Apiiro AI-SAST: Static Scanning Reimagined – From Code to Runtime – for the AI Era

Application security and development teams have relied on SAST scanners that excel at recognizing patterns based on static rules, but struggle to deeply understand the software architecture graph from code to runtime. 

Now, the supercharged speed of development – powered by AI-assisted coding – has made the SAST results unsustainable. Traditional scanners lack the ability to give application security teams the context they need to triage vulnerabilities, understanding the risk of each vulnerability and completely missing important business logic vulnerabilities that fall outside their legacy technology to detect. 

It’s time to scale the context and reasoning of a human application security engineer up to AI-speed, reimagine static analysis, and transform how we think about code risk.

That’s why we’re shifting SAST from a mere detection problem to a risk validation problem – and fixing it.

Legacy SAST Scanners Were Built for the Pre-AI Age. It’s Time for a Change.

As development velocity has increased by 4× and application risk by 10×, application security has shifted from a practitioner and developer-level concern to a C-level business priority, dramatically raising the demands on modern Application Security Posture Management (ASPM) platforms.

Research shows that up to 50% of security issues are introduced at the code level, making SAST essential to securing the software development lifecycle (SDLC). The earlier a true positive vulnerability with risk to the business is detected and fixed, the more organizations reduce both operational costs and business risk – the only viable approach to application security in the AI-driven development era.

However, traditional SAST has been notoriously noisy for years, and this problem has become far more severe with the rise of AI-generated code.

Legacy SAST relies on rigid pattern matching and static rules to detect vulnerabilities. But the sheer volume and speed of modern code delivery mean these scanners now generate millions of findings in large enterprises – without the ability to determine which ones are true-positive and represent real, exploitable risk.

This results in several systemic failures:

• Endless Noise – Application Security teams drown in alerts that don’t materially impact the business, while developers lose trust in the system.
• Missed Business Logic Flaws – Complex, multi-step flows – such as authorization bypasses or improper access to sensitive data – typically uncovered in penetration tests often evade traditional SAST entirely.
• No Understanding of Runtime Exposure – SAST cannot determine whether vulnerable code is actually deployed and reachable from the internet, or already mitigated by existing security controls.
• No Context for Remediation – Even when a finding is true-positive, most tools lack the context to explain where the issue originates, how it propagates through the software architecture graph, or how to fix it safely without introducing regressions.

Traditional SAST is a hammer that treats every vulnerability as a nail. Human expertise is required to audit, validate, and triage its output – but humans-in-the-loop cannot realistically review millions of findings sprawling across today’s AI-generated code sprawling thousands of codebases.

That’s why AI-SAST is necessary: to combine the scale and speed of automated detection, triage and fixes – with the reasoning and judgment of an expert application security engineer.

C.H. Robinson 🤝 Apiiro AI-SAST

“Apiiro’s AI-SAST, powered by Deep Code Analysis (DCA), reduced false positives by over 85% within weeks. By mapping SAST risks to internet-facing API entry points, we can confidently prioritize real exploited risks and help our developers increase development velocity.”

Paddle 🤝 Apiiro AI-SAST

“Deep Code Analysis (DCA) dramatically reduced false positives in our environment within weeks. By mapping SAST findings to API entry points, we can better prioritise the risks that matter most.”

Application Security leaders at C.H. Robinson and Paddle understand the need for strong SAST is an integral part of building a strong application security posture management. But the sea of alerts generated by legacy static analysis tools leave them struggling to prioritize and validate risk. 

That’s where Apiiro came in – to drastically reduce false positives, and streamline prioritization, risk validation and fixes.

How Apiiro AI-SAST reinvented static code analysis

Traditional SAST was built to detect vulnerabilities, not to validate risk or drive outcomes. In an AI-driven development world – where code velocity has exploded, application complexity and attack surface grows daily – this approach no longer works. 

Application Security leaders don’t need more findings; they need clarity on what matters, why it matters, and how to fix it safely at scale.

Apiiro AI-SAST fundamentally reinvents static code analysis by shifting the focus from detection to risk validation and remediation, using the same reasoning process an expert application security engineer would apply – but automated, continuous, at enterprise-scale.

From Scanning Code to Understanding Risk

Apiiro AI-SAST is built on a simple principle: AI is only effective when it has the right map.

Instead of analyzing isolated lines of code / files, Apiiro gives AI a complete, continuously updated understanding of your software graph – across code modules, repositories, and runtime.

This allows Apiiro AI-SAST to answer the questions CISOs and CTOs actually care about:

• Is this a true-positive vulnerability?
• Is it reachable in production?
• Does it impact critical business applications?
• What is the safest and most effective way to fix it?

How It Works: Risk Validation, Not Just Detection

Apiiro combines 5 capabilities into the AI-SAST:

1. AST + AI/Agent: Code Trees Meet Human-Level Reasoning

Traditional AST provides the structure to Apiiro’s specialized AI Agent, which performs:

• Semantic reasoning
• Control-flow, data-flow analysis
• Business-logic interpretation
• Specific security issue identification

2. Deep Code Analysis (DCA): The Software Graph – Map to AI

Apiiro grounds AI-SAST in a comprehensive, continuously-updated Software Graph for every commit and material changes – powered by Deep Code Analysis (DCA) patented technology.

The software graph models every code resource and the relationship between them to represent the entire software architecture from code to runtime.

This enables Apiiro AI-SAST to answer the question no SAST tool has ever answered reliably:

“Is this vulnerability actually reachable?”

3. Code-to-Runtime Matching (C2R): Real-World Reachability and Exploitability, Not Theoretical Risk

Apiiro maps vulnerabilities to the actual runtime environment, automatically correlating code resources with:

• Deployed containers
• API gateways
• Public API endpoints
• Internet exposure
• Traffic patterns
• Active security controls

📈The Risk Graph: Your Contextual Engine

Apiiro’s Risk Graph builds a multi-dimensional map connecting your code, runtime environment, developers, organizational policies and business impact. 

4. AI-SAST That Knows How and Where to Fix

Using the Software Graph, Apiiro AI-SAST:

• Root cause analysis: Identifies the most primary source of the vulnerability
• Smart location: Identifies the best location for a fix
• Contextual fixes: Avoids breaking changes or redundancy

This drastically reduces developer effort, eliminates patch sprawl and reduces risk to the business.

5. Adaptive Feedback: AI-SAST That Learns Your Environment

Every organization and repository is unique. Apiiro AI-SAST evolves with you through:

• Foundation tuning: Security teams can align AI-SAST with organizational coding standards and risk tolerance from day one, reducing noise and ensuring findings are relevant to the business.
• Algorithmic feedback: When teams validate or dismiss findings, Apiiro learns from those decisions – continuously refining accuracy so the system reflects your logic, not generic rules.

This creates a compounding accuracy effect: Your AI-SAST becomes more accurate, less noisy, and more aligned with your codebase over time.

Summary: AI-SAST for Large Enterprises

Developer capabilities have evolved dramatically – but their core security needs have not.

AI-assisted coding is making software teams faster and more productive at enterprise scale. At the same time, it has exposed the growing attack surface through adding more code, and limitations of legacy security technologies like traditional SAST.

Apiiro AI-SAST modernizes one of the most important pillars of application security, reinventing static scanning from a contextless, noisy tool into an expert-level engine that fixes your risks.

What This Means for Application Security

✔ Eliminate noise – Apiiro removes false positives through deep software architectural understanding – not static rules or manual triage.

✔ Focus on the risks that truly matter – Detect real, exploitable vulnerabilities, including business-logic flaws and accidental exposure of sensitive data.

✔ Reduce AppSec backlog and MTTR – Validated findings combined with contextual fixes dramatically accelerate remediation.

✔ Accelerate software development and delivery – While reducing operational costs and risk.

✔ Move beyond legacy SAST – Traditional scanners cannot adapt to AI-driven development velocity. Apiiro AI-SAST is built for it.

👉 Stop scanning. Start fixing risks. See Apiiro AI-SAST in action.