AI vs. AI: Gruve at ETCISO Decrypt 2026. Amit Kulkarni. Amit Kulkarni is VP Sales, APAC at Gruve. Gruve delivers AI-native cybersecurity services, such as autonomous SOC operations, detection and response, and security advisory for regulated enterprises across India, Singapore, UAE, and KSA. July 20, 2026. AI is now arming both sides of the enterprise SOC: the attackers probing it and the defenders running it. That tension anchored the conversation at ETCISO Decrypt 2026, where security leaders asked how they should respond. Gruve was on the stage. Amit Kulkarni, VP Sales, APAC at Gruve, joined the panel "AI vs AI: The New Cyber Battlefield" alongside senior security and risk leaders from Bank of Baroda, Star Health & Allied Insurance, and Aditya Birla Sun Life Insurance, moderated by ETCISO's editorial team. The shift from hours to minutes on both sides. Asked about the most significant change he observes in cybersecurity today, Amit drew a clear line between the old paradigm and the new. Security teams once spent hours, and, on occasions, even days, hunting for threats across fragmented tools and noisy alert queues. AI now enables detection, correlation, and anticipation in minutes, resulting in reduced MTTD and MTTR Then what is the challenge that needs immediate attention? Adversaries have the same capability. AI-powered attacks are probing enterprise environments at machine speed, identifying vulnerabilities and creating exploits faster than any human red team could. Organizations that treat AI adoption in their SOC as optional are at the highest risk of becoming targets of agentic AI-driven attacks. The only effective response is to adopt agentic capabilities within the SOC as early as 'Today'. AI cuts both ways in the SOC. Amit was direct in his interview with ETCISO, held on the sidelines of the event: AI is a double-edged sword in cybersecurity. The same technology that lets attackers move faster also changes the economics of defense, triaging alert volume, correlating signals across domains, and compressing response times. The future of cybersecurity, Amit predicted, will not hinge on who has AI. Everyone in the industry already does. It will be defined by how effectively organizations operationalize intelligent systems to outpace increasingly capable adversaries. The question that resonated in the room and reverberated through the industry leaders. Amit closed with a question to the audience that captured the tension every security leader is wrestling with: Where does the enterprise comfort line sit today between AI as a copilot and AI as an autonomous decision-maker in security operations? And how fast will that line move? The room's response made one thing clear: this has gone beyond a theoretical debate. CISOs are actively negotiating that line inside their organizations, with their boards, their regulators, and their own teams. The line is moving, and it is moving faster in some functions (alert triage, evidence collection) than others (containment authority, response actions). Gruve's position on this is well known to those who work with Gruve: AI runs the security. Gruve run the risk. Autonomous where it accelerates, human-validated where it matters, and audit-logged everywhere. For example, a model built for India-regulated environments where "AI-led" without "audit-ready" is a non-starter. The conversation continues. Decrypt 2026 reaffirmed what Gruve hear from security leaders across India, the Middle East, and Southeast Asia every week: the autonomous SOC has moved from a curiosity to a board-level agenda item. The questions have matured from "should we?" to "how fast, and with what guardrails?" If that's the conversation happening inside your organization, Gruve'd welcome the chance to contribute. Gruve's AI Cybersecurity Posture Assessment is a 30-minute working session with its senior security team. It's an honest read on your SOC operations, your AI readiness, and where autonomous capabilities would deliver the highest impact in your environment.

Breaking the 'intelligence ceiling': how Gruve and Fleak achieved Tier 3 threat detection fidelity. Arif Shaikh. February 23, 2026. Arif Shaikh is Head of AI Innovations, focused on AI-powered cybersecurity Where he leads AI in Security and Security for AI initiative. Also drives automation, efficiency, and measurable outcomes across teams, builds secure & scalable AI-driven operations. Standard pipelines waste 40% on "logic grinding." The Gruve + Fleak architecture ends the janitor work. With Fleak's Normalization Layer, raw logs from Zscaler and Okta become actionable intelligence in the Gruve Command Center. 1. The core discovery. This report documents a collaborative performance benchmark between Gruve and Fleak.ai, designed to explore the relationship between data architecture and AI-driven security outcomes. Gruve Inc. conducted a controlled A/B test to observe how a high-performance UEBA agent - the core of Gruve's AI SOC - performs when supported by different data pipeline configurations: raw heterogeneous logs vs. Fleak.ai-normalized OCSF data. The Conclusion: Data normalization is the primary catalyst for maximizing AI reasoning. The test demonstrated that while the Gruve AI-powered SOC engine is highly capable of processing raw logs, feeding it Fleak.ai-normalized OCSF allowed the model to bypass the "parsing phase" and move immediately to "high-fidelity analysis." This resulted in a shift from standard alert generation to the identification of complex, multi-stage threat patterns - achieving a Tier 3 Hunter performance profile without increasing operational latency or token costs. 2. Test design & performance overview. To ensure scientific validity, the test held all detection variables constant except for the data format. This was not a phased rollout, but a side-by-side comparison of the same dataset. A. The constants (control variables). * The Dataset: Identical Zscaler Private Access (ZPA) log set containing hidden attack patterns. * The AI Engine: The same Large Language Model (LLM) agent part of Gruve's AI-powered cybersecurity stack was used for both scenarios was used for both scenarios. * The Prompt: Identical System Prompt. The agent was given the exact same instructions: "You are an enterprise-grade UEBA engine. Analyze logs for behavioral anomalies, deviations, and insider threats. Do not use static IOCs." B. The variable (the change). * Scenario A (Baseline): Raw Logs. The AI ingested native, messy logs (mixed formats, inconsistent field names like Latitude vs. ClientLatitude). * Scenario B (Fleak.ai): OCSF Logs. The AI ingested the same data, normalized and enriched by Fleak.ai into the Open Cybersecurity Schema Framework (OCSF). Comparative performance summary: Raw Logs vs. Fleak.ai OCSF. 3. Operational efficiency & latency. A key finding is that the performance boost in Scenario B did not come at the cost of detection speed. * Runtime Latency: Negligible. For existing log types, Fleak.ai's normalization occurs in-stream with almost no processing overhead, preserving real-time detection capabilities. * New Log Onboarding: < 5 Minutes. For completely new, unknown event types, Fleak.ai's AI-native mapping agent generates end-to-end OCSF parsers in under 5 minutes. * Result: The system achieves structured data clarity at the speed of raw log ingestion. 4. Comparative detection results. The specific threat scenarios hidden within the dataset illustrate how the same AI agent interpreted the same events differently based on data format. This builds on the multi-agent threat hunting approach Gruve Inc. has deployed for enterprise clients, where data quality directly determines hunting depth. 5. Strategic implications. For a security product company, these results demonstrate that Data Quality is the ceiling for AI performance. * AI Cost Reduction: By normalizing data before it hits the AI model, you remove the need for the LLM to burn tokens parsing syntax. It focuses 100% of its context window on threat analysis. This directly addresses the alert fatigue and analyst overload challenges Gruve Inc. outlined in its piece on how AI is transforming SOC analyst workflows. * Agility: New data sources can be onboarded in minutes (via Fleak's AI mapping) rather than weeks of manual parser coding, without breaking downstream detection logic. * Reliability: Detection logic becomes schema-agnostic. A "Brute Force" prompt works for any identity provider (Okta, Azure AD, Zscaler) because they are all mapped to the same OCSF standard. Add Gruve Inc. to your mailing list

Gruve secures $50 million follow-on Series A financing. REDWOOD CITY - Gruve, a provider of AI services and infrastructure, announced the availability of more than 500 megawatts of distributed AI inference capacity across the United States. The company also secured a $50 million follow-on Series A financing to accelerate deployments, expand strategic partnerships, and scale its full-stack agentic services. The financing brings Gruve's total funding to $87.5 million and was led by Xora Innovation (backed by Temasek), with participation from Mayfield, Cisco Investments, Acclimate Ventures, AI Space and other strategic investors. The capital accelerates Gruve's ability to make low-latency AI inference capacity immediately available across Tier 1 and Tier 2 U.S. cities and scale efficiently as demand grows - without multi-year data center buildouts. The Execution Gap in AI As inference becomes the dominant AI workload, infrastructure has emerged as the industry's primary constraint. While models, agents, and hardware continue to see breakthroughs, the systems running them have not kept pace. Most production inference today relies on infrastructure that was never designed for low-latency, high-throughput, cost-sensitive AI, resulting in unsustainable costs, mounting technical debt, and weak unit economics. Gruve's Inference Infrastructure Fabric was built to close this gap. Inference Infrastructure Services Purpose-built for Production AI Workloads Gruve's Inference Infrastructure Fabric is a distributed platform engineered specifically for production-grade AI inference, delivering predictable latency, scalable throughput and industry leading economics. * 500MW+ of expandable U.S. capacity, leveraging excess power and existing infrastructure near Tier 1 and Tier 2 cities, enabled by long-term partnerships with Lineage, Inc. and other major colocation providers * Modular, high-density, rack-scale inference capacity, engineered for cost efficiency in inference-heavy workloads and rapid deployment * A distributed, low-latency edge fabric for seamless connectivity and workload orchestration across sites * Full-stack operations, including a 24x7 AI-powered SOC, network services, and cluster management to meet enterprise-grade reliability and performance standards Gruve is bringing 30MW live today across four U.S. sites, with additional capacity under development and further near-term expansions in Japan and Western Europe. This unique approach bypasses multi-year data center build cycles and delivers AI-ready capacity in months instead of years. "Gruve's Inference Infrastructure Fabric combines modular state-of-the-art pods with a distributed network architecture to enable rapid capacity deployment in power- available locations today - without compromising on latency. As demand for inference accelerates, scalable, low-latency infrastructure with strong unit economics is increasingly critical, and Gruve is well position to meet that need as it scales in 2026."