The last thing cloud security teams need is more alerts when they’re already drowning in them.
CNAPPs are great at producing findings, adding critical labels, and creating more tickets than teams can handle.
But all of this leads to a growing backlog where teams spend days validating alerts while the same high-risk issues stay open quarter after quarter.
In fact, after analyzing more than 4.7 million alerts in our 2025 State of Cloud Remediation Report, we found that critical alerts take an average of 128 days to remediate, even in environments with the best detection tools.
That gap shows where cloud security breaks down:
- Detection creates volume, but it doesn’t help teams decide what actually matters or move fixes safely into production.
- Alert overload slows response, erodes trust in risk scores, and pulls security teams into investigation loops that don’t change exposure.
The industry is starting to respond by shifting focus from finding everything to fixing what matters. Learn why alert-heavy cloud security creates more work than outcomes, how that flood stalls remediation quietly, and what an outcome-driven approach looks like when the goal is measurable risk reduction.
The Problem: Detection Scales Faster Than Teams Can Decide
“Remediation capacity is bounded by change management, not by scan coverage.”
Early cloud security programs optimized for coverage. The goal was simple: find more issues, faster, across every account, service, and workload. CNAPPs delivered on that promise by dramatically increasing visibility.
At scale, that approach starts to work against teams. Large environments routinely generate 10,000 or more alerts per month (the average across large enterprises in the insurance, pharma, and entertainment industries), while remediation capacity remains largely unchanged. Even well-staffed teams can only investigate and fix a fraction of what gets flagged, so backlogs grow despite constant effort.
The harder problem is decision-making. Risk scoring varies widely between tools, and static severity scores fail to account for real-world context like asset exposure, identity paths, or blast radius. As a result, teams are forced to prioritize manually, debating which alerts deserve attention and which can wait.
Detection clearly answers what exists in the environment. The real bottleneck shows up when teams try to decide what gets fixed next and what can safely be ignored.
Alert Volume Erodes Trust in Security Signals
With every new alert, signal quality drops. A large share of cloud security findings turn out to be low-value false positives or issues already mitigated by the environment.
Analysts spend more time validating alerts than fixing anything, slowing remediation and draining focus. Over time, this changes how teams respond.
Here’s how our customers’ security teams describe alert volume:
- “We’re talking about millions. It’s overwhelming.”
- “Everything lights up like a Christmas tree.”
- “We have a lot of alerts. Basically trash.”
- “About 90% of detections end up being false positives.”
This leads to more than burnout as teams start tuning out entire classes of findings, even when real risk is present.
Alert fatigue quickly becomes a signal problem, where meaningful exposures are buried under volume and security teams lose the ability to distinguish what actually matters from what can be safely ignored.
Related Content: What is a Remediation Workflow?
Why Alerts Stall Once They’re Real
“The cost of a wrong fix is immediate, the cost of a delayed fix is invisible, so teams default to delay.”
Identifying the right issue doesn’t mean it gets fixed. Most cloud security teams are small, stretched thin, and tasked with managing environments that change faster than their staffing models anticipated.
Deep cloud remediation expertise is scarce, and the people who have it are usually pulled in multiple directions.
In real environments, alerts sit open for months, sometimes longer. Teams hesitate to automate fixes because remediation is still manual and risky. One wrong change can break production, disrupt services, or trigger downstream issues that take days to unwind. Legacy systems, brittle dependencies, and over-permissioned roles make even straightforward fixes harder than they appear.
All of this leads to hesitation, even when everyone agrees an issue is real and important, teams lack the time, confidence, or access to fix it safely. This is where remediation slows to a crawl and mean time to remediate (MTTR) stretches from days into months, leaving known risk exposed far longer than anyone is comfortable admitting.
Related Content: Why Speed Matters in Cloud Security (And What You Can Do About It)
The Investigation Tax on Developers
When alert volume grows, security teams escalate issues earlier and more often. Findings get handed to developers before they’re fully validated, shifting the burden of investigation onto the people responsible for shipping code. Instead of executing known fixes, developers are asked to determine whether an alert actually matters in their environment.
Research from the Ponemon Institute reveals that security teams waste about 25% of their time chasing false positives, investigating alerts that never turn into real threats. This creates a lopsided workflow where hours go into investigating alerts, while actual remediation often takes minutes. Fixes that aren’t well understood drift or break and reopen later, restarting the cycle.
Over time, developer trust erodes. Security starts to feel like an interruption rather than a partner in reducing risk. Teams push back on escalations, and genuinely critical issues sit open longer than they should. This is where alert volume stops being a security problem and becomes organizational friction.
What Outcome-Driven Cloud Security Looks Like
Outcome-driven teams measure success by what gets fixed, not how many alerts get generated. The focus shifts from managing volume to executing work that actually reduces exposure.
- Ruthless prioritization: Only issues that materially reduce risk move forward. Teams stop escalating “just in case” and focus on what changes exposure in real environments.
- Deep investigation first: Every alert is validated before it reaches a developer, with clear scope across affected accounts, regions, and assets. Teams know exactly what’s impacted and what isn’t, so no one is asked to chase ambiguous or low-confidence findings.
- Safe remediation: Fixes are planned with production impact in mind. Each change includes a blast-radius check, rollback path, and explicit configuration or code deltas, so teams understand what could break and how to recover if needed.
- Clear ownership and execution path: Remediation comes with defined ownership, approval flow, and execution steps, removing guesswork around who applies the fix and how success is verified.
- Recurrence prevention: Changes are paired with policies or guardrails that prevent the same issue from reappearing in the next deploy or audit cycle.
- Safe automation with human oversight: Automation handles repetitive investigation and triage, while humans stay in the loop for judgment and confidence
| In 2025, we’ve seen overall alert volume drop by 66% in Criticals and Highs for a fully operated Tamnoon customer in the entertainment industry |
In this model, alerts become completed work instead of tickets waiting in a queue. Security effort shifts away from managing noise and toward steadily driving risk down as a unified team.
Finish What Your CNAPPs Start with Tamnoon
Cloud security teams don’t need more alerts. They need fewer decisions, less investigation, and a reliable path to safe remediation. Detection alone keeps teams busy, but it doesn’t move risk down. The real work starts after an alert is raised.
Outcome-driven security focuses on finishing what gets found. That means prioritizing what actually matters, validating issues before they reach developers, and applying fixes that won’t break production.
When remediation is safe and repeatable, MTTR drops, backlogs shrink, and teams regain confidence in their security workflows.
Tamnoon exists to finish what CNAPPs start. We help teams move from noisy findings to verified, production-safe remediation, reducing wasted investigation time and turning alerts into safe remediation plans.
