans for known patterns (e.g., dangerous function calls) and often produces many alerts, including lots of false positives, because it lacks context. AI-based SAST leverages AI to grasp the intent of code and the surrounding context, so it can catch things like business logic flaws that traditional tools miss. It also filters out benign cases that a simple pattern matcher would flag, dramatically cutting down noise. Additionally, AI SAST tools typically provide more detailed explanations and even fix suggestions, whereas legacy tools just point out the code and leave the rest to you. AI-native SAST is essentially more intelligent and developer-friendly in its analysis compared to classic static analyzers.
Can AI-native SAST completely eliminate false positives?
Not completely, but it can significantly reduce them. No automated tool will have zero false positives because determining exploitability can be tricky and sometimes context (like intended business behavior) might be unclear to the AI. However, AI-native SAST has shown the ability to cut false positives by a large margin – reports suggest reductions on the order of 30% up to 85% fewer alerts compared to legacy SAST. The AI’s understanding of context means it’s much less likely to flag something that isn’t truly a problem. You should still review findings, but you’ll have far fewer “noise” items to wade through. Over time, as the AI learns from your feedback, false positives can drop further, since the system adapts to your codebase.
What new types of vulnerabilities can AI-native SAST detect?
AI-native SAST can detect business logic and context-specific flaws that traditional tools often miss. Examples include Broken Access Control issues (like IDOR and authorization logic gaps), logic bombs or misuse of application features, mass assignment vulnerabilities (as in creating an admin account by adding isAdmin=true in a JSON payload), and complex injection scenarios that involve multiple steps or files (like an issue spanning a GraphQL resolver and a REST endpoint). AI can also adapt to emerging threats; for instance, some AI SAST tools are already catching things like prompt injections and insecure usage of AI APIs in code – issues that didn’t even exist a couple of years ago. Essentially, if a vulnerability’s signature isn’t a simple pattern but rather a logical oversight, an AI-based approach has a much better shot at finding it.
Are AI-driven code scanning tools reliable for enterprise use?
They’re rapidly getting there. Many enterprises are piloting or adopting AI-native SAST tools, especially to complement existing security testing. Early feedback is very positive about finding issues that were previously overlooked. That said, since this tech is relatively new, enterprises often start with a phased approach: run the AI scanner in audit mode alongside existing tools, validate its findings over a few months, and gradually increase trust. It’s also important to vet the vendor’s security (make sure they can handle your source code securely) and possibly opt for an on-premise version if required. Some AI SAST providers have achieved SOC 2 compliance and have notable customers, indicating a level of maturity in their operations. As with any critical tool, due diligence is needed – but the technology itself has proven effective, and with the proper safeguards and evaluation, it can be reliably used in an enterprise environment. Many organizations see the reduction in false positives and time savings as worth the leap, especially given the persistent shortage of AppSec experts.
What does “AI-native SAST” mean?
AI-native SAST refers to static application security testing tools that are built from the ground up with artificial intelligence. Unlike traditional SAST (which relies on manual rules and pattern matching), AI-native SAST uses machine learning models – especially large language models – to understand code context and logic. This allows it to detect more complex security vulnerabilities and reduce false positives by analyzing code more like a human security expert. In short, AI-native SAST is a new generation of code scanners that “think” about code, rather than just applying regex rules.
How is AI-based SAST different from traditional SAST tools?
The main differences are in what they can find and how they report results. Traditional SAST scans for known patterns (e.g., dangerous function calls) and often produces many alerts, including lots of false positives, because it lacks context. AI-based SAST leverages AI to grasp the intent of code and the surrounding context, so it can catch things like business logic flaws that traditional tools miss. It also filters out benign cases that a simple pattern matcher would flag, dramatically cutting down noise. Additionally, AI SAST tools typically provide more detailed explanations and even fix suggestions, whereas legacy tools just point out the code and leave the rest to you. AI-native SAST is essentially more intelligent and developer-friendly in its analysis compared to classic static analyzers.
Can AI-native SAST completely eliminate false positives?
Not completely, but it can significantly reduce them. No automated tool will have zero false positives because determining exploitability can be tricky and sometimes context (like intended business behavior) might be unclear to the AI. However, AI-native SAST has shown the ability to cut false positives by a large margin – reports suggest reductions on the order of 30% up to 85% fewer alerts compared to legacy SAST. The AI’s understanding of context means it’s much less likely to flag something that isn’t truly a problem. You should still review findings, but you’ll have far fewer “noise” items to wade through. Over time, as the AI learns from your feedback, false positives can drop further, since the system adapts to your codebase.
What new types of vulnerabilities can AI-native SAST detect?
AI-native SAST can detect business logic and context-specific flaws that traditional tools often miss. Examples include Broken Access Control issues (like IDOR and authorization logic gaps), logic bombs or misuse of application features, mass assignment vulnerabilities (as in creating an admin account by adding isAdmin=true in a JSON payload), and complex injection scenarios that involve multiple steps or files (like an issue spanning a GraphQL resolver and a REST endpoint). AI can also adapt to emerging threats; for instance, some AI SAST tools are already catching things like prompt injections and insecure usage of AI APIs in code – issues that didn’t even exist a couple of years ago. Essentially, if a vulnerability’s signature isn’t a simple pattern but rather a logical oversight, an AI-based approach has a much better shot at finding it.
Are AI-driven code scanning tools reliable for enterprise use?
They’re rapidly getting there. Many enterprises are piloting or adopting AI-native SAST tools, especially to complement existing security testing. Early feedback is very positive about finding issues that were previously overlooked. That said, since this tech is relatively new, enterprises often start with a phased approach: run the AI scanner in audit mode alongside existing tools, validate its findings over a few months, and gradually increase trust. It’s also important to vet the vendor’s security (make sure they can handle your source code securely) and possibly opt for an on-premise version if required. Some AI SAST providers have achieved SOC 2 compliance and have notable customers, indicating a level of maturity in their operations. As with any critical tool, due diligence is needed – but the technology itself has proven effective, and with the proper safeguards and evaluation, it can be reliably used in an enterprise environment. Many organizations see the reduction in false positives and time savings as worth the leap, especially given the persistent shortage of AppSec experts.
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