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.

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.