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For AppSec teams, the goal is simple but difficult: scale security without breaking the CI/CD pipeline. As application vulnerabilities remain a primary cause of breaches, traditional security models, built on decades-old scanning technology, are now a liability. These legacy scanners are context-blind, generating a flood of false positives that force teams into manual triage instead of strategic risk management.
False positives aren't just a technical annoyance; they are a drain on expensive resources. Research indicates that organizations face false positive rates ranging from 71% to over 90%.1
This "triage tax" creates uncertainty between security and engineering. When AppSec teams pass down inaccurate findings, it forces developers to waste cycles on non-exploitable code, causing misalignment that leads to bypassed security gates and increased enterprise risk. To regain credibility and velocity, AppSec must shift from volume-based reporting to high-fidelity, actionable intelligence.
The gap between the demand for faster software and the limitations of legacy tools is widening. Modern DevOps environments move too fast for the slow, manual review cycles required by traditional scanners.
This approach allows AppSec managers to:
By replacing noise with runtime context, you don't just secure the application — you accelerate the business.
The manual verification burden
False positives are more than just a nuisance; they can create uncertainty. AppSec engineers and penetration testers are forced to manually validate every flagged vulnerability, diverting their expertise away from high-value threat modeling and proactive security architecture.
Velocity vs. visibility
As organizations shift toward rapid-release cycles with the help of AI-generated code and microservices, the sheer volume of code being pushed exacerbates the noise. When development speeds increase, the "noise floor" of traditional scanning tools rises alongside it, leading to:
Developer friction: Bombarding engineering teams with inaccurate tickets erodes trust in security tooling.
Operational fatigue: Incident response and AppSec teams become desensitized to alerts, increasing the risk that a critical, "true positive" exploit is overlooked.
Application security (AppSec) specialists have historically leaned on Static Application Security Testing (SAST) and Dynamic Application Security Testing (DAST), often attempting to bridge the gaps through periodic manual penetration testing. The fundamental flaw in these instruments is in the high-velocity world of AI-based development, these legacy scanning solutions fail to scale because they cannot observe the application as it behaves during execution. Because these tools lack runtime application security capabilities, they struggle to verify "reachability" and the confirmation of whether a piece of code is exploitable in a live environment. This forces security, QA and incident response teams into grueling manual verification cycles to determine actual risk.
To compensate for these inherent deficiencies, organizations frequently resort to a “tool-swamp” strategy where teams are layering disparate, outdated security approaches in the hope that a combined effort will yield a secure outcome. This hybrid attempt at vulnerability detection is fundamentally broken. Even with advancements in modern scanning, legacy techniques are often incapable of keeping pace with the velocity of modern CI/CD pipelines.
This manual diagnosis and remediation cycle creates a massive bottleneck in the development release flow. Developers find themselves exasperated, wasting valuable cycles on findings that lack execution context. When measured against rapid AI-generated code and aggressive release deadlines, the friction caused by code commit and release delays becomes untenable. The result is a dangerous culture of alert fatigue, where developers eventually ignore security notifications resulting in them potentially overlooking a critical, reachable vulnerability that poses a genuine business risk simply because the tool could not verify it was "active."
The reliance on these outdated scanning categories also introduces systemic risk, as they have failed to deliver on their original security promises. More damagingly, the friction caused by these tools creates internal business tension and often cultivates a false sense of security. These tools lack the deep application visibility and runtime context required to produce actionable, accurate results. Whether it is SAST or DAST, these legacy systems remain unnecessarily complex and expert-dependent, making it impossible to scale security across the modern enterprise.
While the latest SAST tools have become more accurate through sophisticated data-flow modeling and AI-driven triaging, they still face a fundamental architectural hurdle: they do not execute the code. Without runtime analysis, even advanced static tools are predicting potential paths. They can identify a flaw in the source code, but they cannot definitively prove that the flaw is reachable by an attacker, given the specific configuration of the production server or the state of the database.
This lack of execution context results in findings that are often time-consuming to diagnose and trace. True security efficiency requires more than just high-fidelity pattern matching; it requires the ability to pinpoint the actual vulnerable lines of code within a running state. Without this "ground truth," teams are frequently left debating theoretical issues rather than remediating exploitable truths.
DAST operates from the outside-in, treating the application as an opaque black box. While DAST is effective at identifying certain runtime issues because it tests a functioning application, it lacks any internal visibility into the code itself. When DAST uncovers a vulnerability, it cannot provide the vulnerability insight needed to fix it; it simply knows the "front door" is open but has no idea which specific line of code or backend process left it unlocked.
Teams are forced to spend hours hunting down the root causes of DAST findings, manually correlating external reports with internal source code. Furthermore, DAST struggles significantly with modern APIs and custom protocols, as it lacks the internal hooks to understand non-standard data structures. This lack of runtime application context means that even when a vulnerability is "found," the path to remediation is manual and slow, leaving the application exposed while developers search for the needle in the haystack.
AppSec is not only about finding and fixing vulnerabilities in the Software Development Life Cycle (SDLC) but also protecting software in production runtime. Historically, organizations have relied on a perimeter defense approach using network security tools such as Web Application Firewalls (WAFs). WAFs monitor network-level application communications and draw a “best-guess boundary” around an application.
But perimeter defense approaches such as WAFs are plagued with false positives — not to mention false negatives. To begin with, a WAF identifies every vulnerability that is found in its known signature engine, which can be of benefit as a layer of security, but is not enough on its own.
With upwards of 99% of attacks never finding an application vulnerability, this generates a huge number of false positives that security teams must spend valuable time diagnosing.2
Second, WAFs lack the application context to understand what the traffic means and how the data will be used. This results in false positives — and false negatives — and excessive tuning that must be overseen by security specialists
Security teams are often buried under a mountain of data, but the real issue isn't just the volume — it’s the lack of actionable context. When security tools lack deep visibility into the application at runtime, they generate a high volume of noise that obscures actual risk.
Security teams are drowning in alerts, with organizations processing an average of 960 alerts per day.3 Without the ability to distinguish between a theoretical vulnerability and an exploitable risk, this alert volume can equate to thousands of hours a year investigating false positives. This "high decibel" environment makes it impossible to prioritize remediation based on actual business impact.
Measuring the impact of low-context data
A security team operating without precise data becomes inefficient. When specialists cannot quickly pinpoint which vulnerabilities are reachable by an attacker, they lose valuable time on non-critical issues.
Unable to cope with the deluge of undifferentiated data, many teams fall into the trap of the AppSec tool swamp, where they are adding more scanners that only increase the complexity. By focusing on broad alert categories rather than verified exploitability, teams risk missing the critical vulnerabilities that pose a true threat to the organization.
Precision over proximity: Pinpointing exploitability
The traditional approach to AppSec focuses on finding potential flaws. Contrast Security changes the paradigm by focusing on proven exploitability. Instead of broad warnings, Contrast identifies the specific lines of vulnerable code and confirms whether that code is reachable during execution. This eliminates the guesswork for security teams, allowing them to move past "noise" and focus on the vulnerabilities that pose a tangible risk to the application.
Ending security-development friction
When developers are forced to decipher vague security reports, friction is inevitable. Currently, 52% of developers admit to bypassing security measures to meet deadlines, simply because they cannot afford to spend hours triaging low-context alerts.4
By providing exact code-level locations and clear remediation paths, Contrast empowers developers to fix what matters without slowing down the release cycle. This replaces friction with flow:
The velocity demands of AI-generated code accelerate the pace of application development — in terms of release cycles — to the point where scanning simply cannot keep up. Legacy static and dynamic security scanning tools as well as perimeter defenses for applications in production lack the accuracy needed by modern software.
Frustrated and measured via how much code they write and release, developers are bypassing the alert noise of false positives. While this enables them to meet their Major Business Objectives (MBOs), doing so ratchets up risk and has an adverse effect on security teams and their MBOs. Security and development teams, as a result, often find themselves juxtaposed and constantly butting heads with each other. In response, a different approach to AppSec is needed — one that analyzes applications for vulnerabilities from within the software.
Contributors to false-positive alert rates:
The evolution of application security has reached a critical bottleneck. The core issue lies in security measures that operate from the outside looking in, lacking the contextual depth required by sophisticated, modern software architectures.
The era of "security by volume" has reached its breaking point. As this paper has explored, the reliance on legacy SAST, DAST and perimeter defenses has created a "triage tax" that modern enterprises can no longer afford to pay. When 71% to 90% of alerts are false positives,1 security is no longer a safety net — it is a bottleneck.
The evidence is clear: Traditional, context-blind scanning tools fail because they attempt to secure applications from the outside looking in. This architectural limitation results in a "tool swamp" that drowns AppSec teams in noise, alienates developers and leaves organizations vulnerable to the risks that are truly exploitable. To maintain the velocity of modern DevOps without sacrificing security, the industry must pivot from theoretical detection to runtime observability.
By integrating security within the running application, organizations can finally bridge the gap between "vulnerability on paper" and "vulnerability in practice." Shifting to a runtime-centric approach allows teams to:
Eliminate the noise: Automatically verify "reachability" to ensure developers only spend time on code that can be executed and exploited.
Unify the workflow: Break down the silos between security and engineering by providing high-fidelity, actionable data directly into existing CI/CD pipelines.
Reclaim human capital: Shift expensive AppSec talent away from manual triage and back toward high-value tasks like threat modeling and proactive architecture.
The goal of AppSec is not to find the most vulnerabilities — it is to fix the right ones. By replacing legacy "best-guess" scanning with precision-based runtime context, organizations can end the developer-security divide, accelerate their release cycles and achieve a state of true, scalable resilience.
Contrast empowers your team with the observability and control they need in order to pinpoint exploitable lines of code, and detect and block threats that target custom applications and APIs.
And it lets you strengthen application security in a manner that’s tightly integrated with existing workflows.
Safeguard your applications throughout their entire life cycle with Contrast.
1 Exorcising the SAST Demons, Ghost Security
2 Navigating the Noise: Separating Security Reality from Hype
3 State of AI in Security Operations 2025
4 Application Security and the Innovation Imperative
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