For much of the past decade, **Managed Detection and Response (MDR)** offered a crucial solution to a pervasive problem: security teams' inability to provide 24/7 coverage and staff enough analysts to manage an ever-growing alert queue. MDR stepped in, providing a valuable service. Yet, the current threat landscape, heavily influenced by artificial intelligence, is rapidly outpacing the traditional MDR model's capabilities. Attackers are now leveraging AI to accelerate operations, generate highly convincing phishing campaigns at scale, automate reconnaissance, and develop new malware variants that bypass signature-based detection. The attack surface has simultaneously expanded across endpoints, cloud environments, identity, and networks. Despite these shifts, many MDR services continue to operate largely as they always have: routing alerts to human analysts who triage them in a prioritized, often sequential, manner. This approach is no longer sufficient. Data indicates that many organizations may have outgrown their current MDR solutions. ## MDR's 24/7 Promise: A Closer Look at Alert Coverage While MDR promises 24/7 human coverage, what it often delivers is 24/7 human capacity to triage *high-severity* alerts. These are not interchangeable. Across the industry, approximately 60% of security alerts go unreviewed. This isn't a failure of effort but a limitation of human capacity. Both in-house and outsourced MDR teams simply cannot process the sheer volume of alerts generated by modern environments. Consequently, they prioritize, focusing on P1s and P2s while P3s and P4s accumulate untouched. Crucially, this is precisely where sophisticated attackers often hide. Analysis of 25 million alerts across global enterprises in 2025 revealed that nearly 1% of genuine threats originate from low-severity and informational alerts. For an enterprise generating 450,000 alerts annually, this translates to roughly 54 real incidents per year—about one per week—languishing in a deprioritized queue, completely unnoticed. These unaddressed breaches are not theoretical; they are actively occurring in organizations that believe they are fully covered. ## Inconsistent Investigation Quality Even for alerts that do receive attention, the quality of MDR investigations can be inconsistent. It is inherently tied to the experience of the analyst on duty, the current queue depth, the time of day, and team staffing levels. A P1 alert at 3 AM may receive a significantly different level of scrutiny than the same alert at 10 AM. This isn't a critique of MDR analysts themselves but an acknowledgement of the inherent variability in any human-executed process operating at high volume, under pressure, around the clock. The consequences are tangible: shallow investigations can lead to threats being misclassified as benign noise, and inconsistent follow-through can cause early-stage lateral movement to be overlooked as routine behavior. An attacker who gains initial access via a low-severity alert can continue to move undetected because no one had the time or context to connect the subtle signals. ## The Loophole in Detection Engineering In most MDR deployments, detection engineering is a periodic, rather than continuous, process. Rules are typically tuned only when customers report excessive alert volumes, or new coverage is added in response to major **CVEs** making headlines. Otherwise, the detection posture tends to drift. The core issue is architectural: MDR investigation and detection engineering often operate in separate silos. When an analyst investigates and closes an alert as a false positive, that critical insight rarely feeds back into the detection system. Broken rules persist, noisy rules continue to generate false positives, and new attacker techniques emerge without corresponding detections. This results in a detection posture that degrades faster than it improves, meaning actual coverage, when measured against frameworks like **MITRE ATT&CK**, can be significantly lower than organizations assume. ## The Black Box of Auditing and Transparency Many MDR services operate as a black box. Customers typically receive escalations and summary reports, but they rarely gain insight into the underlying investigation logic, the evidence trail, the verification process for verdicts, or what the analyst actually reviewed before closing a case. In an era where accountability and transparency are paramount security requirements, this lack of visibility presents a genuine liability. When an incident is missed, diagnosing the cause becomes impossible. When a verdict is incorrect, tracing the reasoning is difficult. And when regulators demand details on what was investigated and how, organizations may find themselves without adequate answers. ## AI Savings: Vendor Benefit, Not Customer Value AI is undeniably reducing the operational costs associated with MDR. Providers are leveraging AI to automate portions of triage, reduce analyst hours, and enhance profit margins. However, these efficiency gains are not consistently passed on to customers through lower prices or expanded coverage. Buyers often continue to pay the same rates, or even more, while the provider retains the savings. Crucially, the coverage gap and human scaling constraints remain unchanged. Only the provider's cost structure has improved. ## Lack of Ownership: Your Data, Their Knowledge Detection rules, triage logic, case history, and investigative insights accumulate within the MDR vendor's platform throughout the contract's duration. When the contract concludes, this valuable institutional knowledge does not transfer to the customer. Years of tuning, accumulated context about the environment, and detection improvements derived from the customer's data all remain with the vendor. This creates two significant problems: First, organizations switching providers are forced to start from scratch, rebuilding institutional knowledge that took years to develop. Second, organizations looking to bring security operations in-house—a growing trend as **AI SOC** tools mature—find themselves without a foundational knowledge base. MDR providers, understandably, are not incentivized to help customers build internal capabilities, as their business model depends on retaining these services. ## MDR Contracts and AI Readiness This knowledge lock-in is no longer merely a switching-cost issue; it's also an **AI** readiness problem. When deploying an AI agent for SOC work, it requires a robust knowledge foundation to reason effectively over—detection rules, case history, behavioral baselines, and forensic verdicts. If this crucial data resides exclusively within an MDR vendor's platform, the customer's AI agent starts from a significant disadvantage. ## Additional MDR Gaps Beyond these core issues, several smaller MDR gaps compound over time: * **Generic Playbooks**: Most customers receive the same generic playbook, regardless of their specific risk profile, compliance obligations, or data sensitivity. * **SOAR Integration Failures**: Integration tools like **SOAR** (Security Orchestration, Automation, and Response), intended to streamline MDR findings into internal workflows, have largely failed to deliver because human-driven investigations often do not produce the structured, consistent outputs required for effective automation. * **Limited Human Interaction**: During a real incident, when customers need to speak with someone who truly understands their environment, they often encounter an AI chatbot or a ticketing queue instead of a direct human connection. ## The Demands of the AI-Powered Attacker Era The attackers of 2026 are not waiting for alert queues to clear. AI-generated phishing campaigns are hitting inboxes at unprecedented volumes and levels of sophistication, bypassing conventional gateways. Credential stealers like **Agent Tesla** and **LummaC2** operate with speed. **EDR** tools are being actively evaded, with research indicating that more than half of confirmed compromised endpoints were already marked as "mitigated" by the EDR vendor. In many cases, the attacker has already achieved their objective before the defender even realizes a battle is underway. Addressing this new reality demands a fundamentally different operating model. One where investigation speed is measured in seconds, not hours. Where every alert, regardless of severity or time of day, is thoroughly examined. And where the output is an evidence-backed verdict, rather than an analyst's judgment call made under pressure. This is the promise of an **AI SOC**. ## An Operating Model Shift: AI Executes, Humans Supervise The core concept behind an AI SOC is straightforward: shift investigative execution from the human queue to AI, enabling humans to focus on critical decisions rather than time-consuming discovery. In practice, this means 100% of alerts—across endpoint, identity, cloud, network, phishing, and **SIEM**—are triaged and investigated automatically. Not sampled. Not filtered by severity. *All* of them. The AI applies the same forensic depth to a P4 alert at 3 AM that a senior analyst would dedicate to a P1 during business hours. Data from **Intezer**'s platform, across 25 million alerts, demonstrates the feasibility of this approach. Less than 2% of alerts required human escalation. The more than 98% that resolved autonomously did so with a sub-minute median triage time and 98% verdict accuracy. For a large enterprise generating 450,000 alerts annually, this translates to approximately 441,000 alerts fully investigated and resolved without human intervention, and crucially, 54 genuine threats that would have been missed under traditional MDR coverage are now caught with actionable remediation.