"We need to fix our fundamentals first.""Our data isn't clean enough.""The technology is still too immature."

These are the justifications security leaders cite when delaying AI adoption. While they do make sense and sound responsible, they're costing organizations millions.

As of 2025, only 4% of companies are fully prepared for today’s AI powered cybersecurity threats, according to CISCO’s cybersecurity readiness index 2025.

Another report shows that only 18% of security teams have fully implemented AI cybersecurity tools, the remaining 82% cite "readiness concerns" as their primary barrier (Ponemon Institute). Meanwhile, 65% state challenges integrating AI with legacy systems using this technical hurdle to justify inaction rather than evolution.

The reality? There is no "perfect readiness" for AI.

Here we will break down the "not ready" myth, what it truly costs, and how security leaders can realistically assess their organization's AI readiness because the question isn't whether you're ready for AI, but whether you can afford to wait.

The Hidden Costs of Delayed AI Adoption

AI-driven cybersecurity solutions are advancing quickly in response to the projected annual cost of cybercrime, which is expected to surpass $10.5 trillion in 2025. Yet despite the rising stakes, many organizations still rely on traditional SOCs that are not efficient to keep up.

According to Morning Consult and IBM, nearly one-third of a typical SOC analyst’s workday is spent responding to incidents that pose no real threat, with false positives and low-priority alerts comprising about 63% of daily alerts. This constant drain on time and focus prevents teams from addressing genuine threats promptly, creating serious operational blind spots.

Let’s discuss the main ones:

Knowledge Erosion and Tribal Loss

The cybersecurity industry's turnover problem has reached crisis levels. 71% of SOC analysts report feeling burned out, and 64% are contemplating leaving their positions within the first year. This revolving door creates institutional knowledge gaps that grow exponentially:

• Expertise that never transfers: When experienced analysts leave, they take with them critical context about the company’s environment: detection patterns, system quirks, and historical attack behaviors that never made it into documentation.
• Extended vulnerability windows: New analysts require 7 months to 2 years to reach full productivity, creating prolonged periods where threats may go undetected simply because no one recognizes the patterns. Replacing a tech employee costs 1.5–2x their salary.

This tribal knowledge loss creates blind spots that attackers can exploit for months before being detected.

Innovation Drought

Organizations caught in the alert-response cycle face a paralysis that prevents strategic advancement:

• Tactical imprisonment: When teams process upwards of 4,484 alerts daily, they're trapped in perpetual firefighting mode. Teams end up spending most of their time on routine maintenance and response activities, leaving minimal bandwidth for innovation.
• Strategic debt: Every hour spent investigating false positives (which account for 75-80% of all alerts) represents lost opportunity to develop new detection capabilities, implement proactive controls, or align security with business objectives.

This innovation deficit creates a widening gap between security capabilities and business needs. Organizations unable to evolve beyond reactive security measures find themselves at a significant competitive disadvantage, with security becoming a business blocker rather than an enabler.

Burnout's Compounding Interest

Perhaps most costly is the human impact of delayed AI adoption.

• Escalating stress: 71% of security professionals report high levels of stress, with alert fatigue cited as the primary contributor.
• Performance degradation: Burnout directly impacts security effectiveness as tired analysts miss 33% more critical alerts and produce 26% more false positives than their engaged counterparts.

Using these data, we can estimate that when accounting for lost productivity, increased turnover, and greater breach risk due to burnout, a single SOC team may cost a company upwards of $1.7 million per year which is entirely avoidable with smarter automation and AI-driven workflows.

A Practical AI Readiness Framework for Cybersecurity Leaders

This framework helps security leaders realistically assess their organization's readiness to adopt AI for alert investigation and security operations. Rather than waiting for perfect conditions, it can help define the company’s current state and determine practical next steps.

AI Readiness Self-Assessment Checklist

Rate your organization on these 10 critical factors (1=Not Started, 5=Mature):

• Data from critical security systems is accessible in one location
• Security analysts spend >50% of time on repetitive, high-volume tasks
• Team has identified specific processes that would benefit from AI
• Current manual processes have documented metrics (time, accuracy, etc.)
• At least one team member has experience with AI/ML technologies
• Core security tools have API or integration capabilities
• There's a process to evaluate AI decisions and provide feedback
• Leadership supports AI adoption with realistic expectations
• There's a basic understanding of AI strengths and limitations
• There's willingness to start small and scale based on results

Scoring:

• 40-50: Ready for comprehensive AI implementation
• 30-39: Ready for targeted AI use cases with clear boundaries
• 20-29: Ready for initial AI pilots with significant oversight
• 10-19: Focus on foundational elements before AI implementation

The organizations that have moved beyond the "readiness" paralysis are already seeing transformative results. For example, Eric Bohec, Chief Technical Officer at Nomios, a leading Pan-European MSSP supporting customers across the European continent, explains: "With Qevlar, we can rapidly analyse even the most complex of cases in just three minutes compared to the half hour it previously took, and we know its assessments will be accurate. Our SOC analysts are now “augmented analysts” — capable of accelerating response times while maintaining quality."

This improvement has helped Nomios, whose cybersecurity team spans 600 professionals, not only significantly reduce the turnover rate but also scale affectively since the company is now able to process more alerts (without increasing headcount and operational complexity).

Almond, a French MSSP with 450 experts in France and Switzerland and international service centers to ensure 24/7 operations, has also adopted Qevlar AI in their workflow. Julien Steunou, shared:

“We chose Qevlar for the SOC after testing several solutions. The challenge we wanted to meet was to be able to autonomously handle investigations and have a system that can deliver a verdict on an analysis, along with a confidence level for that verdict, so we canreuse it in subsequent processing. It complements well the automation setup we’ve had in place for a while so that detection and remediation can mostly happen at machine speed. In these scenarios, we can handle more than 80% of cases this way, and call in human experts only when the system isn’t confident in the verdict it provides. This makes everything fully integrated.That was the technical challenge Qevlar really delivered on.”

Bottom Line

Organizations succeeding with AI security tools start where they are, focus on specific high-value use cases, implement iteratively, measure impact, and scale progressively. The cost of waiting for perfect conditions far exceeds the cost of starting with imperfect but improving capabilities.

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