Managed Detection and Response (MDR) was supposed to solve one of cybersecurity’s biggest problems: the lack of time, talent, and expertise to investigate alerts and stop threats quickly.
Yet, after years of adoption, the same pain points keep surfacing across enterprises, no matter which MDR provider they use.
It’s not that MDR doesn’t work. It’s that the MDR model has hit its scalability ceiling.
And that’s exactly where the AI SOC comes in.
The persistent pain points with MDR
Even top-tier MDR services face systemic limitations, mostly rooted in their heavy reliance on human analysts and manual workflows.
Slow speed of investigation and escalation
Traditional MDRs still depend on people manually triaging and escalating alerts. When alerts are high-volume and context is shallow, triage slows down. Minutes or hours pass before something is even reviewed — and attackers don’t wait.
Inconsistent quality of service
Service quality often depends on who’s on shift. A senior analyst might quickly recognize a benign admin script, while a newer one escalates it as suspicious. Customers experience inconsistency and noise, not confidence.
“Alert regurgitation” with too many escalations
Many MDRs escalate too many alerts back to the customer, simply because their analysts can’t safely close them. Without deep forensic capabilities or contextual reasoning, they pass the decision back creating more work for internal security teams, not less.
Shallow coverage (only high severity alerts)
Because human bandwidth is finite, most MDRs only analyze high-severity alerts. Low-severity anomalies where early indicators of compromise often hide, are ignored.
The black box problem
Customers rarely know why an alert was raised or dismissed. MDR reports often lack transparency, context, or traceability. You’re asked to trust the verdict but you can’t validate it.
These issues aren’t about vendor quality; they’re about model design. The traditional MDR is inherently limited by human capacity and rule-based logic.
Detection engineering is the heart of security operations
At the center of every MDR’s capability is detection engineering, the practice of building, tuning, and maintaining the rules and analytics that proactively identify threats.
Detection engineering defines:
- What threats are visible.
- How fast they’re detected.
- How accurate detections are.
- How much noise analysts must deal with.
Strong detection engineering means faster time-to-detect and fewer false positives. Weak detection engineering means missed attacks and alert fatigue.
It is, without question, the intellectual property core of every MDR.
But detection engineering the old way doesn’t scale
Traditional detection engineering is slow, manual, and fragile.
Rules take time to create and validate. New TTPs require new detections, which must be tested, tuned, and redeployed, all by hand. And rules decay quickly. Attackers evolve, environments change, telemetry shifts and yesterday’s rules stop working tomorrow.
Furthermore, detection coverage is opaque. Most internal SOCs can’t quantify exactly which ATT&CK techniques they’re covered for.
Finally, testing is inconsistent. Manual QA means some detections are solid, others are noisy.
This leads to more false positives which drain MDR analysts. The more data collected, the more noise generated and the slower their response.
In MDR models, detection engineering is a constant treadmill: write > tune > test > break > repeat. Ad nauseam.
Explore this article on making sense of the AI SOC market.
The paradigm shift of the AI SOC
The AI SOC represents a fundamental shift from reactive, human-bounded detection to autonomous, hybrid intelligence that combines human reasoning with machine precision.
Instead of relying solely on analysts, the AI SOC uses automation, behavioral modeling, and language models to perform much of the investigative heavy lifting.
Here’s how it solves the core MDR pain points:
Instant investigation at scale
AI can automatically collect context, process ancestry, file reputation, network behavior, identity usage, and reason across it in seconds. What once took an MDR analyst 30 minutes now takes milliseconds.
Consistency without fatigue
LLMs and behavioral models don’t have “bad days” or “night shifts.” They deliver consistent quality, 24/7. Every alert is triaged against the same logic, evidence, and reasoning patterns.
Contextual decisions, not alert regurgitation
Rather than escalating uncertain alerts back to customers, the AI SOC enriches and explains them. It can summarize findings, justify verdicts, and give clear remediation guidance.
Broader, deeper coverage
Because AI triage scales infinitely, it can analyze low- and medium-severity alerts that MDRs typically ignore, surfacing early-stage threats before they escalate.
Full transparency
AI SOCs can log every step of their reasoning and decision path. Customers see not only the verdict, but the why behind it. A true end to the black box.
Intezer’s edge: Deep forensics meets AI reasoning
At Intezer, we believe AI alone is not enough. Large Language Models are powerful, but on their own, they’re probabilistic, great at language and context, but not inherently trustworthy for high-stakes decisions.
That’s why Intezer’s AI SOC is built on the hybrid foundation of deterministic forensic analysis with LLM flexibility and intuition. Read more in this blog, Why the best LLMs are not enough for the AI SOC.
Deep forensic heritage
Intezer’s AI SOC is built on a forensic-grade understanding of system behavior, a foundation that goes far beyond traditional detection or endpoint analytics.
Attackers today often operate without dropping malware at all. They use stolen credentials, exploit vulnerabilities or misconfigurations, and leverage legitimate tools like PowerShell, WMI, or RDP to move laterally and persist. Detecting these techniques requires more than just pattern matching. It demands a forensic view of what’s really happening inside the system.
That’s where Intezer’s deep forensic heritage comes in. Our platform continuously analyzes execution at multiple layers, from code and memory to processes, persistence mechanisms, and network communication, to reconstruct the full behavioral narrative of an incident.
When required, Intezer can even execute suspicious payloads in our controlled sandbox environment to capture precise behavioral indicators such as process creation, memory injection, and command-line actions. But that’s just one piece of a broader forensic ecosystem tightly integrated with static analysis, code reuse detection, memory forensics, and runtime telemetry.
This multi-layered forensic engine gives the AI SOC deterministic truth. A complete, evidence-based understanding of activity whether or not malware is involved. It’s how Intezer’s AI can confidently assess not just what ran, but why it ran, who executed it, and how it fits into an adversary’s tactics.
LLMs emulating human analysts as the reasoning layer
On top of that forensic backbone, we apply large language models to:
- Correlate telemetry and behavioral chains.
- Explain detections in plain language.
- Assess intent and business impact.
- Generate new detection logic dynamically from emerging TTPs.
The LLM provides intuition; the forensics provide ground truth. Together, they deliver what MDRs have always lacked. Speed, consistency, transparency, and trust.
Redefining detection engineering with AI
Even what MDRs once considered their “core IP”, detection engineering, is being transformed by AI SOC architecture. Here is how:
AI-assisted detection creation: LLMs can generate draft Sigma or YARA rules from threat reports in seconds.
Automated validation: The system tests detections against known TTPs and real-world telemetry.
Adaptive tuning: AI adjusts thresholds and correlations based on live data, minimizing noise.
Forensic-backed confidence: Every detection is validated against Intezer’s code intelligence and endpoint forensics.
This turns detection engineering from an artisanal, reactive process into a continuous, self-improving capability.
The bottom line
The MDR market won’t disappear overnight, but its limitations are becoming clear.
Customers want faster investigations, consistent quality, and full transparency.
They want a SOC that doesn’t just watch alerts, but understands them.
That’s exactly what the AI SOC delivers.
And with Intezer’s unique combination of deterministic forensics and AI reasoning, we’re not just matching MDR capabilities. We’re surpassing them, including in the area once considered MDR’s crown jewel: detection engineering.
The AI SOC isn’t the next version of MDR. It’s what comes after it.
Itai Tevet
