Introduction

In Q1 2026, India ranked as the second most targeted country across Asia and fifth worldwide.

Ransomware operators, initial access brokers, and hacktivist groups are all active in the region. Campaigns span financial services, manufacturing, government, and critical infrastructure. Common access methods include credential theft, exploitation of exposed systems, and reuse of compromised accounts.

The question facing security leaders is not whether they will be targeted. It is whether their operations are built to respond quickly and accurately enough when they are.

Most are not.

And the reason is not the tools. Security operations teams are not failing for lack of data. They are failing for lack of performance.

Tools get deployed but sit under-optimized. Detection logic is generic rather than environment-specific. Response is reactive rather than engineered. Coverage exists, but measurable improvement does not.

And when AI gets layered on top of this model, it does not fix it. It accelerates it. Faster noise is not better security.

SOC teams now receive an average of 2,992 security alerts per day, yet 63% go unaddressed. Forty percent are never investigated at all. Burnout is systemic: 71% of analysts report it and 64% are considering leaving their roles within a year.

Three structural failures drive this:

  1. Rotating teams that reset institutional knowledge with every handover.
  2. Detection logic built for the average customer rather than your specific environment.
  3. AI applied to a reactive model that produces more alerts faster rather than better outcomes.

According to the IBM Cost of a Data Breach Report 2025, organizations using AI and automation extensively saved an average of $1.9 million per breach and shortened the breach lifecycle by 80 days. The gap between operating with engineering discipline and without it is measurable, and it is growing.

The answer is not another tool. It is a different operating model.

One that moves security operations beyond coverage and toward continuous, measurable performance improvement.

At SecurityHQ, this is powered by AXCEL: Analyze, eXtract, Correlate, Execute, Learn. A closed-loop detection and response pipeline where every alert is refined, contextualised, acted on, and fed back so the system gets sharper with every incident.

AXCEL ingests raw telemetry across endpoint, cloud, identity, network, and dark web sources. It strips noise and suppresses benign activity before an analyst sees an alert. Detections are enriched against live threat intelligence and mapped to MITRE ATT&CK. Analyst-verified playbooks execute the response. And every outcome feeds back into detection logic so the system continuously improves.

AXCEL sits within SecurityHQ’s broader approach: Security Performance Engineering.

Security Performance Engineering is a continuous, engineered approach to improving the measurable performance of security operations. It is how SecurityHQ helps organizations move from “we are monitored” to “we can prove our security program is getting stronger over time.”

It is built on three pillars:

N-of-1 Security Engineering: Detection tuned to your specific environment. No templates. No forced platforms. No assumptions based on the “average” customer.

Continuous Performance Accountability: A designated team that builds context over time and takes ownership of outcomes, not just activity. The goal is not to process more tickets. It is to improve signal quality, detection accuracy, response speed, and control maturity over time.

Institutional Intelligence at Global Scale: 20+ years of learning across six global SOCs, applied to every customer environment from day one. Clients do not start from zero maturity; they inherit proven detection patterns, cross-industry threat intelligence, and operational learning refined across regions and sectors.

What this looks like in practice:

  • 62% lower noise-to-signal ratio compared to industry standard
  • 15-minute mean time to respond
  • 450+ security experts available globally across 6 SOCs
  • 20+ years of independent security operations

This was the focus of our recent roundtable with security leaders: AI only improves outcomes when it is embedded in an operating model designed for performance. Without environment-specific engineering, continuous accountability, and human-verified response, AI simply accelerates the same reactive workflows that already overwhelm SOC teams.

With threat actors operating across the region and targeting the gaps between tools, the question is no longer whether the environment is monitored.

It is whether the operations behind that monitoring are built to improve, continuously, around your specific business.

More alerts and more tools will not close that gap. A model built for measurable, engineered performance will.

To learn more about how SecurityHQ approaches Security Performance Engineering, or to continue the conversation from the roundtable, drop us a message here.