What is application security?

Dynamic IT environments have made application security more complex than ever. As organizations standardize on cloud-native architectures, microservices, and AI-assisted development, the speed and complexity of software delivery has increased dramatically. At the same time, the application layer has become the primary target for attackers, and emerging AI-assisted attack techniques are accelerating both the scale and sophistication of exploitation attempts. Application security (AppSec) encompasses the practices, tools, and processes used to reduce and manage vulnerabilities that could enable unauthorized access, data exfiltration, or service disruption. While this mission hasn’t changed, how applications are built, deployed, and attacked has.

Today, effective AppSec goes beyond finding the most vulnerabilities into understanding which vulnerabilities matter most.

Modern applications are assembled from open source libraries, container images, APIs, managed cloud services, and infrastructure-as-code. This composable model accelerates delivery, but it also expands the attack surface and introduces new forms of risk.

That gap is increasingly visible. In practice, most security teams find vulnerability and posture findings meaningful only when they’re enriched with runtime context. Without that context, findings remain largely theoretical, offering little clarity into actual exposure or risk.

This is why runtime visibility has become the defining requirement for modern application security.

Security teams now face:

• Massive vulnerability backlogs
• High false-positive rates
• Limited guidance on what to fix first
• Ongoing friction between development, platform, and security teams

Detection isn’t the hard part anymore; it’s being able to understand and prioritize security issues.

Open source software underpins nearly every modern application. However, many AppSec tools still treat all detected vulnerabilities as equally urgent, regardless of whether the vulnerable code is ever executed.

In practice:

• A library that is never loaded cannot be exploited
• A vulnerable function that is unreachable poses minimal risk
• A dependency present only in non-production paths may have no real impact

The consequences are measurable:

• Over 80% of vulnerable application dependencies remain unpatched for more than a year, even though safer alternatives exist in the vast majority of cases
• Even when updates are applied, some dependencies are upgraded to different insecure versions, perpetuating risk rather than reducing it

Without runtime insight, teams can’t confidently determine whether a vulnerable component is reachable, exposed, or relevant in production. This forces an impossible tradeoff between slowing delivery and accepting unknown risk.

As cloud adoption has matured, attackers have shifted away from infrastructure-level exploits and toward applications where business logic, user access, and sensitive data converge.

Industry data consistently shows that:

• Most cloud breaches begin with web application attacks
• Web applications remain one of the largest and most exploited attack surfaces
• Applications are continuously targeted by attackers
• Viable application-layer attacks regularly bypass traditional defenses

At the same time, perimeter-based controls are proving insufficient. Traditional web application firewalls catch only a small subset of application-layer exploits, leaving much of the attack surface invisible to security teams.

The implication is clear: security tools that operate outside the application runtime can’t reliably see or stop modern attacks.

To secure modern applications, AppSec must operate where risk actually materializes: at runtime.

Runtime intelligence allows teams to:

• See which code paths execute in production
• Determine whether vulnerable functions are reachable and exposed
• Detect active exploitation attempts in real time
• Prioritize remediation based on exposure and business impact

This shift is reflected in market direction. Over 60% of security leaders now identify Application Detection and Response capabilities as the most important emerging feature in cloud security platforms, signaling a move away from static scanning toward continuous, runtime-driven protection.

AI-assisted coding is rapidly becoming standard practice. While it accelerates development, it also introduces new and less predictable risk patterns.

At the same time, security leaders believe AI-powered threats will evade traditional detection techniques, increasing pressure on tools that rely on signatures, static rules, or perimeter controls.

In this environment, security without runtime context becomes increasingly blind.

Dynatrace approaches application security with runtime context, embedding security analysis directly into its AI-powered observability platform. Rather than relying solely on isolated scans or post-deployment alerts, Dynatrace analyzes application behavior as it executes.

By correlating vulnerabilities, attacks, and runtime behavior of pre-productiona and production environments, Dynatrace enables teams to:

• Identify which vulnerabilities are actually exploitable
• Detect and investigate real application-layer attacks as they occur
• Eliminate false positives by grounding findings in execution context
• Automatically prioritize risk based on exposure and impact