Every CEO and board member I speak with is laser-focused on developer productivity. In fact, 90% of tech leaders say improving developer productivity is a top initiative. It's no wonder that organizations are eagerly embracing new AI-powered coding tools to boost output. Executives even expect nearly a 50% jump in developer productivity from AI adoption, and many developers are already leveraging AI assistants to write software faster. This push for speed and efficiency is understandable—but it also warrants a hard look at an often overlooked drag on productivity: the hidden tax that traditional security processes impose on developers.

Before rushing to adopt the next shiny AI tool, we need to consider just how much of a time-sink security tasks have become for developers. The reality is that developers today spend an alarming amount of their week on security-related work—tasks like fixing vulnerabilities, managing dependency updates, responding to security tickets, and so on. Worse, this burden often comes on top of regular development duties, with many developers handling security outside regular hours, costing companies thousands of dollars per developer per year in lost productivity. Yet despite this heavy workload, many surveys report that few developers consider security their top priority when coding—a clear sign that developers see much of this effort as a tax rather than a value-add. Despite this being an old trope, sadly, it still seems to be true: in the rush to deliver software, security can feel like a roadblock, or even a headwind on innovation if it consistently slows down releases and distracts from building new features.

The Developer Productivity Tax of Security

Let's unpack the pain points contributing to this "security tax" on development. Several factors are draining developer time and causing frustration:

• Noise from Unprioritized Alerts: AppSec tools (SAST, SCA, container scanners, etc.) often overwhelm developers with findings that lack context or prioritization. Studies show that only 2–5% of security alerts are actionable; the other ~95% are essentially noise. Developers end up wading through huge lists of theoretical vulnerabilities and false positives. For example, one large-scale analysis of 101 million alerts found that without context, developers waste time chasing issues that pose little to no real risk. This alert fatigue means critical signals get drowned out by irrelevant warnings.
  
• Impact on Developer Productivity: All this noise has a direct productivity cost. Time spent investigating non-issues or explaining false positives is time not spent coding. As noted earlier, developers are losing a significant portion of their week to security busywork. Thomas Dohmke, CEO of GitHub, recently observed that developers often get only ~2 hours of real coding done per day, spending much more time "deciphering rather than shipping" due to issues, bugs, and vulnerabilities. In other words, the friction from security issues is eating into the creative, high-value work developers want to be doing. Long scan reports and endless ticket queues cause context-switching and interrupt "flow", making it hard for engineers to stay productive.
  
• Headwinds to Innovation: When security processes are overly burdensome, they can actively slow innovation. If every new library import or deployment triggers a flood of alerts, teams become hesitant to adopt new technologies or make bold changes. Developers may delay upgrades or avoid using open-source components because they fear the avalanche of vulnerability reports that will follow. In extreme cases, security becomes a blocker to agility—features get postponed while teams grind through remediation backlogs. Over time, this can create a culture where engineers see security as the enemy of velocity.  The organization pays the price in slower time-to-market and missed opportunities.
  

These pain points are not just hypothetical. They show up in metrics and anecdotes across the industry. For instance, an industry survey of 1,500 engineers found the average developer spends 19% of their time on security tasks (often outside normal hours) and frequently doesn’t even fully understand the tickets they receive. It's common to hear grumblings that security tools are “noisy”, that their builds break for minor policy violations, and teams feel they’re spinning their wheels on low-value fixes. Security policies today can create more problems than they solve, overwhelming developers with noise and unnecessary build breaks. The result is predictable: developers get frustrated, security teams struggle to get buy-in, and critical issues can slip through the cracks amid all the noise.

Why Developer Experience Matters in Security Tools

Improving this situation starts with a mindset shift: developer experience (DX) is not a “nice-to-have” in security tools—it's essential for success. If a security tool or process creates too much friction, developers will find ways to work around it, delay it, or ignore it. On the flip side, a developer-friendly security approach can actually accelerate delivery by catching issues early without creating drag. In other words, security programs need to enable developers, not hinder them.

Unfortunately, many legacy AppSec tools have earned a reputation for doing the latter. An experienced AppSec practitioner noted that older SCA tools “often hindered more than helped” development, whereas effective modern tools provide actionable insights and integrate smoothly into developer workflows. This integration is key—developers shouldn’t have to jump through hoops or leave their familiar tools to handle security. Whether it's receiving security findings directly in pull requests or IDEs, or having clear guidance on how to fix issues, a good DX keeps developers in their flow state. The difference is stark: organizations that embed security into dev workflows see much higher adoption and success. (In the DevSecOps maturity model, moving from bolted-on security to a “developer experience focused” approach is what enables teams to collaborate and speed up fixes truly.)

A positive developer experience with security tools also builds trust and buy-in. When developers feel a tool is on their side—saving them time and highlighting important problems (as opposed to spamming them)—they're far more likely to use it consistently. One DevSecOps engineer recounted that their previous SCA tool was easy enough to use that it “trained our developers to have a good attitude towards [the] SCA tool, and therefore use it!”. This is a great example of how focusing on usability and low friction can actually cultivate a security mindset on the engineering team. Developers begin to see the tool as a helpful safety net rather than a hindrance. In contrast, if a tool constantly cries wolf with false alarms, developers will tune it out (or turn it off). Burnout and apathy set in when teams feel they are drowning in pointless security tasks—a phenomenon some have dubbed "AppSec burnout" in the face of relentless alert fatigue.

In short, developer experience can make or break a security program. Strong security outcomes increasingly depend on developer cooperation. Achieving that means designing security tools and processes that respect developers’ time and workflows. The goal should be to reduce developers' cognitive load, not increase it. When evaluating any code security solution, technical leaders should ask: Will our developers love using this? Or at least, will they not hate it? A tool that finds every theoretical vulnerability but alienates the dev team is worse than useless—it undermines the security mission. By contrast, a tool that might find slightly fewer issues but filters out noise and fits into dev workflows can dramatically improve real security by ensuring the critical issues actually get fixed. Developer-centric design and strong UX in security tooling are not just about convenience—they directly correlate with risk reduction and faster remediation.

Eliminating the Productivity Tax with Context and Intelligence

How can we actually eliminate the “developer productivity tax” caused by security tasks? The answer lies in smarter, context-driven security tools that drastically cut down noise and effort for developers. Both AI-generated code and human-written code stand to benefit from these advancements—and let's not forget, your existing codebase isn’t going away, so any solution must handle legacy code at scale too. The key is to triage and prioritize security issues the way a savvy human expert would, but automated and at scale. This means leveraging contextual information about how a vulnerability actually impacts your application to decide whether it’s worth a developer’s attention.

For example, modern Application Security platforms use techniques such as function-level reachability analysis to determine whether a vulnerable piece of code is ever called in your product. If a vulnerability in an open-source library is present but not reachable (i.e., your application never invokes the vulnerable function), that finding can be safely deprioritized or even ignored. This approach alone can eliminate a vast swath of false positives. At Starburst Data, the security engineering team adopted a reachability-based SCA tool and saw a 98.3% reduction in noise in their open source vulnerability findings, “no more manual research” needed to explain false positives. With such a tool, hundreds of irrelevant alerts dropped to just a handful of real issues, enabling much faster turnaround on customer security inquiries. Results like this are not outliers—other organizations have reported average noise reductions of 92%+ by switching to context-aware scanning. When the vast majority of trivial or non-exploitable alerts are automatically weeded out, developers can focus on the vulnerabilities that truly matter.

Context-driven prioritization goes beyond reachability. It also means incorporating data about exploitability (Is there a known exploit or active attack for this issue? Is it a known KEV from CISA’s database?), business impact (Is this component in a critical application or just an internal tool?), and environmental context (Is the vulnerable code executed in production or only in a dev/test environment?). In one analysis, applying such context cut a 570,000-alert backlog by 98%, distilling it to about 12,000 alerts with only ~200 truly critical issues. These kinds of risk-based filters are game-changing. They turn the impossible task of fixing “everything” into a much smaller, achievable to-do list. As a result, developers and security teams spend their time on the 2% of issues that matter, rather than the 98% that do not.

Beyond prioritization, improving the developer experience also means streamlining remediation. This is where some of the same AI revolution can be applied on the security side, not just in code generation. For instance, we are seeing the rise of AI assistants that can automatically suggest fixes or even open merge requests for certain classes of vulnerabilities. Imagine an AI that not only flags a dangerous use of a crypto API, but also provides a code change to use a safer library, saving the developer from researching the solution. By integrating these intelligent agents into code security tools, we can start chipping away at the remediation bottleneck as well. The endgame is a virtuous cycle: the tool highlights a real issue (one that genuinely needs fixing) and helps the developer fix it quickly, ideally within their normal workflow. This reduces the overall "time to remediate" and prevents security tasks from piling up in a backlog. Organizations that embrace automated or assisted remediation have reported 74% less manual triage effort and significantly faster fix times in their AppSec process. While full automation won't fit every scenario, even partial automation and guided fixes can reduce developers' burden.

Crucially, these improvements apply equally to code written by humans and code generated by AI. In fact, AI-generated code might introduce security issues at a speed and scale even greater than human code (since an AI can produce insecure code very quickly if not properly guided). As teams rely more on AI coding assistants, it's even more critical to have intelligent security tooling that catches mistakes in real time without flooding developers with noise. The solution is not to hold back on using AI—it's to apply the same AI and context-driven techniques in our security layer. 

One example is Endor Labs' platform, which was built with this philosophy: it analyzes codebases of all ages (from legacy C++ to modern code) with AI agents and rich security data, and doesn’t just flag issues—it reduces noise, prioritizes what matters most, and even proposes intelligent fixes based on the code’s context. In practice, this means identifying the critical 1% of vulnerabilities and helping developers address them faster, rather than dumping 100% of findings on their plate. Whether your code was authored by a person or generated by an AI, the goal is the same: maximize signal, minimize noise, and make the secure path the path of least resistance for developers.

Conclusion: Secure Code, Happy Developers

As engineering and security leaders, we must recognize that developer experience is a top priority for code security. Pushing security onto developers without regard for productivity leads to burnout, wasted effort, and ultimately a less secure outcome. The good news is that by reducing noise and friction through smarter prioritization, better tooling, and a bit of AI assistance, we can eliminate much of the “developer productivity tax” that security has historically imposed. Developers regain time to build and innovate, yet security risk still goes down because the truly critical issues are being fixed more reliably. It’s a classic win-win.

Every organization will need to tackle both its AI-generated code and its existing codebase with this approach. By investing in developer-centric security tools that cut alert noise by 90%+ and integrate seamlessly into workflows, you enable your teams to move fast and safely. The boardroom goal of developer productivity and the CISO’s goal of a strong security posture no longer have to be at odds. When done right, empowering developers with great security tooling means they can deliver secure code without sacrificing speed. In the end, a happy developer experience with security translates to software that is not only built faster, but built better and more securely – a result every CEO, CISO, and developer can celebrate.

Interested in how this works in practice? Check out the next installment in this series, where we take a detailed look at the Endor Labs developer experience.