For nearly two decades, managed-security models have defined how most organizations handle detection and response.
Faced with alert overload, chronic staffing shortages, and the high cost of 24/7 coverage, many teams turned to Managed Security Service Providers (MSSPs) and later to Managed Detection and Response (MDR) vendors to fill the gap. Beyond staffing and capacity, many also lacked in-house expertise in building detection systems.
It was a rational choice. MSSPs and MDRs provided 24/7 monitoring, experienced analysts, and predictable coverage. They gave companies without an in-house SOC a viable way to maintain security coverage in an increasingly complex threat landscape.
But the ground has shifted.
AI-driven SOC platforms are now automating large parts of what human analysts once did: triaging alerts, correlating signals, enriching incidents, and recommending or even executing responses.
That raises a simple but profound question: what happens to the managed-security model when AI can perform much of that work internally, faster and more consistently?
The Outsourcing Workaround
MSSPs, SOC-as-a-Service vendors, and MDRs emerged to handle the growing flood of alerts and compliance demands overwhelming internal teams.
Managed providers have dominated this space for over a decade, evolving to fill most mid-market organizations' talent and coverage gap. According to Forrester's MDR Services Wave (Q1 2025), managed detection offerings remain central to many security strategies, but the model is increasingly strained by scale, cost, and visibility limits.
Beyond staffing and capacity, many organizations lacked the expertise to build detection systems, write custom rules for their environment, or keep pace with evolving attack techniques. MSSPs brought analysts and detection engineering teams that built and maintained the logic that identifies threats.
Why Outsourced Security Became a Standard
Outsourcing solved four critical problems: rising threat complexity, chronic talent shortages, compliance pressure, and the prohibitive cost of staffing a 24/7 SOC. This approach worked well for years, delivering coverage, structure, and a sense of operational maturity.
The Limitations of Outsourcing
As infrastructure expanded and threats accelerated, cracks appeared: high recurring costs (starting from $100K annually), shallow investigations by teams unfamiliar with each environment, unclear value proposition, and brutal response coordination handoffs.
When an MSSP/MDR detects something, you must coordinate with IT, legal, and business units. Accountability gaps remain, vendors detect and notify, but customers still bear breach consequences. Rotating analysts erase institutional memory while juggling dozens of clients, missing the critical nuance of environmental context.
The AI Shift
Modern AI-SOC platforms combine language models, correlation logic, and enrichment pipelines to perform Tier-1 investigations at machine speed, turning reactive security operations into continuous ones.
Precision Triage at Any Scale
Most alerts in a modern environment are duplicates or false positives. The SACR AI-SOC Landscape Report 2025 found that 40% of alerts go uninvestigated; of those reviewed, nine in ten are false positives.
AI-SOC platforms can process thousands of alerts in parallel, clustering related signals, correlating indicators, and automatically suppressing noise. That results in a concise queue of high-fidelity incidents, each with reasoning chains and evidence trails that analysts can verify.
These systems use large language models to interpret security telemetry, combined with correlation engines that link related events and enrichment pipelines that gather context from threat intelligence, asset databases, and historical patterns.
This redefines analyst work. Instead of combing through telemetry, teams focus on containment, communication, and post-incident learning.
For smaller security teams, it also frees time for proactive work, such as threat hunting, vulnerability reduction, and process improvements previously unnoticed.
A Sustainable Virtual SOC Architecture
Traditional 24/7 monitoring requires at least seven analysts rotating across three shifts.
AI now acts as a virtual Tier-1 layer, automatically handling enrichment, correlation, and initial triage, while human analysts supervise escalations and respond to real threats.
In a modern setup, AI can route confirmed, high-fidelity alerts directly to an on-call analyst through tools like PagerDuty, complete with context and evidence.
This allows smaller internal teams, often transitioning from MSSPs, to maintain 24/7 responsiveness without staffing full overnight shifts.
The result is round-the-clock coverage with a lean team, lower cost, less burnout, and coverage that scales with telemetry, not headcount.
Operational Impact
AI-SOC adoption removes the traditional labor bottleneck, allowing mid-sized organizations to achieve enterprise-grade visibility and speed. Human scale is no longer the ceiling for SOC performance.
What AI-SOC Doesn't Replace
AI-SOC platforms excel at triage and correlation, but don't replicate every MSSP/MDR capability.
Threat Intelligence and Threat Hunting: A handful of top-tier MSSPs aggregate threat intelligence across large client bases, spotting emerging attack patterns and novel TTPs that individual organizations might miss. However, most providers operate with limited or third-party intel feeds and focus primarily on monitoring and response, rather than active hunting.
As AI-SOC platforms evolve, many integrate external intelligence sources directly, giving internal teams similar visibility without depending on a managed service layer.
Hands-On Incident Response: AI automates investigation and can trigger containment actions. When you need forensic analysis, threat actor attribution, or coordinated remediation across complex environments, human IR expertise remains essential. Many MDRs provide retainer-based IR services that AI-SOC platforms don't replicate.
Compliance and Audit Support: MSSPs often provide formatted compliance reports, attestation letters, and audit support that satisfy regulators. AI-SOC platforms generate detailed audit trails but may require additional effort to map to specific compliance frameworks.
Who Should (and Shouldn't) Replace Their MSSP or MDR
Who Should Consider It
- SMB companies (200+ employees) with at least a small SOC or SecOps team
- Teams already using EDR, SOAR, or SIEM that can integrate automation
- Organizations with data-residency or compliance-visibility needs
- Enterprises aiming to reduce MTTR without scaling headcount
Who Shouldn't - Yet
- Small organizations with no security engineering talent. If you can't tune and maintain an AI-SOC, an MSSP/MDR is still better.
- Organizations that need 24/7 phone coverage for incident escalation.
- Even with AI, someone must be available when an alert escalates at 3 AM.
- Hybrid operations that include physical security.
- Organizations where compliance reporting is a primary driver (e.g., PCI-DSS, HIPAA attestation requirements).
- Teams lacking comprehensive telemetry - AI can't triage what it can't see
What Changes for Security Leaders
Adopting an AI-SOC changes the operating model. Leaders must rethink control (direct ownership of detections), cost (predictable spend vs. renewed labor contracts), people (analysts become system supervisors), and pace (high-fidelity alerts triaged in seconds, routine incidents contained in minutes).
Budget pressure reinforces this shift. The IANS Security Budget Benchmark 2025 reports that 54% of SOC leaders now face flat or shrinking budgets, a trend that pushes teams to flatten cost structures and reduce reliance on manual service models.
The Cost Equation
AI-SOC platforms' annual pricing typically starts at $30K, depending on log volume and feature set, and is substantially less than most MSSP contracts. Beyond cost savings, AI-SOC reduces risk by ensuring every alert is investigated, eliminating the coverage gaps caused by analyst overload, shift changes, and 40% of alerts traditionally uninvestigated. However, this excludes internal staffing (1-3 FTEs for tuning, oversight, and response coordination), integration costs, or the dual-spend period during transition. The business case is strongest for organizations already paying for SIEM, SOAR, and security staff, where AI-SOC consolidates and optimizes existing investments, rather than adding net-new infrastructure costs.
For many, the question is no longer "Can we build a SOC?"
It's becoming "Can we afford to outsource control of our threats?"
The Transition Path
The move toward AI-first operations doesn't happen overnight and doesn't look the same for every organization. Investing in transition, dual costs, integration effort, and process changes pays off through reduced risk exposure, faster threat detection, and regained control over your security posture. Organizations that complete the shift typically see a 60-80% reduction in mean time to respond and eliminate the coverage gaps that leave 40% of alerts uninvestigated.
Teams relying on MSSPs face a different journey than those already working with MDRs.
For Organizations Using MSSPs
The path depends on your current setup. If the MSSP provides log management, start by retaining visibility into your data through co-managed access or gradually migrating log ingestion in-house. Use AI to pre-filter and correlate alerts generated by your tools or the MSSP's platform. This reduces noise and ticket volume while maintaining coverage. Over time, progressively insource investigation and response tasks, relying on AI to extend coverage instead of adding shifts.
Note: during overlap, you're paying for both services.
For Organizations Using MDRs
MDR users already share some ownership of detection, making the transition more strategic than structural. Start by onboarding use cases outside your MDR scope: cloud workload alerts, SaaS application logs, or endpoints in development environments. As AI effectively triages these alerts, expand coverage into production systems currently monitored by the MDR.
This allows you to prove ROI before disrupting existing coverage. When renewal comes, renegotiate to focus MDR resources on specialized scenarios, advanced persistent threats, forensic analysis, and threat hunting, where human expertise adds the most value. The end state: internal teams drive routine response with AI assistance, while MDR providers act as escalation specialists for complex investigations.
Across both paths, the objective isn't a "fully autonomous SOC." It's a collaborative model where AI handles scale and speed, and human expertise provides oversight, context, and accountability.
De-Risking the Transition
Unlike MSSP engagements, which typically require contractual commitment before seeing results, AI-SOC platforms can demonstrate value through POC deployments.
A 30-60 day POC against your actual alert volume validates triage accuracy, false positive reduction, and integration quality before you make a decision.
When evaluating platforms, assess the same fundamentals you'd consider for any critical infrastructure: data portability (can you export detection logic and tuning?), integration standards (does it work with your existing tools?), and operational fit (does it match your team's skill set and workflows?).
The vendor relationship shifts from service provider to technology partner. You no longer evaluate analyst availability and SLAs.
You assess algorithm maturity, integration depth, and platform roadmap alignment for your security strategy.
Conclusion
Managed security providers are here to stay, but the operating model will definitely change. MSSPs and MDRs remain essential for organizations without internal capacity or strict compliance demands.
But AI-SOC platforms now provide greater visibility, control, and transparency to organizations that carry the risk. Managed providers are already shifting toward co-managed and hybrid models, where automation handles detection and human experts focus on escalation.
About Radiant: The new way of doing SOC
Radiant is pioneering a fresh approach to SOC operations. Its Agentic AI analysts process every alert, suppress false positives, and escalate only real threats with full investigation context and 1-click response for rapid containment. Integrated log management in the customer's cloud removes the scale, cost, and data ownership constraints of traditional SIEMs, making enterprise-grade security operations achievable for any organization.
Visit our website to learn more about us.
About the Author: Shahar Ben-Hador is the CEO and Co-founder of Radiant Security. He spent nearly a decade at Imperva, where he rose from IT Manager to become the company’s first CISO, experiencing the day-to-day challenges of running security operations. Later, as VP of Product Management at Exabeam, he led the building of the products he wished he had as a practitioner.
Shahar Ben-Hador — CEO and Co-founder at Radiant Security https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgHI2DlWbFATjVyhCBzh0cHwEN1FHSF6uSinlM-ynd6yVmuJ3IHJxjL1Ip-aHqoU6AzYK2briXjkoExqlMu08PuNbshh9LvcO_jRTrfj91S6OLC8CMtwky0Ne0TWbnmDEvTzcKTOu7yz7XMlH0cTAKUMztVcv7CBFfiHde82GLLdgHvz9t3vaaJDcGuBbk/s1600/Shahar.png
