In line with the Bahrain Economic Vision 2030, leading Bahraini bank enhances AI experiences for its clients Al Salam Bank has signed a strategic deal with Denodo, a global leader in data management, AWS, and NAIB IT, a Bahrain-based systems integrator known for delivering high-impact technology solutions across banking, government, public sector, and enterprise organizations. The agreement aims to adopt the Denodo platform to amplify the Bank’s data and AI infrastructure, in line with Bahrain’s Vision 2030 and the national direction toward digital transformation. The signing ceremony was attended by Shaikha Dr. Dheya Bint Ebrahim Al Khalifa, Managing Director at NAIB IT;  Mr. Anwar Murad, Deputy CEO – Banking at Al Salam Bank, Mr. Hemantha Wijesinghe, CTO at Al Salam Bank; and Mr. Gabriele Obino, Denodo Regional Vice President South Europe and Middle East and General Manager Denodo Arabian Limited. Through the Denodo platform, Al Salam Bank will be able to unify its enterprise data across various systems, enabling faster decision-making and driving innovation. This step also reflects the Bank’s commitment to leading innovation in digital banking, in line with the Kingdom of Bahrain’s long-term economic vision. Shaikha Dr. Dheya Bint Ebrahim Al Khalifa stated, “This strategic collaboration represents a significant milestone in Bahrain’s digital transformation journey. We are happy to facilitate partnerships that advance our nation’s technological capabilities and strengthen our position as a regional fintech hub. Through initiatives like this, we are building the foundation for a knowledge-based economy that aligns with Bahrain’s Vision 2030.” “At Al Salam Bank, we are committed to remaining at the forefront of digital transformation within the financial sector,” said Anwar Murad, Deputy CEO – Banking at Al Salam Bank. “This strategic partnership with Denodo and NAIB IT marks a significant step in advancing our digital maturity and optimizing the use of data and AI to better serve our clients. By harnessing real-time data integration and AI-powered analytics, we aim to enhance responsiveness, strengthen operational agility, and deliver a more personalized and seamless banking experience. This initiative goes beyond technology adoption—it represents our dedication to embedding intelligence into core operations, enabling informed decision-making and positioning Al Salam Bank as a forward-looking institution aligned with the aspirations of Bahrain’s Vision 2030.” “This partnership reflects our vision to build a smarter, more agile bank powered by advanced data and AI capabilities. We believe this initiative will not only enhance the clients experience but also set a benchmark for innovation in the region,” said Hemantha Wijesinghe, CTO at Al Salam Bank. Al Salam Bank has signed a strategic agreement with Denodo and NAIB IT to advance its data management and AI initiatives through AWS Marketplace, enabling faster procurement, cloud-native scalability, and real-time access to data products to accelerate innovation. The agreement forms a key pillar in Al Salam Bank’s broader digital transformation roadmap, reinforcing its position at the forefront of smart banking in the region. With the Denodo Platform’s logical data management capabilities including a universal semantic layer, Al Salam Bank can connect and manage data from its core systems, cloud-based services, and fintech partners, within minutes instead of weeks. The interoperability among the different systems will enable AI-powered analytics and reporting, enabling faster, data-driven decisions at the executive and operational levels. Commenting on the partnership, Gabriele Obino, regional vice president and general manager, Southern Europe and Middle East at Denodo, stated, “We are proud to support Al Salam Bank in its digital transformation journey. Our platform enables real-time data access, governance, and agility, critical components for AI success. This partnership showcases how modern data management can empower financial institutions to lead in a rapidly evolving digital economy.” “As a local integrator, our mission is to ensure that global innovation translates into local success, said Ebrahim Sonde, COO at NAIB IT. “Collaborating with Al Salam Bank and Denodo, we are committed to delivering a robust, secure, and scalable data architecture that drives meaningful transformation.” By adopting the Denodo Platform’s logical data management layer and leveraging NAIB IT’s deployment expertise, the Bank expects further enhancements in operational efficiency, regulatory compliance, and service agility. Real-time access to data will not only empower teams with faster insights but also elevate the end-user experience. In embracing this transformation, Al Salam Bank reinforces its position as a technology-forward institution, aligned with the aspirations of Bahrain’s Vision 2030 and prepared to lead in a future defined by intelligent financial services.

The 2024 State of the Data Lakehouse survey shows that 84%of large-enterprise data leaders have already fully or partially implemented data-mesh practices, and 97% expect those initiatives to expand this year. Jay Krishnan welcomes the shift but cautions that “a mesh built on orphaned lineage and blind spots in privacy will collapse under its own compliance debt.” Jay Krishnan’s Background in Distributed Data Governance Jay Krishnan is known for turning data-mesh theory into production patterns that auditors sign off. His recent projects include a petabyte-scale feature platform that maps lineage across six business units, a column-level encryption scheme that meets regional privacy law, and an open-source contribution adding policy tags to Apache Iceberg metadata. Peers value his knack for combining catalog precision with low-latency analytical paths. Why Federation Challenges Feature Stores Feature engineering often starts in a domain team then migrates to a central platform. Lineage can snap when files are copied or when tables are refactored into new formats. Jay Krishnan warns that personal data risks climb just as quickly. “If a customer hash sneaks into a marketing feature, you inherit GDPR fines overnight.” A governed data mesh must therefore guarantee three things at read time Provenance for every feature column Automatic masking or tokenization of PII Contract enforcement across domain boundaries Architectural Blueprint Domain layer Each business unit stores features in its own lake table using Iceberg or Delta. Column metadata includes owner, sensitivity flag, and logical data type. Shared catalog A global Glue or Unity catalog registers every table pointer. A lineage service writes edge records whenever Spark or Flink pipelines transform a column. Policy engine Open Policy Agent evaluates read requests. Rules combine sensitivity flag with caller identity. PII columns are either masked, tokenized, or blocked. Access broker Arrow Flight or Delta Sharing serves feature sets. Requests carry a signed JWT that lists approved columns. The broker strips unauthorized fields before the parquet scan. Observability loop Every query emits a lineage delta and a policy verdict to Kafka. A nightly batch reconciles graph completeness and raises an alert if an edge or policy tag is missing. All traffic is encrypted in transit. Keys live in a partitioned KMS with separate master keys per domain. Pilot Metrics A six-week pilot joined four domains in a retail group. Key results: Lineage completeness reached 96% of columns up from 62%. Mean feature-read latency rose from 95 to 117 milliseconds, still inside the 200 millisecond SLA. Privacy scanner logged zero PII leakage events; baseline had averaged three per month. Infrastructure added two c5.4xlarge catalog nodes and one m5.4xlarge OPA cluster. Cost increase stayed under four percent of the analytics budget. Trade-offs and Mitigations Latency overhead. Policy checks add about twenty milliseconds per call. Jay Krishnan mitigated this by caching allow lists for low-sensitivity feature groups. Metadata drift. Developers occasionally forgot to tag new columns. A pre-merge Git hook now blocks schema files missing owner or sensitivity labels. Cross-zone data egress. A misconfigured share pushed data between regions. The broker now rejects requests that cross residency boundaries unless an exemption tag is present. “Governance is code. Anything left to tribal knowledge breaks within a sprint,” Jay Krishnan notes. Governance Controls that Satisfied Audit Feature lineage graph stored in Neptune with daily completeness check Column sensitivity tags backed by a change-management ticket Quarterly access review exported to the data-protection office in CSV These steps met both internal policy and external privacy-law requirements. Leadership Perspective Jay Krishnan offers three lessons for senior data leaders: A data mesh only scales if lineage travels with the feature, not the file location. Policy decisions must happen at read path milliseconds, not in separate workflows. Governance cost stays modest when metadata and enforcement move with the platform code. “Central warehouses solve control by turning every request into the same query,” he concludes. “A federated mesh solves it with portable lineage and machine-speed policy. That is how you keep agility without inviting regulatory heat.” For CTOs who want domain autonomy yet cannot risk privacy breaches, the pattern shows that feature store federation and strong governance can coexist in the same architecture today.

Share Share Share Email A 2024 Google Cloud benchmark shows that a pre-warmed Cloud Run service equipped with an NVIDIA L4 GPU can become ready in about five seconds, then scale to thousands of containers in a single region. Jay Krishnan recently used the same capability for a Fortune 500 client, steering just over ten thousand concurrent training tasks without maintaining a permanent GPU cluster. “Serverless used to be glue,” Jay Krishnan says. “Now it is a control plane that can burst-allocate GPUs faster than any fixed cluster we have ever owned.” Jay Krishnan’s Track Record in Large-Scale AI Infrastructure In this space, Jay Krishnan is widely regarded as an authority on secure, large-scale AI platforms. Over the past decade, he has led cloud engineering teams that automated disaster-recovery drills across multiple regions with zero downtime, designed regulator-approved confidential-computing stacks for financial services, and authored reference blueprints on burst GPU training that are cited by industry groups focused on sustainable compute. He is a regular speaker at regional cloud summits, where his talks center on elastic AI and governance. His recent collaboration with senior leadership at NAIB IT Consultancy W.L.L, where the General Manager – AI & Cybersecurity oversees emerging AI infrastructure and cybersecurity practices across Dubai and Bahrain, reflects the growing importance of scalable, stateless architectures in enterprise innovation. Why Burst Training Needs a Stateless Control Plane Traditional trainers reserve GPUs for hours even when most time is lost to I/O or gradient exchange. Jay Krishnan argues that workloads such as prompt tuning, vector embedding, and contrastive learning gain little from that model. “Each sample is independent,” he explains. “Compute should appear for ninety seconds, finish its tensor math, then disappear.”The team therefore designed an orchestration layer where Cloud Run or Lambda issues shards, tracks metadata, and releases capacity the moment a task completes. Architectural Blueprint Dispatch layer: Cloud Run services or Lambda functions read job manifests from Pub/Sub or SQS, slice them into micro-batches, and push task IDs into Redis. Worker layer: GPU containers run on GKE, AWS Batch, or a small Slurm pool. A worker pulls a task, downloads the mini-dataset from Cloud Storage or S3, performs the forward or backward pass, and writes the result to object storage. Aggregation layer: A lightweight Cloud Function collects partial outputs, applies a reduce step if required, and stores the updated model artefact. Mutual TLS protects every hop. A run hash in each call binds logs, code digest, data URI, and GPU type for later audit. Cold-Start Economics Pre-warmed Cloud Run revisions keep GPUs in parking mode and deliver first-byte latency near thirteen seconds. Lambda handles orchestration only, so its response stays in the millisecond range. GPU nodes are spot instances that join or leave the pool every few minutes according to queue depth. Jay Krishnan reports a 38% cost reduction compared with a dedicated cluster that idles between peaks. Failure Modes and Their Fixes Three issues surfaced during the pilot: Task duplication appeared when Redis visibility timeouts expired before kernel completion; longer timeouts and idempotent writes removed the problem. Burst throttling on Lambda triggered at roughly thirty-five thousand invocations a minute; using two extra regions and adding jitter smoothed throughput. Version drift occurred when container tags diverged from dataset hashes; digest pinning and SHA-based data URLs eliminated mismatches. “Five-digit concurrency forces discipline,” Jay Krishnan notes. “Retry logic, idempotency, and strict versioning are no longer optional.” Governance at Scale Every task writes a JSON envelope that records container digest, data URI, GPU SKU, runtime, and exit status. A nightly batch reconciles envelopes with object-store manifests; discrepancies open a PagerDuty ticket. Security blocks any image older than ninety days through an admission policy. Leadership Perspective Jay Krishnan distills his lessons into three key takeaways for senior engineering leaders: Serverless functions can coordinate GPU bursts at enterprise scale while keeping control-plane latency low. Cold-start penalties are manageable; warm pools and snapshotting keep latency acceptable for batch workloads. Governance remains intact through automated metadata capture, region caps, and image-age policies. As one executive from NAIB IT Consultancy W.L.L remarked, “this model aligns perfectly with our vision of agile and cost-efficient AI deployment across borders.” “We treat GPUs as a transient utility,” Jay Krishnan concludes. “When training ends, the fleet dissolves. Finance gets a lower bill, security trusts the isolation model, and scientists iterate without waiting.” For CTOs dealing with spiky training demand and idle cluster cost, the evidence shows that serverless GPU orchestration has moved from prototype to production reality.

A January 2024 white paper from Microsoft’s Office of the Chief Economist reported a 22% drop in task duration for experienced SOC analysts using Security Copilot. Jai, who advises a Fortune 500 security operations center, says that integrating retrieval-augmented LLMs into their triage workflow produced even sharper results. “We cut more than half the minutes out of every triage,” Jai shares. “The average alert dropped from eleven minutes to under five.” These results, he says, came not from generative chat, but from disciplined engineering decisions that gave the model access only to what it needed, nothing more. Jai’s Background in Large-Scale Cyber Analytics In this space Jai is recognized for turning research into production platforms that pass enterprise audit. Over the past decade he has built log pipelines that handle tens of petabytes each month, introduced zero-trust controls across multi-cloud SOCs, and authored reference blueprints on retrieval-augmented detection cited by industry working groups on AI for cyber defence. Colleagues respect his blend of data-engineering rigor and focus on measurable analyst productivity, qualities that underpin the results described here. Retrieval Comes Before Reasoning The real bottleneck in threat hunting, Jai explains, is narrowing down petabytes of logs into the few kilobytes that matter. “You don’t want the model guessing. You want it reading the right five lines.” His team implemented three core retrieval strategies: chunking logs into ~300-token blocks for better recall, embedding those with metadata like timestamps and MITRE tags, and enforcing a refresh cadence of under five seconds for high-velocity sources like auth logs. Two Calls, Not One Instead of direct prompting, the architecture separates retrieval from reasoning. A gRPC service first fetches the top-k relevant events, which are then passed into a tightly scoped prompt. “The model only sees curated context. It’s cheaper, faster, and audit-safe,” Jai notes. That setup ensures flat costs per query, evidence-cited output, and a cacheable retrieval layer keeping end-to-end latency under 300 milliseconds. A Prompt That Refuses to Wander Open chat is banned. The template exposes four short fields: Indicator, Context, Hypothesis, Recommended Action. Temperature sits at zero point one. A post-run checker discards any reply lacking a quoted evidence line. “If the model cannot ground its claim, we never see it,” Jai notes. Scoring That Integrates Seamlessly The model outputs a triage score between zero and one hundred. Alerts above eighty are promoted into a fast lane already trusted by human analysts. After eight weeks, the SOC reported 70% agreement between model scores and analyst decisions, while false escalations remained under 3%. Hardware Footprint Remains Modest In the pilot, a global manufacturer indexed thirty days of Sentinel, CrowdStrike, and Zeek telemetry, around 1.2 billion vectors in total. The system ran on four NVIDIA A10G nodes for vector search and a single L4 cluster for prompt inference. No other infrastructure was modified. Across the same window: Mean triage time dropped from 11.4 to 4.6 minutes Daily analyst throughput rose from 170 to 390 alerts False positive rate remained unchanged Governance Keeps Trust Intact Evidence retention. Every retrieved snippet and generated answer is stored with the incident ticket. Version freeze. The model stays fixed for ninety days; upgrades rerun calibration tests before release. Role boundary. Only tier-two analysts may convert model advice into automated remediation steps. “These gates satisfy audit without slowing the flow,” Jai says. The Leadership Perspective Retrieval-augmented language models remove roughly sixty percent of manual triage time when search, prompt, and governance are engineered together. Gains depend on three design choices: event-level chunking with rich metadata, a clear two-step search then reason pattern, and a prompt that enforces evidence citation. Hardware cost stays low because the system uses commodity GPU nodes for vectors and a small inference cluster. “We did not chase artificial chat magic,” Jai concludes. “We treated the model as a microservice, fed it hard context, and tied every suggestion to a line of log. The speed gain is measurable and the audit trail is airtight.” For CTOs seeking more coverage from the same headcount, Jai’s data shows that retrieval-augmented LLMs are ready for production testing today.

Read More In line with the Bahrain Economic Vision 2030, leading Bahraini bank enhances AI experiences for its clients Al Salam Bank has signed a strategic deal with Denodo, a global leader in data management, AWS, and NAIB IT, a Bahrain-based systems integrator known for delivering high-impact technology solutions across banking, government, public sector, and enterprise organizations. The agreement aims to adopt the Denodo platform to amplify the Bank’s data and AI infrastructure, in line with Bahrain’s Vision 2030 and the national direction toward digital transformation. The signing ceremony was attended by Shaikha Dr. Dheya Bint Ebrahim Al Khalifa, Managing Director at NAIB IT;  Mr. Anwar Murad, Deputy CEO – Banking at Al Salam Bank, Mr. Hemantha Wijesinghe, CTO at Al Salam Bank; and Mr. Gabriele Obino, Denodo Regional Vice President South Europe and Middle East and General Manager Denodo Arabian Limited. Through the Denodo platform, Al Salam Bank will be able to unify its enterprise data across various systems, enabling faster decision-making and driving innovation. This step also reflects the Bank’s commitment to leading innovation in digital banking, in line with the Kingdom of Bahrain’s long-term economic vision. Shaikha Dr. Dheya Bint Ebrahim Al Khalifa stated, “This strategic collaboration represents a significant milestone in Bahrain’s digital transformation journey. We are happy to facilitate partnerships that advance our nation’s technological capabilities and strengthen our position as a regional fintech hub. Through initiatives like this, we are building the foundation for a knowledge-based economy that aligns with Bahrain’s Vision 2030.” “At Al Salam Bank, we are committed to remaining at the forefront of digital transformation within the financial sector,” said Anwar Murad, Deputy CEO – Banking at Al Salam Bank. “This strategic partnership with Denodo and NAIB IT marks a significant step in advancing our digital maturity and optimizing the use of data and AI to better serve our clients. By harnessing real-time data integration and AI-powered analytics, we aim to enhance responsiveness, strengthen operational agility, and deliver a more personalized and seamless banking experience. This initiative goes beyond technology adoption—it represents our dedication to embedding intelligence into core operations, enabling informed decision-making and positioning Al Salam Bank as a forward-looking institution aligned with the aspirations of Bahrain’s Vision 2030.” “This partnership reflects our vision to build a smarter, more agile bank powered by advanced data and AI capabilities. We believe this initiative will not only enhance the clients experience but also set a benchmark for innovation in the region,” said Hemantha Wijesinghe, CTO at Al Salam Bank. Al Salam Bank has signed a strategic agreement with Denodo and NAIB IT to advance its data management and AI initiatives through AWS Marketplace, enabling faster procurement, cloud-native scalability, and real-time access to data products to accelerate innovation. The agreement forms a key pillar in Al Salam Bank’s broader digital transformation roadmap, reinforcing its position at the forefront of smart banking in the region. With the Denodo Platform’s logical data management capabilities including a universal semantic layer, Al Salam Bank can connect and manage data from its core systems, cloud-based services, and fintech partners, within minutes instead of weeks. The interoperability among the different systems will enable AI-powered analytics and reporting, enabling faster, data-driven decisions at the executive and operational levels. Commenting on the partnership, Gabriele Obino, regional vice president and general manager, Southern Europe and Middle East at Denodo, stated, “We are proud to support Al Salam Bank in its digital transformation journey. Our platform enables real-time data access, governance, and agility, critical components for AI success. This partnership showcases how modern data management can empower financial institutions to lead in a rapidly evolving digital economy.” “As a local integrator, our mission is to ensure that global innovation translates into local success, said Ebrahim Sonde, COO at NAIB IT. “Collaborating with Al Salam Bank and Denodo, we are committed to delivering a robust, secure, and scalable data architecture that drives meaningful transformation.” By adopting the Denodo Platform’s logical data management layer and leveraging NAIB IT’s deployment expertise, the Bank expects further enhancements in operational efficiency, regulatory compliance, and service agility. Real-time access to data will not only empower teams with faster insights but also elevate the end-user experience. In embracing this transformation, Al Salam Bank reinforces its position as a technology-forward institution, aligned with the aspirations of Bahrain’s Vision 2030 and prepared to lead in a future defined by intelligent financial services.

A January 2024 white paper from Microsoft’s Office of the Chief Economist reported a 22% drop in task duration for experienced SOC analysts using Security Copilot. Jai, who advises a Fortune 500 security operations center, says that integrating retrieval-augmented LLMs into their triage workflow produced even sharper results. “We cut more than half the minutes out of every triage,” Jai shares. “The average alert dropped from eleven minutes to under five.” These results, he says, came not from generative chat, but from disciplined engineering decisions that gave the model access only to what it needed, nothing more. Jai’s Background in Large-Scale Cyber Analytics In this space Jai is recognized for turning research into production platforms that pass enterprise audit. Over the past decade he has built log pipelines that handle tens of petabytes each month, introduced zero-trust controls across multi-cloud SOCs, and authored reference blueprints on retrieval-augmented detection cited by industry working groups on AI for cyber defence. Colleagues respect his blend of data-engineering rigor and focus on measurable analyst productivity, qualities that underpin the results described here. Retrieval Comes Before Reasoning The real bottleneck in threat hunting, Jai explains, is narrowing down petabytes of logs into the few kilobytes that matter. “You don’t want the model guessing. You want it reading the right five lines.” His team implemented three core retrieval strategies: chunking logs into ~300-token blocks for better recall, embedding those with metadata like timestamps and MITRE tags, and enforcing a refresh cadence of under five seconds for high-velocity sources like auth logs. Two Calls, Not One Instead of direct prompting, the architecture separates retrieval from reasoning. A gRPC service first fetches the top-k relevant events, which are then passed into a tightly scoped prompt. “The model only sees curated context. It’s cheaper, faster, and audit-safe,” Jai notes. That setup ensures flat costs per query, evidence-cited output, and a cacheable retrieval layer keeping end-to-end latency under 300 milliseconds. A Prompt That Refuses to Wander Open chat is banned. The template exposes four short fields: Indicator, Context, Hypothesis, Recommended Action. Temperature sits at zero point one. A post-run checker discards any reply lacking a quoted evidence line. “If the model cannot ground its claim, we never see it,” Jai notes. Scoring That Integrates Seamlessly The model outputs a triage score between zero and one hundred. Alerts above eighty are promoted into a fast lane already trusted by human analysts. After eight weeks, the SOC reported 70% agreement between model scores and analyst decisions, while false escalations remained under 3%. Hardware Footprint Remains Modest In the pilot, a global manufacturer indexed thirty days of Sentinel, CrowdStrike, and Zeek telemetry, around 1.2 billion vectors in total. The system ran on four NVIDIA A10G nodes for vector search and a single L4 cluster for prompt inference. No other infrastructure was modified. Across the same window: Mean triage time dropped from 11.4 to 4.6 minutes Daily analyst throughput rose from 170 to 390 alerts False positive rate remained unchanged Governance Keeps Trust Intact Evidence retention. Every retrieved snippet and generated answer is stored with the incident ticket. Version freeze. The model stays fixed for ninety days; upgrades rerun calibration tests before release. Role boundary. Only tier-two analysts may convert model advice into automated remediation steps. “These gates satisfy audit without slowing the flow,” Jai says. The Leadership Perspective Retrieval-augmented language models remove roughly sixty percent of manual triage time when search, prompt, and governance are engineered together. Gains depend on three design choices: event-level chunking with rich metadata, a clear two-step search then reason pattern, and a prompt that enforces evidence citation. Hardware cost stays low because the system uses commodity GPU nodes for vectors and a small inference cluster. “We did not chase artificial chat magic,” Jai concludes. “We treated the model as a microservice, fed it hard context, and tied every suggestion to a line of log. The speed gain is measurable and the audit trail is airtight.” For CTOs seeking more coverage from the same headcount, Jai’s data shows that retrieval-augmented LLMs are ready for production testing today.

A January 2024 white paper from Microsoft’s Office of the Chief Economist reported a 22% drop in task duration for experienced SOC analysts using Security Copilot. Jai, who advises a Fortune 500 security operations center, says that integrating retrieval-augmented LLMs into their triage workflow produced even sharper results. “We cut more than half the minutes out of every triage,” Jai shares. “The average alert dropped from eleven minutes to under five.” These results, he says, came not from generative chat, but from disciplined engineering decisions that gave the model access only to what it needed, nothing more. Jai’s Background in Large-Scale Cyber Analytics In this space Jai is recognized for turning research into production platforms that pass enterprise audit. Over the past decade he has built log pipelines that handle tens of petabytes each month, introduced zero-trust controls across multi-cloud SOCs, and authored reference blueprints on retrieval-augmented detection cited by industry working groups on AI for cyber defence. Colleagues respect his blend of data-engineering rigor and focus on measurable analyst productivity, qualities that underpin the results described here. Retrieval Comes Before Reasoning The real bottleneck in threat hunting, Jai explains, is narrowing down petabytes of logs into the few kilobytes that matter. “You don’t want the model guessing. You want it reading the right five lines.” His team implemented three core retrieval strategies: chunking logs into ~300-token blocks for better recall, embedding those with metadata like timestamps and MITRE tags, and enforcing a refresh cadence of under five seconds for high-velocity sources like auth logs. Two Calls, Not One Instead of direct prompting, the architecture separates retrieval from reasoning. A gRPC service first fetches the top-k relevant events, which are then passed into a tightly scoped prompt. “The model only sees curated context. It’s cheaper, faster, and audit-safe,” Jai notes. That setup ensures flat costs per query, evidence-cited output, and a cacheable retrieval layer keeping end-to-end latency under 300 milliseconds. A Prompt That Refuses to Wander Open chat is banned. The template exposes four short fields: Indicator, Context, Hypothesis, Recommended Action. Temperature sits at zero point one. A post-run checker discards any reply lacking a quoted evidence line. “If the model cannot ground its claim, we never see it,” Jai notes. Scoring That Integrates Seamlessly The model outputs a triage score between zero and one hundred. Alerts above eighty are promoted into a fast lane already trusted by human analysts. After eight weeks, the SOC reported 70% agreement between model scores and analyst decisions, while false escalations remained under 3%. Hardware Footprint Remains Modest In the pilot, a global manufacturer indexed thirty days of Sentinel, CrowdStrike, and Zeek telemetry, around 1.2 billion vectors in total. The system ran on four NVIDIA A10G nodes for vector search and a single L4 cluster for prompt inference. No other infrastructure was modified. Across the same window: Mean triage time dropped from 11.4 to 4.6 minutes Daily analyst throughput rose from 170 to 390 alerts False positive rate remained unchanged Governance Keeps Trust Intact Evidence retention. Every retrieved snippet and generated answer is stored with the incident ticket. Version freeze. The model stays fixed for ninety days; upgrades rerun calibration tests before release. Role boundary. Only tier-two analysts may convert model advice into automated remediation steps. “These gates satisfy audit without slowing the flow,” Jai says. The Leadership Perspective Retrieval-augmented language models remove roughly sixty percent of manual triage time when search, prompt, and governance are engineered together. Gains depend on three design choices: event-level chunking with rich metadata, a clear two-step search then reason pattern, and a prompt that enforces evidence citation. Hardware cost stays low because the system uses commodity GPU nodes for vectors and a small inference cluster. “We did not chase artificial chat magic,” Jai concludes. “We treated the model as a microservice, fed it hard context, and tied every suggestion to a line of log. The speed gain is measurable and the audit trail is airtight.” For CTOs seeking more coverage from the same headcount, Jai’s data shows that retrieval-augmented LLMs are ready for production testing today.

A January 2024 white paper from Microsoft’s Office of the Chief Economist reported a 22% drop in task duration for experienced SOC analysts using Security Copilot. Jai, who advises a Fortune 500 security operations center, says that integrating retrieval-augmented LLMs into their triage workflow produced even sharper results. “We cut more than half the minutes out of every triage,” Jai shares. “The average alert dropped from eleven minutes to under five.” These results, he says, came not from generative chat, but from disciplined engineering decisions that gave the model access only to what it needed, nothing more. Jai’s Background in Large-Scale Cyber Analytics In this space Jai is recognized for turning research into production platforms that pass enterprise audit. Over the past decade he has built log pipelines that handle tens of petabytes each month, introduced zero-trust controls across multi-cloud SOCs, and authored reference blueprints on retrieval-augmented detection cited by industry working groups on AI for cyber defence. Colleagues respect his blend of data-engineering rigor and focus on measurable analyst productivity, qualities that underpin the results described here. Retrieval Comes Before Reasoning The real bottleneck in threat hunting, Jai explains, is narrowing down petabytes of logs into the few kilobytes that matter. “You don’t want the model guessing. You want it reading the right five lines.” His team implemented three core retrieval strategies: chunking logs into ~300-token blocks for better recall, embedding those with metadata like timestamps and MITRE tags, and enforcing a refresh cadence of under five seconds for high-velocity sources like auth logs. Two Calls, Not One Instead of direct prompting, the architecture separates retrieval from reasoning. A gRPC service first fetches the top-k relevant events, which are then passed into a tightly scoped prompt. “The model only sees curated context. It’s cheaper, faster, and audit-safe,” Jai notes. That setup ensures flat costs per query, evidence-cited output, and a cacheable retrieval layer keeping end-to-end latency under 300 milliseconds. A Prompt That Refuses to Wander Open chat is banned. The template exposes four short fields: Indicator, Context, Hypothesis, Recommended Action. Temperature sits at zero point one. A post-run checker discards any reply lacking a quoted evidence line. “If the model cannot ground its claim, we never see it,” Jai notes. Scoring That Integrates Seamlessly The model outputs a triage score between zero and one hundred. Alerts above eighty are promoted into a fast lane already trusted by human analysts. After eight weeks, the SOC reported 70% agreement between model scores and analyst decisions, while false escalations remained under 3%. Hardware Footprint Remains Modest In the pilot, a global manufacturer indexed thirty days of Sentinel, CrowdStrike, and Zeek telemetry, around 1.2 billion vectors in total. The system ran on four NVIDIA A10G nodes for vector search and a single L4 cluster for prompt inference. No other infrastructure was modified. Across the same window: Mean triage time dropped from 11.4 to 4.6 minutes Daily analyst throughput rose from 170 to 390 alerts False positive rate remained unchanged Governance Keeps Trust Intact Evidence retention. Every retrieved snippet and generated answer is stored with the incident ticket. Version freeze. The model stays fixed for ninety days; upgrades rerun calibration tests before release. Role boundary. Only tier-two analysts may convert model advice into automated remediation steps. “These gates satisfy audit without slowing the flow,” Jai says. The Leadership Perspective Retrieval-augmented language models remove roughly sixty percent of manual triage time when search, prompt, and governance are engineered together. Gains depend on three design choices: event-level chunking with rich metadata, a clear two-step search then reason pattern, and a prompt that enforces evidence citation. Hardware cost stays low because the system uses commodity GPU nodes for vectors and a small inference cluster. “We did not chase artificial chat magic,” Jai concludes. “We treated the model as a microservice, fed it hard context, and tied every suggestion to a line of log. The speed gain is measurable and the audit trail is airtight.” For CTOs seeking more coverage from the same headcount, Jai’s data shows that retrieval-augmented LLMs are ready for production testing today.

A January 2024 white paper from Microsoft’s Office of the Chief Economist reported a 22% drop in task duration for experienced SOC analysts using Security Copilot. Jai, who advises a Fortune 500 security operations center, says that integrating retrieval-augmented LLMs into their triage workflow produced even sharper results. “We cut more than half the minutes out of every triage,” Jai shares. “The average alert dropped from eleven minutes to under five.” These results, he says, came not from generative chat, but from disciplined engineering decisions that gave the model access only to what it needed, nothing more. Jai’s Background in Large-Scale Cyber Analytics In this space Jai is recognized for turning research into production platforms that pass enterprise audit. Over the past decade he has built log pipelines that handle tens of petabytes each month, introduced zero-trust controls across multi-cloud SOCs, and authored reference blueprints on retrieval-augmented detection cited by industry working groups on AI for cyber defence. Colleagues respect his blend of data-engineering rigor and focus on measurable analyst productivity, qualities that underpin the results described here. Retrieval Comes Before Reasoning The real bottleneck in threat hunting, Jai explains, is narrowing down petabytes of logs into the few kilobytes that matter. “You don’t want the model guessing. You want it reading the right five lines.” His team implemented three core retrieval strategies: chunking logs into ~300-token blocks for better recall, embedding those with metadata like timestamps and MITRE tags, and enforcing a refresh cadence of under five seconds for high-velocity sources like auth logs. Two Calls, Not One Instead of direct prompting, the architecture separates retrieval from reasoning. A gRPC service first fetches the top-k relevant events, which are then passed into a tightly scoped prompt. “The model only sees curated context. It’s cheaper, faster, and audit-safe,” Jai notes. That setup ensures flat costs per query, evidence-cited output, and a cacheable retrieval layer keeping end-to-end latency under 300 milliseconds. A Prompt That Refuses to Wander Open chat is banned. The template exposes four short fields: Indicator, Context, Hypothesis, Recommended Action. Temperature sits at zero point one. A post-run checker discards any reply lacking a quoted evidence line. “If the model cannot ground its claim, we never see it,” Jai notes. Scoring That Integrates Seamlessly The model outputs a triage score between zero and one hundred. Alerts above eighty are promoted into a fast lane already trusted by human analysts. After eight weeks, the SOC reported 70% agreement between model scores and analyst decisions, while false escalations remained under 3%. Hardware Footprint Remains Modest In the pilot, a global manufacturer indexed thirty days of Sentinel, CrowdStrike, and Zeek telemetry, around 1.2 billion vectors in total. The system ran on four NVIDIA A10G nodes for vector search and a single L4 cluster for prompt inference. No other infrastructure was modified. Across the same window: Mean triage time dropped from 11.4 to 4.6 minutes Daily analyst throughput rose from 170 to 390 alerts False positive rate remained unchanged Governance Keeps Trust Intact Evidence retention. Every retrieved snippet and generated answer is stored with the incident ticket. Version freeze. The model stays fixed for ninety days; upgrades rerun calibration tests before release. Role boundary. Only tier-two analysts may convert model advice into automated remediation steps. “These gates satisfy audit without slowing the flow,” Jai says. The Leadership Perspective Retrieval-augmented language models remove roughly sixty percent of manual triage time when search, prompt, and governance are engineered together. Gains depend on three design choices: event-level chunking with rich metadata, a clear two-step search then reason pattern, and a prompt that enforces evidence citation. Hardware cost stays low because the system uses commodity GPU nodes for vectors and a small inference cluster. “We did not chase artificial chat magic,” Jai concludes. “We treated the model as a microservice, fed it hard context, and tied every suggestion to a line of log. The speed gain is measurable and the audit trail is airtight.” For CTOs seeking more coverage from the same headcount, Jai’s data shows that retrieval-augmented LLMs are ready for production testing today.

A January 2024 white paper from Microsoft’s Office of the Chief Economist reported a 22% drop in task duration for experienced SOC analysts using Security Copilot. Jai, who advises a Fortune 500 security operations center, says that integrating retrieval-augmented LLMs into their triage workflow produced even sharper results. “We cut more than half the minutes out of every triage,” Jai shares. “The average alert dropped from eleven minutes to under five.” These results, he says, came not from generative chat, but from disciplined engineering decisions that gave the model access only to what it needed, nothing more. Jai’s Background in Large-Scale Cyber Analytics In this space Jai is recognized for turning research into production platforms that pass enterprise audit. Over the past decade he has built log pipelines that handle tens of petabytes each month, introduced zero-trust controls across multi-cloud SOCs, and authored reference blueprints on retrieval-augmented detection cited by industry working groups on AI for cyber defence. Colleagues respect his blend of data-engineering rigor and focus on measurable analyst productivity, qualities that underpin the results described here. Retrieval Comes Before Reasoning The real bottleneck in threat hunting, Jai explains, is narrowing down petabytes of logs into the few kilobytes that matter. “You don’t want the model guessing. You want it reading the right five lines.” His team implemented three core retrieval strategies: chunking logs into ~300-token blocks for better recall, embedding those with metadata like timestamps and MITRE tags, and enforcing a refresh cadence of under five seconds for high-velocity sources like auth logs. Two Calls, Not One Instead of direct prompting, the architecture separates retrieval from reasoning. A gRPC service first fetches the top-k relevant events, which are then passed into a tightly scoped prompt. “The model only sees curated context. It’s cheaper, faster, and audit-safe,” Jai notes. That setup ensures flat costs per query, evidence-cited output, and a cacheable retrieval layer keeping end-to-end latency under 300 milliseconds. A Prompt That Refuses to Wander Open chat is banned. The template exposes four short fields: Indicator, Context, Hypothesis, Recommended Action. Temperature sits at zero point one. A post-run checker discards any reply lacking a quoted evidence line. “If the model cannot ground its claim, we never see it,” Jai notes. Scoring That Integrates Seamlessly The model outputs a triage score between zero and one hundred. Alerts above eighty are promoted into a fast lane already trusted by human analysts. After eight weeks, the SOC reported 70% agreement between model scores and analyst decisions, while false escalations remained under 3%. Hardware Footprint Remains Modest In the pilot, a global manufacturer indexed thirty days of Sentinel, CrowdStrike, and Zeek telemetry, around 1.2 billion vectors in total. The system ran on four NVIDIA A10G nodes for vector search and a single L4 cluster for prompt inference. No other infrastructure was modified. Across the same window: Mean triage time dropped from 11.4 to 4.6 minutes Daily analyst throughput rose from 170 to 390 alerts False positive rate remained unchanged Governance Keeps Trust Intact Evidence retention. Every retrieved snippet and generated answer is stored with the incident ticket. Version freeze. The model stays fixed for ninety days; upgrades rerun calibration tests before release. Role boundary. Only tier-two analysts may convert model advice into automated remediation steps. “These gates satisfy audit without slowing the flow,” Jai says. The Leadership Perspective Retrieval-augmented language models remove roughly sixty percent of manual triage time when search, prompt, and governance are engineered together. Gains depend on three design choices: event-level chunking with rich metadata, a clear two-step search then reason pattern, and a prompt that enforces evidence citation. Hardware cost stays low because the system uses commodity GPU nodes for vectors and a small inference cluster. “We did not chase artificial chat magic,” Jai concludes. “We treated the model as a microservice, fed it hard context, and tied every suggestion to a line of log. The speed gain is measurable and the audit trail is airtight.” For CTOs seeking more coverage from the same headcount, Jai’s data shows that retrieval-augmented LLMs are ready for production testing today.