As the AI SOC category establishes maturity, various pricing models have emerged, each with its tradeoffs. Some vendors’ prices are based on alert volume, others on the number of AI analyst hours, and more. Security leaders must understand how each model functions and impacts the organization’s risk posture as they evaluate these options. This is essential to ensuring they can achieve an AI SOC’s true promise: to detect, investigate, and respond to threats faster.
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Comparing AI SOC Pricing Models
For CISOs navigating a rapidly evolving market, pricing isn’t just a procurement detail—it’s a strategic decision. How an AI SOC solution is priced impacts your budget and shapes your detection strategy. That said, as early-stage AI SOC vendors look for scalable pricing structures, many are adopting models reminiscent of MDR providers. While some of these approaches may offer initial savings, they can also limit long-term visibility and security outcomes if not carefully implemented. Here’s how the most common pricing approaches compare:
- Alert Volume-Based Pricing: This model is straightforward to meter and may cost less upfront. However, alert volumes can be challenging to predict, so this approach can incentivize alert suppression and could limit visibility into emerging threats.
- AI Analyst Time-Based Pricing: This model reflects investigative effort and promotes efficiency. On the downside, it’s often just a proxy for alert volume and may discourage complete, thorough analysis of incoming alerts.
- Custom-Quote Pricing: This flexible approach enables AI SOC vendors to deliver tailored solutions to unique environments. However, it often lacks transparency, making comparing vendors or predicting costs difficult.
- Endpoint-Based Pricing: This model is predictable, scales with your infrastructure, and supports comprehensive alert ingestion. It also requires upfront scoping, as you’ll want to ensure an accurate count of your endpoints to ensure total alert coverage.
At Intezer, we base our pricing on endpoint count, not alert volume or AI agent time. While every model has its tradeoffs, this approach empowers our customers to ingest all alerts, regardless of severity. That way, customers don’t have to worry about exceeding quotas or facing surprise overage costs. The result is not only full threat coverage but also predictable pricing that aligns with the scale of your environment, regardless of alert volume ebbs and flows.
More importantly, this model reinforces our belief that cost should never limit alert visibility. If the goal of an AI SOC is to detect what humans miss or don’t have time to investigate, then limiting what AI agents can triage and investigate defeats the purpose.
Why Total Alert Coverage—Regardless of Severity—Is Critical
When adopting new technology, a common instinct is to narrow the scope and start with a subset of data. That’s why some vendors recommend starting with high-severity alerts so new customers can dip their toes into the AI SOC waters while remaining within alert quotas. While the logic makes sense, it’s flawed, as critical alerts are only one piece of the cybersecurity puzzle. And while high-severity alerts are essential, they’re often already receiving analyst attention.
Agentic AI’s true advantage lies in validating high-severity alerts and identifying subtle anomalies in lower-severity alerts that can indicate nefarious activity. These quieter alerts can frequently contain the early signs of compromise, so AI must be applied across the full alert spectrum.
What We’ve Seen in the Wild
In the past three months alone, about 0.6% of all informational, low, and medium-severity alerts we ingested led to escalations. This represents over 10% of all the alerts we escalated. That’s significant. These real threats would have slipped through if we’d only focused on high-severity alerts. Included below are some examples of medium and low-security alerts we’ve escalated.
Example 1: “Mitigated” Medium-Severity Alert Leaves Endpoint Infected with Malware
Intezer’s AI SOC deeply investigates each alert it encounters. In this example, the alert came from CrowdStrike EDR, and Intezer chose to escalate it.
The original information from the alert source indicates threat mitigation, so it was assigned a medium-severity level.
Mitigated cases, especially those with lower severity, are usually overlooked.
Intezer extracted all available evidence from the host and conducted a memory forensic scan to identify any possible remaining infections.
Analyzing the associated files and command line artifacts confirmed an infection attempt, while the memory scan confirmed an active infection currently on this host.
This case was escalated to the customer’s security team for response and containment within a few minutes of the detection, compared to the lower priority it would have received otherwise.
Furthermore, the quick analysis enabled us to accurately identify what happened on the machine. The malware here was a stealer. Stealers exfiltrate data from the machine and leave without being detected. If this alert had sat in a queue for hours before an analyst had reviewed it, the stealer would have likely been long gone without a trace by the time anyone started the investigation. This would make the response process much more complicated, but Intezer was instead able to notify the SOC team that a user just had their credentials and session cookies stolen.
This scenario isn’t unique to CrowdStrike; we have seen similar cases with all the major EDR providers.
Example 2: “Low-Severity” Custom Identity Detection Leaves Brute-Force Attempt Undiscovered
A SIEM detection in Microsoft Sentinel triggered the low-severity alert below based on a login attempt from a suspicious IP.
Intezer escalated the alert by investigating the associated network artifacts and the relevant identity logs.
AI analysis of the logs identified an attempted brute-force attack on the user, which requires further investigation and response.
These findings reinforce our core belief: you can’t afford to assume low severity equals low risk. Our approach helps customers eliminate alert backlogs while ensuring deep and thorough investigations of every alert.
Starting with a sample of high-severity alerts or benchmarking against AI analyst time might seem efficient, but it often amounts to omission. And in cybersecurity, what you don’t see or don’t investigate can hurt you. An AI SOC solution should process the bulk of the alerts to find the 10% of cases worth escalating because humans alone don’t have the time.
Can you afford to ignore 10% of real events? Explore our product tour to see how you can achieve total alert coverage.
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