The application security tool you pick can make or break the cooperation between your application development team and your security team.
Low development velocity, tool abandonment, and other AppSec headaches frequently put deadlines at risk; worse, they create real vulnerabilities, especially as teams focus on driving down high false-positive rates.
AI is changing the AppSec marketplace and complicating the process of finding the best tool, but it also promises to bridge the gap between your engineering and security teams.
Find out how AI is changing AppSec, then read our top nine criteria for choosing an AppSec tool — four cautions, four go-aheads, and one must-have. These tips will help you find the best investment for your team.
How AI Improves AppSec
While AppSec is constantly evolving, one thing doesn’t change: the need for reliable, developer-friendly tooling. Together, traditional and AI tools deliver that capability.
Can SAST Tools Keep Up?
Traditional static application security testing (SAST) has long been the backbone of secure development practices. Tools like Semgrep, developed over years of hands-on work, are precise, rules-driven, and based on well-understood vulnerability patterns. There’s no reliable replacement for these battle-tested assets.
But legacy SAST also has some limitations:
Processing code can be slower: SAST tools analyze every line of code, slowing down CI/CD pipelines if they’re not optimized for efficiency.
The results are noisy: false positives consume time and energy, create friction between teams, frustrate developers, and lead to burnout for security teams.
Static tools lack flexibility: SAST only works on known vulnerabilities, so adapting to new threats means writing new rules.
Semgrep prioritizes addressing those concerns, but many teams ask themselves — do AI tools offer a better alternative?
Beware the Shiny Promise of AI
AI enthusiasts make bold claims about changing the AppSec landscape — AI can magically eliminate false positives, write perfect rules, or instantly detect new vulnerability classes. The truth is more nuanced.
While AI excels at pattern recognition, code summarization, and accelerating tedious manual processes, it can’t fully replace the approach that makes SAST reliable. It’s vulnerable to its own unique security issues, and can’t consistently identify some common vulnerabilities, let alone reveal new ones.
Combine AI and SAST for Success
The space between AI marketing claims and traditional SAST holds significant potential. The best AppSec innovations come from weaving these two approaches together.
AI can analyze code behavior, provide context-aware guidance, and triage complex findings. SAST can enforce deterministic checks, revealing critical known vulnerabilities that AI alone will miss. Combined, they create a more powerful and usable system than either approach alone.
As you evaluate AppSec tools, look for solutions that let you leverage this blended future. Tools should enable AI to fill SAST’s gaps without requiring developers to adopt unproven, AI-only workflows. The best investment you can make right now is a plan that builds on a mature SAST foundation with AI layered in as a force multiplier.
The nine tips below show you what that might look like in practice.
9 Factors to Pick the Right AppSec Tool
Choose an AppSec platform or tool that empowers both your development and security teams. As you compare options, keep an eye out for warning signs and positive indicators. Finally, make sure the tool meets the one crucial requirement at the bottom of the list.
⚠️ Proceed with Caution Around Yellow Flags
AI-Only or SAST-Only Approaches
Tools that rely entirely on AI or solely use legacy SAST leave you exposed. AI-only may miss fundamental security flaws, while SAST-only may struggle with the complexity of current AppSec.
Hollow Promises About False Positives
Guaranteeing “zero false positives” can oversimplify the complexity of a solution and undersell the risk of over-optimizing. Zero is a good aspiration, but there is no magical fix if it leads you to prioritize incorrectly and miss real critical vulnerabilities.
What matters is whether the tool helps you manage false positives intelligently through prioritization, reachability analysis, and feedback loops while still showing a complete picture of the security issues. AI solutions can have a short memory.
Limited Customization Options
Every codebase is unique, and every company has to satisfy different security requirements from customers, partners, and regulators. Developers have to consider functionality and performance in addition to security and privacy.
If a tool can’t adapt to your specific context, or doesn’t allow you to customize rules or noise levels, you’ll spend more time wading through irrelevant reports than fixing vulnerabilities.
Lack of Transparency
No one trusts a black box. If a tool doesn’t explain why it flagged something or how its AI models reach conclusions, your developers won’t accept its feedback, and your security team won’t be able to validate the results. Transparency fosters trust in the tool and between your teams.
✅ Move Forward with Four Green Flags
AI and SAST Enhance Each Other
The best tools combine SAST’s deterministic analysis with AI-driven context. In an ideal pairing, the two technologies drive a three-step process:
Prior: Before SAST analysis, AI identifies design-level problems and applies general clean code principles. It can also customize SAST rules to codebase needs.
During: SAST rules optimize code, reducing noise.
After: Post processing, AI guides developers in understanding and prioritizing findings.
The result is a reliable report on what needs fixing and why, which helps your teams communicate and collaborate better.
Lower Friction Through Reachability Analysis
Trustworthy reachability analysis is the holy grail for security teams suffering from alert fatigue. There’s no sense in devoting resources to fix a problem in code that will never run, but it happens all the time with SAST.
AI integrations can do a much better job of mapping dependencies and tracking which functions are actually executed. When tools understand real execution paths, they can highlight meaningful vulnerabilities and suppress theoretical ones.
Multiple Language Coverage
Modern software ecosystems are multilingual. A strong AppSec tool should support your stack end-to-end — not just the “easy” languages.
A single tool that covers your entire codebase makes security more consistent and effective. Take a careful inventory of the languages you use across your organization to ensure the tool meets the needs of all your developers.
Robust Benchmarks and Realistic Tests
Marketers love benchmarks, but they can be misleading — tools often get optimized for those targets, leaving real-world problems aside.
Look for tools with a real-world track record of performing well across a range of benchmarks. The best ones may not have the highest marks on any single metric, but can back up the numbers with multiple examples.
Be skeptical of AI demos and performance claims. Vendor-controlled demos can manipulate results in many ways. A model trained on a narrow dataset can perform extremely well, but that won’t necessarily translate into a reliable analysis of your code. Even a demo of a high-quality tool won’t reflect what happens inside your CI/CD pipeline.
🌟 One Must-Have: Time-Tested Security Expertise
AI-driven AppSec is a big step forward, but security tools are not the place to gamble on untested strategies or radical visions.
An expert-built tool can help you reduce the risks inherent to innovation. Invest in a vendor with a long track record in secure code analysis, including battle-tested SAST. Veteran security professionals understand the shortcomings of SAST and the risk of over-relying on AI. Their expertise can help shape every part of an AppSec tool, from training data to rulesets.
The counterpoint to specialized, expert-developed security solutions is one-size-fits-all, all-in-one tools that handle all your security needs. But experts know that no single strategy can cover all risks, and that no single tool can cover all security functions. A general-purpose solution may be easy to set up, but it will deliver inconsistent, untrustworthy results.
Can Trying A New AppSec Approach Be Painless?
If the prospect of adopting new security tools feels daunting, you’re in good company. Changing up a major part of your development process is never an easy sell, no matter how much faith you have in the products.
Fortunately, another AI-era technology is making it a bit easier: MCP servers are a bit like an API for an LLM — a simple natural language interface that lets developers interact with a complex resource in predictable ways. Or think about it as an SDK for developers whose preferred language is plain, conversational exchanges.
MCP servers coordinate between SAST tools and AI AppSec to let developers request security reviews and get feedback in a simple, readable form. No special security knowledge or command memorization required. It makes the highest-quality AppSec available to everyone in your organization, including developers who are vibe-coding their first feature demo.
When high-quality security reviews are accessible, your whole organization can shift left on security. Rather than worrying about catching everything in a final code review, developers can iterate and adapt as they go, fixing small problems before they become bigger ones.
If you’re ready to see what we mean, try out the Semgrep MCP server on some of your code now. It’s ready to review code in 30+ programming languages, and draws on a Semgrep ruleset of over 5,000 rules.
Be sure the tools you choose deliver that expertise and usability so you can get the full value of your AI AppSec investment.
