New Report: State of AppSec 2026 | Security at Engineering Speed

The new bottleneck: verification (not detection)
1) Velocity outpaced verification
2) Risk moved up the stack
3) “Findings” stopped being a useful unit of truth
Why scan-and-report hit a ceiling
Configuration brittleness doesn’t scale
Findings lack decision context
Detection volume doesn’t reduce risk
Where modern risk actually lives: authZ, logic, and exploit chains
What comes next: from findings to outcomes
How ProjectDiscovery's Neo fits into this shift
Download the report

Authors

Rachel Benson

Software has changed faster than the security model designed to protect it.

In 2026, most organizations aren’t shipping “applications” so much as they’re shipping continuous change; across APIs and services, infrastructure and configuration, identity and permissions, feature flags, and AI-assisted code. Each layer evolves independently. The system’s security behavior is increasingly an emergent property of interactions, not any single pull request.

And yet a large percentage of AppSec programs are still operating on an older rhythm:

scan periodically
ship findings
triage manually
fix what fits the sprint

That model worked when delivery was slower and environments were more stable; when humans could close the gap between detection and decision. But as delivery becomes machine-paced, the limits of scan-and-report aren’t just inconvenient. They’re architectural.

This blog is a quick preview of our new report, State of AppSec 2026: Security at engineering speed, and why we believe the next era of application security requires a different foundation.

→ Download the full report: https://projectdiscovery.io/whitepapers/application-security-report-2026

The new bottleneck: verification (not detection)

Most security leaders already feel it: you can always add more detection. But what you can’t easily add is trusted, high-confidence decisions at the same rate engineering ships.

AI made this unavoidable. But it didn’t create the fire; it poured gas on structural issues that were already there:

1) Velocity outpaced verification

AI accelerated code creation, but the constraint moved downstream into review, testing, integration, and governance. Many teams see the same pattern: output increases, but end-to-end quality and assurance don’t rise at the same rate.

In other words, faster generation doesn’t automatically mean faster delivery of safe software. It often means a larger verification burden; one traditional AppSec workflows were never designed to absorb.

2) Risk moved up the stack

Many of the highest-impact failures today aren’t missing patches. They’re authorization gaps, business logic abuse, and exploitable workflows that look “valid” to most scanners.

These failures aren’t just harder to detect. They often require reasoning about intent, identities, boundaries, and invariants across multi-step flows.

3) “Findings” stopped being a useful unit of truth

A finding without context is a tax. It still requires a human to answer the only question that matters:

Is this exploitable in our application, our environment, right now?

When the output isn’t trusted or provable, teams don’t get safer software. They get larger backlogs and slower alignment between security and engineering.

Code is now cheaper to produce than it is to verify. That imbalance is the defining constraint of modern software risk.

Why scan-and-report hit a ceiling

Scan-and-report AST platforms were built for an earlier operating model: run scanners, collect findings, prioritize, then hand off to humans to interpret and fix.

In modern delivery, that breaks down for structural reasons that more tuning can’t solve:

Configuration brittleness doesn’t scale

Accurate scanning depends on accurate configuration. But the parts that determine security behavior change constantly: auth and login flows, session handling, role/permission logic, tenant routing, feature flags.

When security depends on brittle configuration, AppSec becomes maintenance work that scales linearly with application count and change rate.

Findings lack decision context

Pattern detection can’t reliably answer the questions that drive real decisions:

Is the input attacker-controlled in this execution path?
Does authorization actually enforce ownership—or merely check authentication?
Does the endpoint behave the same under real identities, data, and configuration?

Without runtime context, you get predictable disagreement: security sees severity, engineering sees non-exploitability, and the truth sits behind manual investigation.

Detection volume doesn’t reduce risk

More findings aren’t the same as reduced risk. A useful result is one that’s:

contextual (why it matters here)
actionable (how to fix it)
ideally proven exploitable (so teams can move fast with confidence)

In an environment where attackers move quickly, “maybe” findings are increasingly unaffordable.

Where modern risk actually lives: authZ, logic, and exploit chains

If you only modernize scanners, you’ll still miss many of the failures that matter most, as modern incidents often originate from valid requests that exploit unintended behavior.

Business logic abuse hides behind legitimate workflows (discount/refund edge cases, replay/race/reorder sequences).
Authorization failures are behavioral problems: the request can be syntactically correct, pass validation, and still be a breach.
Exploit chains amplify small gaps across services and workflows—and often only become visible end-to-end.

This is the core shift: modern risk is less about “bad input” and more about unexpected behavior under real constraints.

Which leads to the conclusion we make in the report:

AppSec needs exploitability proof in real environments; not more ungrounded output.

What comes next: from findings to outcomes

We believe the next era of AppSec isn’t “a better scanner.” It’s a new architecture: systems that can reason, remember, and execute security work continuously across the delivery lifecycle.

In the report, we outline what “Security at engineering speed” requires in practice:

Continuously threat model (not as a quarterly artifact, but as living understanding that updates as architecture changes)
Review every PR with full context (repo history, prior decisions, and fix guidance developers can apply immediately)
Prove exploitability to cut noise (evidence ends debates and accelerates remediation)

This is the shift from scan-and-report to reason-and-execute—from “more alerts” to faster, higher-confidence security decisions.

How ProjectDiscovery's Neo fits into this shift

Neo is built around the constraint the report describes: verification is now the bottleneck. So the goal isn’t to produce more output; it’s to turn ambiguous signals into evidence-backed outcomes engineering can act on quickly.

At a high level, Neo is designed to:

maintain long-lived application context over time (not stateless scans)
validate hypotheses through controlled execution in environments you allow
attach reproducible evidence (steps, logs/artifacts, and minimal fixes)
operate with governable workflows (bounded tools, scoped permissions, auditable actions)

If your AppSec team is buried in triage, struggling to prove exploitability, or watching logic/authZ issues slip through despite heavy tooling—this is the problem Neo was built to solve.

Download the report

If you’re leading AppSec or security engineering, this report is designed to be directly useful; not theoretical.

In State of AppSec 2026, you’ll get:

a clear framing of why scan-and-report hit a ceiling
the structural reasons “more scanning” doesn’t fix verification
where risk concentrates in modern systems (authZ, logic, exploit chains)
what a next-generation AppSec architecture needs to deliver
how evidence-backed outcomes replace noisy findings