More than half of executives say fragmented security tools are holding them back from responding to threats, and they estimate that fragmentation and complexity cost their organizations an average of 5% of annual revenue. This finding comes from a 2025 IBM Institute for Business Value study with Palo Alto Networks, and it captures a shift now underway across the industry. The stack teams built to keep themselves safe has become a liability. Today, 61% of organizations are either consolidating their tools and vendors or planning to.
Scale is what turns tool coverage into tool sprawl. The average enterprise juggles 83 security solutions from 29 vendors. Each tool had a clear purpose when it was added. One scanned source code, another checked dependencies, another detected secrets, and another covered infrastructure. Some were adopted directly; others came bundled inside broader platform offerings. But added together, the stack creates a hidden tax beyond the license cost. Teams spend more time chasing alerts, switching contexts, and reconciling findings than actually fixing the risks those tools were meant to surface.
The bill you can see, and the one you cannot
Security stacks are full of overlapping tools, each adding another line item. The spend adds up quickly, but the bigger cost shows up in your engineers' day.
It starts with noise. Every tool reports findings in its own way, with its own severity scale, so prioritization becomes guesswork. Real risk gets buried in a flood of alerts. Security teams can face alert volumes so large that a hundred thousand findings becomes an unworkable queue.
Then comes the lack of context and reasoning. Pattern-based scanners can read syntax, but they do not understand how the application actually works. That is how complex, logic-based flaws slip through, while teams spend time chasing false positives that were never exploitable.
And finally, the backlog grows. Detection without a clear path to remediation creates a pile of issues that moves faster than any team can clear. Triage becomes a constant drain, and security checks turn into delivery blockers. The findings meant to protect production end up competing with the work required to ship it.
AI moved the bottleneck to verification
Over the past year, the pressure has moved downstream. AI is writing more enterprise code, and the hard part is no longer just producing it, it is reviewing, validating, and trusting what gets shipped. In Sonar's 2026 State of Code survey, developers named reviewing and validating AI-generated code for quality and security as the single most important task of the AI era.
In the same study, 96% of developers say they do not fully trust that AI-generated code is functionally correct, and 57% worry it exposes sensitive company or customer data, their top concern. The data points in the same direction. An academic benchmark found that even the best AI models produced correct, secure backend code only 37% of the time, while roughly half of the functional code was still exploitable. New Relic research also found that 62% of teams ship AI-generated code without a line-by-line review.
More code is being written faster, introducing more security vulnerabilities while being reviewed less consistently and pushed through tools built for the pre-AI era. The result is compounding security debt. Veracode's 2026 State of Software Security found 82% of organizations now carry security debt, up 11% in a single year, with high-risk vulnerabilities up 36% year over year.
For a closer look at why this pressure is accelerating, see Security at AI speed.
Why adding another tool makes it worse
The instinct is to solve every new security gap with another tool. But that is how the sprawl started, and in the AI era, it backfires. Each new point tool adds another severity scale, dashboard, and queue, creating more noise when teams need clarity most, especially as more code is AI-generated and harder to validate, triage, and trust.
More tools also multiply context switching, and switching is expensive. Research on interrupted work found it takes about 25 minutes to return to a task once you have been pulled away. Every separate tool, dashboard, and ticket queue is one more switch that pulls a developer off the fix.
What a modern review solution looks like
The teams pulling ahead are consolidating onto a single platform built for shipping at AI speed. They tend to look for the same core capabilities:
- One platform for the full picture. Code review sits alongside code security review, including static analysis, dependency scanning, secrets detection, and infrastructure checks. Instead of a stack of point tools, teams get one product, one workflow, and one bill.
- One place to prioritize risk. Findings arrive in a single workflow, ranked by severity and reachability, so teams can focus on issues that are genuinely exploitable instead of chasing dead code or low-signal alerts.
- Codebase-wide reasoning. Detection reasons across the full codebase to catch logic-based flaws that pattern matching often misses. High-signal security issues are surfaced, reducing false positives.
- Remediation where developers already work. Findings show up directly in the pull request, so developers can understand, validate, and fix issues before they hit the backlog. For fixes that require cross-team tracking, integrations with major issue trackers keep the work moving. This is what helps close the verification gap AI-generated code has opened. For teams formalizing that gate, see building a quality gate that works for AI-generated code.
- One view of security posture. Teams get a consolidated dashboard where findings remain visible, trackable, and tied back to the code changes that introduced them.
Consolidation as leverage
Cutting redundant licenses is the easy win. The real return is everything downstream. In the IBM and Palo Alto Networks research, organizations that consolidated onto integrated platforms generated nearly four times the return on their security investment, identified incidents about 72 days faster, and contained them about 84 days faster than those running fragmented tools. Developers keep their focus, the backlog stops growing, and security leaders finally see their whole posture in one view.
The security stack was assembled one tool at a time, for a pace of software that no longer exists. The teams shipping at the speed of AI are rebuilding it around one question. Not how many tools they own, but how quickly they can find real risk, validate it, fix it, and ship with confidence.
