In this exclusive conversation, Zubair Chowgale, Sales Engineering Manager for APMEA at Securonix, discusses how the company is transforming modern security operations through cloud-native scalability, agentic AI and an open approach to SIEM architecture.
How does Securonix’s SIEM architecture differ from traditional SIEM platforms in terms of scalability, analytics, and data handling?
Legacy SIEM platforms were built for a different era. Their proprietary, database-centric architecture struggles to handle the scale, speed, and diversity of data generated by modern enterprises. As data volumes grow, these limitations lead to performance constraints, rising costs, and vendor lock-in, effectively restricting how organizations access and use their own security data.
Securonix Unified Defense SIEM is designed for the realities of today’s SOC. Built on an open architecture and powered by agentic AI, it is engineered to scale with each customer’s environment and threat landscape. Open by design and supported by extensible APIs, the platform integrates seamlessly across SIEM, SOAR, XDR, EDR, cloud, and on-premises ecosystems without forcing rip-and-replace decisions.
The platform is natively powered by Snowflake and AWS, delivering elastic scale, resilience, and performance. Its big data analytics and long-term retention capabilities enable organizations to ingest and analyze massive volumes of telemetry in real time while maintaining cost-efficient, long-term storage. By using an open data model, Securonix ensures data portability and long-term flexibility, giving customers full control over their security data as their needs evolve.
What specific UEBA (User & Entity Behavior Analytics) capabilities set Securonix apart from other SIEM/XDR vendors?
As cyberattacks grow more sophisticated, traditional rule-based security approaches are increasingly ineffective. They struggle to detect advanced threats and generate large volumes of false alerts that slow investigations and overwhelm security teams.
Securonix UEBA addresses this challenge by continuously analyzing user and entity behavior to identify anomalies, suspicious lateral movement, and insider threats across both cloud and on-premises environments. Built-in integrations and APIs provide visibility across major cloud platforms as well as critical security and business applications.
By applying machine learning and proven, out-of-the-box use cases, UEBA reduces noise and surfaces the highest-risk activity, allowing analysts to focus on what matters most. As part of the industry’s first Unified Defense SIEM powered by agentic AI, Securonix helps organizations reduce mean time to respond, stop threats faster, and deliver measurable security outcomes that stand up at the board level.
How does Securonix integrate with existing security stacks EDR, IAM, cloud platforms (AWS/Azure/GCP), threat intel feeds and what native integrations are strongest?
Securonix is designed to work with existing security ecosystems rather than replace them. The platform uses an open architecture and extensible APIs to integrate across EDR, IAM, cloud, and threat intelligence sources, allowing organizations to preserve prior investments while improving visibility and response.
At the endpoint and identity layers, Securonix integrates with leading EDR and IAM platforms to ingest telemetry such as authentication activity, privilege changes, endpoint behavior, and access patterns. This data is enriched and correlated through behavioral analytics to detect insider threats, compromised credentials, and lateral movement that point solutions often miss in isolation.
For cloud environments, Securonix provides deep, native integrations with AWS, Azure, and Google Cloud. These integrations collect identity, audit, network, and workload telemetry to deliver unified visibility across hybrid and multi-cloud environments. Cloud activity is analyzed alongside on-premises data to establish consistent behavioral baselines and detect anomalous access or data movement.
Securonix also integrates with a broad range of commercial and open-source threat intelligence feeds. Threat indicators are contextualized within user, entity, and activity data, improving detection fidelity and reducing false positives. Native integrations are strongest in areas where behavioral context matters most, including identity and access data, cloud audit logs, and endpoint activity. This allows security teams to move beyond isolated alerts and gain a more complete, risk-based view of threats across the enterprise.
What is your approach to AI/ML-driven detection? How do you reduce false positives while maintaining high detection fidelity?
AI and machine learning give SOC teams a critical advantage in today’s high-pressure threat landscape. At Securonix, AI is designed to augment analysts by handling the operational workload that slows detection and response, allowing teams to focus on real risk.
The Securonix Unified Defense SIEM continuously analyzes data streams to identify anomalies and suspicious activity that traditional, rule-based controls often miss. By converting raw telemetry into contextualized insights in real time, the platform significantly reduces false positives and analyst fatigue. Alerts are enriched with identity, asset, network, and activity context, giving security teams a clearer and more complete view of risk without manual correlation.
Securonix threat chains connect related activity over time, linking indicators of compromise with attacker tactics, techniques, and procedures to uncover patterns associated with advanced and insider threats. This behavioral analytics approach prioritizes high-fidelity alerts, reduces noise at scale, and enables faster, more confident response. In practice, customers reduce false positives by up to 90 percent and lower SIEM operating costs by more than 50 percent, allowing security teams to operate more efficiently and effectively.
How do you support hybrid and multi-cloud environments, and what visibility do you provide across cloud identities, data access, and workloads?
Securonix supports hybrid and multi-cloud environments through a cloud-native, SaaS-based SIEM platform built on AWS and Snowflake. It delivers unified security monitoring, behavioral analytics, and automated response across cloud and on-premises environments, providing a consistent view of risk regardless of where data or workloads reside.
As organizations accelerate cloud adoption, many struggle to balance agility with security. Gaps in visibility, unclear ownership of assets, data privacy concerns, and inconsistent access controls often introduce risk that traditional tools were not designed to address. These challenges are amplified in hybrid and multi-cloud environments where data and activity are fragmented across platforms.
Securonix addresses these limitations through deep, two-way integrations with cloud infrastructure and applications. Using API-based connectivity, the platform ingests and correlates identity, activity, and configuration data from AWS, Azure, Google Cloud, and on-premises systems to eliminate blind spots and improve detection accuracy. With more than 350 built-in cloud connectors, Securonix simplifies data collection and response, enabling security teams to monitor cloud activity continuously, detect abnormal behavior, and respond quickly with confidence.
What does the Securonix Marketplace offer today? Which pre-built content packs are most valuable for SOC teams?
The Securonix Marketplace extends the value of the platform by providing ready-to-use integrations, detections, and response content that help SOC teams move faster without custom development. It is designed to simplify onboarding of new data sources, accelerate detection coverage, and reduce the operational effort required to maintain security content over time.
Today, the Marketplace offers a broad range of pre-built content, including connectors, parsers, detection use cases, enrichment logic, dashboards, and response workflows. This allows teams to quickly integrate security, cloud, identity, and business applications while ensuring telemetry is normalized and immediately usable for analytics and investigation.
The most valuable content packs for SOC teams tend to focus on areas of highest operational impact. Identity and access monitoring packs are widely adopted because they improve visibility into authentication activity, privilege misuse, and insider risk. Cloud and SaaS content packs for platforms such as AWS, Azure, and Google Cloud help teams monitor user behavior, configuration changes, and data access across hybrid environments. Endpoint and network-focused packs add behavioral context that strengthens detection of lateral movement and advanced threats.
Threat intelligence enrichment and automated response content are also highly valued, as they help reduce noise and speed investigation by providing context and guided actions directly within the analyst workflow. Together, these pre-built packs allow SOC teams to expand coverage quickly, improve detection quality, and focus more time on responding to meaningful risk rather than building and maintaining content from scratch.
How does Securonix handle long-term log retention and analytics without increasing storage or compute costs excessively?
Securonix is designed to support long-term log retention and analytics without forcing organizations to trade cost for visibility. The platform uses a cloud-native architecture that separates storage and compute, allowing each to scale independently based on operational needs rather than peak demand.
Built on Snowflake and AWS, Securonix enables organizations to retain large volumes of security telemetry while maintaining efficient performance. Data can be stored in an optimized, open format and accessed on demand, allowing teams to run advanced analytics and investigations without keeping all data in high-cost, always-on compute tiers.
Through Data Pipeline Manager, customers have fine-grained control over how telemetry is ingested, processed, and stored. This includes deciding which data requires real-time analysis, which data can be retained for compliance or forensics, and where it should live across hot and long-term storage tiers. This flexibility helps reduce unnecessary ingestion, storage, and processing costs while preserving analytic depth.
As a result, security teams can maintain extended retention periods for compliance and investigation, perform historical analysis when needed, and control costs as data volumes grow. The outcome is predictable spend, scalable analytics, and long-term visibility without excessive storage or compute overhead.
What is your roadmap around autonomous SOC capabilities, AI-driven investigation, automatic enrichment, correlation, and SOAR integrations?
We don’t typically comment on detailed product roadmap timelines, but we can share the direction we’re moving in and the principles guiding our innovation.
Our focus is on advancing autonomous SOC capabilities in a way that delivers real operational value while keeping humans in control. This includes deeper AI-driven investigation, more intelligent automatic enrichment, stronger correlation across identities, assets, and activity, and tighter integration between detection and response through SOAR. The goal is to reduce manual effort, accelerate decision-making, and improve consistency across security operations.
We have several exciting new capabilities in the works that build on these foundations. These enhancements are designed to further streamline investigations, improve detection fidelity, and automate routine response actions without introducing risk or loss of oversight. As always, autonomy is being applied deliberately, with transparency and explainability at the core, so security teams can move faster with confidence rather than surrender control.
What matters most to us is outcomes. Every capability we introduce is aimed at helping SOC teams reduce response times, operate more efficiently at scale, and stay ahead of increasingly sophisticated threats.
For customers migrating from legacy SIEMs (e.g., Splunk, QRadar, ArcSight), what does the transition timeline and data ingestion strategy typically look like?
There is no single transition timeline when moving from a legacy SIEM. Each customer’s timeline is different and is driven by their specific needs, priorities, and readiness.
Some organizations move quickly, onboarding key data sources and shifting detections within a short timeframe to address immediate visibility or operational gaps. Others take a more gradual approach, running platforms in parallel for longer periods to meet internal change management, compliance, or operational requirements. Factors such as data volume, regulatory obligations, internal processes, and team maturity all influence the pace of transition.
The focus is on aligning the migration timeline with what works best for the customer, allowing them to adopt Securonix at a speed that supports their business and security objectives rather than forcing a fixed or one-size-fits-all schedule.
What differentiates Securonix’s Managed Services (Securonix Analytics-Driven MDR) and how does it reduce SOC workload compared to standard MSSP offerings?
The next phase of managed security will be defined by analytics-driven detection, disciplined data economics, and agentic AI that scales with customer demand. Securonix is built to support this shift. Our Unified Defense SIEM enables MSSPs to deliver stronger outcomes with greater efficiency, deeper visibility, and predictable economics across multi-tenant environments.
The platform is grounded in three core principles. First, analytics-first detection reduces noise at the source by focusing on behavior and risk rather than raw event volume. Second, intelligent data control aligns cost with value, giving providers flexibility in how data is ingested, retained, and analyzed. Third, agentic AI accelerates investigation and response, allowing services to scale based on outcomes rather than analyst headcount.
Together, these capabilities allow MSSPs to move beyond commoditized alert monitoring toward differentiated, outcome-driven services. Behavioral analytics and entity-centric detection significantly reduce false positives, improving signal quality and analyst productivity. This enables providers to offer premium MDR tiers, vertical-specific services, and outcome-based SLAs that customers are willing to invest in.
By applying agentic AI within defined guardrails, MSSPs can speed investigations, enrich context, and execute response actions with consistency and control. The result is the ability to support more customers, respond faster, and improve service quality without scaling SOC headcount linearly.