Introduction

SecurityHQ has been named a Leader for the second consecutive year in the IDC MarketScape: Middle East Managed Detection and Response 2025 Vendor Assessment. IDC’s MarketScape evaluations assess providers across service delivery, operational maturity, and technology strategy. This recognition highlights how AI driven security investigation is enabling SOCs to move from raw detection to clearer, outcome-focused response. It reflects SecurityHQ’s commitment to delivering outcomes, not just coverage.

A key focus of IDC’s 2025 assessment was evaluating AI applications, examining not just adoption but how effectively it addresses core SOC challenges.

SecurityHQ’s differentiation is rooted in understanding of what SOCs actually need: not more tools or more detections, but more clarity. While many vendors focus AI capabilities on detection, increasing the volume and sophistication of threat identification, this approach assumes the primary challenge is visibility.

This is where AI driven security investigation becomes critical, shifting focus from alert volume to contextual understanding and decision support. 

SecurityHQ’s approach addresses the actual limiting factor in SOC performance, the ability to transform fragmented signals into clear, actionable intelligence. SecurityHQ operationalises this approach through SHQ Autopilot. Rather than sending analysts isolated alerts to correlate manually, the platform assembles related activity across environments into centralized, contextualised insight.

IDC’s assessment captured this distinction, highlighting that SecurityHQ’s emphasis on investigation clarity produces the outcomes security leaders care about: faster incident resolution, more defensible decisions, sustainable operations, not just broader visibility or more sophisticated detection for its own sake.

Why Investigation Breaks in Modern SOCs 

Security Operations Centers have expanded significantly in size, tooling, and coverage. Yet threats continue to bypass defences. Industry research consistently points to a gap between detection and action, where teams struggle to investigate alerts quickly enough to contain real threats. 

As environments grow across endpoints, networks, cloud platforms, identities, and applications, analysts are required to manually connect fragmented signals under constant time pressure. This slows investigations, introduces inconsistency, and creates opportunities for attackers to evade response. 

In many SOCs, analysts still review alerts one by one, determine relevance, and piece together activity across multiple tools. While this can work at smaller scales, it becomes difficult to sustain in high-volume environments generating thousands of alerts each day. 

AI has been widely adopted to improve detection, but increased alert volume alone does not solve investigation challenges. In many cases, it exacerbates them.   

Leveraging AI for Decision Clarity

What security teams need is not more alerts, but clearer context. Analysts need to understand which signals are related, how activity has unfolded over time, and whether a situation actually warrants response.

By connecting signals across time and environments, AI driven security investigation helps analysts understand how activity unfolds and whether it genuinely warrants response.

When telemetry from different security controls is analysed in isolation, that clarity is hard to achieve. When it is brought together and examined as a whole, investigation can shift from alert handling to behaviour-based understanding. 

Machine learning helps establish baselines and surface anomalies across time-series data. Large language models can then assemble related activity into readable investigative summaries, making it easier for analysts to understand what is happening and why it matters. 

“Modern SOCs don’t fail because they lack tools, they fail because they lack clarity. AI changes the investigation model by reducing noise, connecting activity across the environment, and allowing analysts to focus on threats that genuinely matter. When applied correctly, it becomes a force multiplier for both speed and decision quality,” said Aaron Hambleton, SVP MEA. 

SHQ Autopilot

SHQ Autopilot brings AI driven security investigation into daily SOC operations by correlating activity and assembling incidents as clear investigative narratives.

SHQ Autopilot uses AI and automation to qualify alerts, correlate related activity across users, hosts, IP addresses, and cloud resources, and assemble incidents as clear investigative narratives. Instead of presenting analysts with disconnected alerts, it provides a structured view of what happened, how activity progressed, and which assets or identities are involved. 

By embedding investigation logic directly into workflows, SHQ Autopilot reduces manual correlation and repetitive analysis. This supports earlier identification of multi-stage attacks and more consistent incident qualification, particularly in high-volume environments. 

Investigation outputs then flow directly into response. Context, enrichment, and mitigation guidance are attached to incidents, enabling automated containment actions where confidence thresholds are met. Where human judgement is required, analysts are supported with clearer information and recommended next steps. 

This allows analysts to focus on validation, decision making, and oversight, rather than reconstructing events across multiple tools.

What IDC Recognised

In its assessment, IDC highlighted SecurityHQ’s ability to consolidate telemetry across SIEM, EDR, NDR, and cloud sources through the SHQ Response platform, supported by a data fabric aligned to Open Cybersecurity Schema Framework standards. 

IDC also noted the role of SHQ Autopilot in generating contextualised incident storylines, applying workflow automation, and supporting investigation and triage, alongside ContainX for enabling automated containment actions. 

Together, these capabilities reflect an approach that prioritises investigation clarity, consistency, and expert-led response at scale. 

While AI and automation reduce friction and speed up workflows, experienced analysts remain essential for interpreting complex scenarios, applying business context, and overseeing response decisions. 

This balance allows SecurityHQ to scale operations without sacrificing transparency or control.

Conclusion

SecurityHQ’s recognition as an IDC MarketScape Leader reflects the strength of its MDR offering across multiple dimensions, including how investigation and response are handled in practice. As security environments continue to grow in scale and complexity, the ability to move from fragmented alerts to clear, defensible decisions becomes increasingly important. 

IDC’s assessment reinforces the value of approaches that prioritise investigation clarity, contextual understanding, and expert-led response. SecurityHQ’s continued recognition underscores its focus on producing measurable outcomes for its clients by enabling them with greater speed, clarity, and confidence in the face of evolving threats.