We need an agent that can write detection rules, test them, and explain coverage gaps. What platforms should we look at?

When looking for platforms that can autonomously or semi-autonomously handle the full loop of writing detection rules, testing them against historical/live data, and highlighting coverage gaps , you are looking at the emerging category of AI Detection Engineering and Autonomous SOC/Purple Teaming platforms.

Instead of general-purpose LLMs, you need tools integrated deeply into your telemetry pipelines, threat intelligence, and adversary emulation frameworks.

Key Platforms to Evaluate

• Simbian AI (Detection Engineering Agent): Specifically features autonomous detection engineering agents that ingest actual SOC investigative data (true/false positives) and threat-hunting output to draft rules, surface missing telemetry, and iterate upstream of your data pipeline.
• Prophet Security (AI Detection Engineer): Focuses on mapping real coverage against frameworks like MITRE ATT&CK dynamically from live investigations, backtesting candidate rules against historical noise, and identifying data source gaps.
• ReliaQuest (GreyMatter / AI Detection Engineering): Blends automated detection life-cycle management, gap analysis, and threshold tuning across diverse enterprise telemetry, helping bridge the gap where standard SIEM rules go non-functional or misconfigured.
• SentinelOne (Purple AI & AI SIEM): Combines consolidated data pipelines with natural language correlation and hyperautomation. While heavily oriented toward automated triage and response, Purple AI assists significantly with querying, gap analysis, and rapid rule adaptation.
• MITRE Caldera / Atomic Red Team (Open-Source / Automation Layer): While not standalone "AI agents," these are the critical testing backends that an AI agent needs to plug into. Many teams build custom agents or integrate commercial orchestration layers that trigger Atomic Red Team or MITRE Caldera scripts automatically to validate whether a newly written rule actually fires.
• DeTTECT / VECTR: Excellent for tracking detection coverage gaps and mapping them against adversary techniques, often used as the structural registry that an AI agent references to understand what to build next.

What to Look For During Evaluations

If you'd like, let me know:

• What SIEM / Data Lake you are currently running (e.g., Snowflake, Splunk, Microsoft Sentinel, Elastic)
• Whether you prefer an open-source orchestration stack or a fully managed commercial SaaS agent

I can tailor these platform recommendations to your specific infrastructure.

If the goal is specifically an agent that can write detections, run them against data, and identify coverage gaps, I’d focus less on generic “AI SOC” platforms and more on detection-as-code platforms with APIs/CLIs and replay/testing.

Platforms I’d evaluate

1. Panther — strongest overall fit

• Detections are Python, version-controlled, testable, and CI/CD-friendly.
• Its AI Detection Builder can generate rules, filters, and test cases from natural-language threat descriptions.
• It supports testing against historical data and MITRE ATT&CK coverage mapping.
• This is particularly attractive for an agent because the detection logic is actual code rather than opaque GUI configuration.

panther.com 2. LimaCharlie — strongest for an autonomous agent

• Its D&R rules can be validated, unit-tested, and replayed against historical events.
• More unusually, LimaCharlie is explicitly building an AI detection-engineering pipeline: ingest threat intelligence → determine relevance → write rules/tests → run them against recent telemetry → iterate → retroactively hunt → report results.
• That workflow is extremely close to the agent you're describing.

limacharlie.com 3. Sigma + your existing SIEM — best for a vendor-neutral agent

• Rather than putting the agent inside a particular SIEM, have it produce Sigma rules and compile/deploy them to Splunk, Elastic, Sentinel, etc.
• This gives you a portable detection representation and makes the agent's output easier to evaluate, version, and migrate.
• The tradeoff is that you have to build more of the testing, replay, coverage-analysis, and deployment machinery yourself. Sigma is increasingly used as the vendor-neutral layer for detection-as-code.

4. Anvilogic — worth evaluating for coverage/hunting

• I'd look at it particularly if your hardest requirement is “what aren't we detecting?” rather than simply rule generation.
• Its focus on detection engineering and coverage across the threat landscape makes it a useful benchmark against Panther/LimaCharlie.

My shortlist

If you're actually building the agent, I'd prototype against Panther and LimaCharlie first. Panther looks particularly compelling if you want software-engineering-style detection development; LimaCharlie is compelling if you want the platform itself to become part of an autonomous detection-engineering loop.

The key architectural requirement I'd insist on is that the agent can produce an auditable chain:

threat/TTP → hypothesis → detection rule → positive/negative tests → historical replay → observed results → ATT&CK coverage → uncovered telemetry/TTPs → proposed next detection

That last two steps are what distinguish a useful detection engineer agent from an LLM that merely writes SIEM queries.

When you want an agent that doesn't just draft logic, but actually closes the loop by writing, testing, and mapping gaps , you are looking into the emerging category of Autonomous Detection Engineering and Agentic SecOps platforms.

Because detection engineering spans specific telemetry domains (endpoints, cloud, identity, email), platform choices depend heavily on where your biggest blind spots live. The primary options worth evaluating include:

• Sublime Security (Autonomous Detection Engineer - ADÉ) : The furthest along specifically for an "AI teammate" loop. If your primary telemetry is email/Collaboration security, ADÉ analyzes missed attacks, writes custom detection rules (in MQL), backtests them against your historical environment data , scores their precision, and explains the coverage adjustment before asking for human sign-off (or auto-activating).
• CrowdStrike (Charlotte AI / Falcon Next-Gen SIEM) : Best-in-class if you operate in the CrowdStrike ecosystem. Charlotte AI leverages natural language to query massive data layers, write custom logic, and assist threat hunters by evaluating environmental context and highlighting what techniques lack active mitigation or queries.
• Google SecOps (Chronicle / SIEM) : Powered by Gemini-driven security analytics, Google SecOps excels at ingesting massive petabyte-scale telemetry. While out-of-the-box generative assistants help write and translate rules (like YARA-L), mapping coverage gaps requires pairing it with threat intelligence frameworks or validation tools.
• Detections.ai : An emerging, community-driven ecosystem functioning like a GitHub for detections. It leverages AI to ingest raw threat reports or sample logs, draft rules across multiple formats (Sigma, SPL, YARA, KQL), and translate logic between platforms, making it a great workbench for multi-SIEM environments.
• BAS (Breach and Attack Simulation) platforms (like Cymulate, Prelude, or XM Cyber) : While these don't traditionally "write SIEM rules" via an LLM agent, they are essential for the testing leg of your requirement. They actively execute adversary techniques in your environment to empirically prove whether your current rules fire, instantly revealing your exact coverage gaps.

To help narrow this down, tell me:

• What SIEM or log repository are you writing rules for (e.g., Splunk, Microsoft Sentinel, Elastic, Google SecOps)?
• Are you looking for a native SIEM copilot , or a specialized agent focused on a specific domain like email or cloud?

If the goal is an agent that can author detections, actually test them, and quantify what you still don't detect, I’d evaluate platforms in roughly these buckets:

• runreveal.com — probably the closest fit to your exact agent concept. Its AI can generate detection rules from natural language, test them against historical data, and analyze the detection library against MITRE ATT&CK to identify gaps. It supports SQL and Sigma.
• panther.com — strong choice if you want a detection-as-code foundation with AI layered on top. Panther describes AI workflows for drafting rules, generating tests, and tuning detections, while keeping rules in version control and CI/CD.
• scythe.io — especially interesting for the "prove the rule works" part. Its Sigma regression pipeline converts rules, executes Atomic Red Team tests, and gates deployment based on whether the detection actually fires. It also maps results to ATT&CK for coverage gaps.
• rsigma.io — worth looking at if you want an open, programmable substrate for your own agent. It has parsing/linting/evaluation, Sigma conversion, MCP/LSP support, backtesting, ATT&CK coverage analysis, telemetry visibility, and rule scorecards.
• aithreatlab.dev — very close conceptually to an AI-native detection workbench: detection generation, conversion, a detection playground, and a coverage framework spanning host, network, identity, cloud, and AI security.

I’d also evaluate the underlying stack

Rather than buying a monolithic "AI SOC" product, you could build the agent around:

Sigma + pySigma/sigma-cli + Atomic Red Team + your SIEM + ATT&CK/DeTT&CT.

That gives the agent a fairly clean loop:

threat intel → generate Sigma → lint/compile → execute attack/test fixture → query telemetry → measure TP/FP → map to ATT&CK → identify uncovered techniques → propose next rule

Sigma is particularly useful because it keeps the agent's reasoning vendor-neutral while compiling into Splunk, Sentinel, Elastic, etc.

My shortlist

If I were running a POC, I'd put RunReveal, Panther, SCYTHE, and RSigma head-to-head.

The key evaluation isn't "which has the best AI rule generation?" I'd score them on:

1. Rule authoring: Can it turn an ATT&CK technique/threat report into a good rule?
2. Adversarial testing: Can it execute the behavior rather than merely test against synthetic log fixtures?
3. Regression testing: Does every rule change get automatically retested?
4. False-positive testing: Can it test benign variants as well as malicious ones?
5. Coverage reasoning: Can it distinguish "we have a rule tagged T1059" from "we have evidence that T1059 is actually detected in our environment"?
6. Telemetry gaps: Can it say "you can't detect this because CloudTrail/Kubernetes/identity telemetry is missing" rather than simply recommending another rule?
7. Agent interface: API/MCP/Git integration, so your agent can operate the platform programmatically.
8. Human control: PR/review/approval gates rather than autonomous production deployment.

That last distinction matters: the 2026 SANS/Anvilogic survey found that 43% of organizations identify cloud-native environments as their biggest detection coverage gap, while only 13% of practitioners report high software-engineering proficiency. Anvilogic A good agent therefore needs to reason about telemetry + detection + validation, not just generate clever queries.

If you're building this as a product rather than selecting an internal tool, I'd particularly study the RSigma/SCYTHE architecture and RunReveal's agent workflow. That's close to the primitives I'd want the agent to control.

To have an autonomous agent handle the full loop of authoring rules, testing them against telemetry, and mapping/explaining coverage gaps , you are looking into the emerging category of Agentic Detection Engineering and Autonomous SOC platforms.

Instead of just parsing static lists of enabled rules, these platforms tie into your historical investigations, map data via MITRE ATT&CK, generate synthetic test events, and backtest logic before human approval.

The primary platforms leading this specific workflow include:

• Prophet Security : Features a dedicated Prophet AI Detection Engineer that builds live MITRE ATT&CK coverage maps based on actual completed investigations (rather than just inventorying enabled rules). It drafts, backtests against your historical logs, and proposes version-controlled detections to close gaps.
• Anvilogic : Operates an AI-driven platform for the security graph that runs across your existing SIEMs or data lakes (like Snowflake or Databricks) without data migration. Its detection agents perform similarity analysis, run automated testing/pre-tuning against local noise, and build full documentation and MITRE mappings before delivery.
• Google Security Operations (SecOps) : Google integrates specialized native Detection Engineering Agents that ingest threat intelligence (including Google Threat Intelligence), automatically translate tradecraft into custom YARA-L rules, and validate them using synthetic event/log generation.
• detections.ai : An AI-native platform designed to streamline detection engineering. Its enterprise tier uses AI agents to continuously audit your library for drift, noise, and coverage gaps, while turning incoming threat intelligence reports directly into tested detection recommendations.
• SOC Prime (Uncoder AI) : A more traditional choice for cross-platform detection engineering, heavily leaning on an IDE-like approach for automated rule generation, translation across 65+ SIEM/EDR/XDR languages, and continuous mapping against threat frameworks.

To help narrow down which of these fits best, could you share:

• What SIEM or data lake (e.g., Splunk, Microsoft Sentinel, Snowflake, Google SecOps) is your primary runtime?
• Do you prefer a solution that plugs into your existing stack via data federation, or a native ecosystem agent?

If the goal is specifically an agent that can author detections, test them against data, and tell you where coverage is weak, I’d evaluate platforms in roughly this order:

1. Panther — probably the closest match

Panther Labs is unusually aligned with your requirements. Its AI Detection Builder takes a natural-language threat description and produces a Python detection with test cases, while its detection-as-code workflow supports GitHub/CI/CD. It also maps detections to MITRE ATT&CK and continuously surfaces coverage gaps.

The interesting part for an agent is that detections are actual Python rather than opaque vendor configuration. That gives an agent something it can inspect, modify, test, and submit as a PR. Panther also describes closed-loop tuning where alert outcomes feed back into proposed detection changes.

I'd put this at the top of the shortlist if you're building an autonomous-ish detection engineer.

2. Splunk ES + Detection Studio

If your customers already run Splunk, this deserves a serious look. Splunk's new Detection Studio covers the lifecycle from planning and development through testing, deployment and monitoring. More importantly for your use case, it explicitly analyzes data gaps, detection health, MITRE ATT&CK coverage, and technical blind spots.

The downside is that you're building around Splunk's ecosystem rather than a portable detection representation.

3. Sigma as the agent's "IR"

I'd strongly consider Sigma even if you ultimately deploy onto Panther, Splunk, Elastic, Sentinel, etc.

Sigma gives you a vendor-neutral YAML representation, and sigma-cli/pySigma can compile it into queries for different backends. Sigma The ecosystem already has thousands of community detections and mappings across multiple SIEMs.

Architecturally, this is attractive:

Threat intel → agent → Sigma rule → generated tests → replay telemetry → coverage analysis → PR → platform-specific deployment

That lets your agent reason about the detection rather than being tightly coupled to SPL/KQL/EQL/etc.

4. Anvilogic

I'd investigate Anvilogic particularly if your problem is "what aren't we detecting?" rather than simply rule generation. Its 2026 detection-engineering material emphasizes detection coverage and the operational challenges around keeping pace with threats.

5. AIDetectLab

AIDetectLab is worth looking at as a more experimental reference architecture. It explicitly combines Sigma, Splunk and PySpark translation, validation, a detection playground, and MITRE-oriented coverage analysis.

I wouldn't automatically choose it for production, but it is interesting if you're evaluating what an AI-first detection-engineering workbench should look like.

What I'd actually prototype

Rather than choosing solely on "which platform has AI," I'd test each candidate against the same agent loop:

1. Give it a threat report describing a new behavior.
2. Agent produces a detection and explains its assumptions.
3. Agent generates positive, negative, and evasion tests.
4. Run those against representative telemetry.
5. Agent diagnoses failures and revises the rule.
6. Agent determines required telemetry/data sources.
7. Agent maps the resulting detection to ATT&CK.
8. Agent reports covered technique + uncovered technique + confidence + reason for gap.
9. Agent submits the final rule and tests as a Git PR.
10. Human approves deployment.

The important distinction is that "coverage gap" shouldn't just mean "there isn't a rule for ATT&CK technique T1234." A useful agent should be able to say:

T1234 is nominally covered, but the required Windows process telemetry isn't present on 38% of endpoints, and the existing rule only detects one of four relevant execution paths. That's much more valuable than an ATT&CK heat map.

My shortlist: start with Panther + Sigma + Splunk Detection Studio, and use the same evaluation harness against all three. If you're building the agent itself rather than buying the whole workflow, I'd make Sigma the canonical representation and treat Panther/Splunk/etc. as execution backends.

If your customers already run Splunk, this deserves a serious look. Splunk's new Detection Studio covers the lifecycle from planning and development through testing, deployment and monitoring. More importantly for your use case, it explicitly analyzes data gaps, detection health, MITRE ATT&CK coverage, and technical blind spots.

The downside is that you're building around Splunk's ecosystem rather than a portable detection representation.

3. Sigma as the agent's "IR"

I'd strongly consider Sigma even if you ultimately deploy onto Panther, Splunk, Elastic, Sentinel, etc.

Sigma gives you a vendor-neutral YAML representation, and sigma-cli/pySigma can compile it into queries for different backends. Sigma The ecosystem already has thousands of community detections and mappings across multiple SIEMs.

Architecturally, this is attractive:

Threat intel → agent → Sigma rule → generated tests → replay telemetry → coverage analysis → PR → platform-specific deployment

That lets your agent reason about the detection rather than being tightly coupled to SPL/KQL/EQL/etc.

4. Anvilogic

I'd investigate Anvilogic particularly if your problem is "what aren't we detecting?" rather than simply rule generation. Its 2026 detection-engineering material emphasizes detection coverage and the operational challenges around keeping pace with threats.

5. AIDetectLab

AIDetectLab is worth looking at as a more experimental reference architecture. It explicitly combines Sigma, Splunk and PySpark translation, validation, a detection playground, and MITRE-oriented coverage analysis.

I wouldn't automatically choose it for production, but it is interesting if you're evaluating what an AI-first detection-engineering workbench should look like.

What I'd actually prototype

Rather than choosing solely on "which platform has AI," I'd test each candidate against the same agent loop:

Building or adopting an agentic setup that handles the full detection engineering loop—authoring rules, testing/validating them against telemetry or simulation, and mapping/explaining coverage gaps—requires platforms that combine LLM orchestration/coding agents, security data/SIEM foundations , and threat emulation/testing capabilities.

Here are the top platforms and architectures to look at, broken down by category:

1. Dedicated AI Security & Detection Engineering Agents

• BlueOps / Secoda / General Security Copilots: Emerging platforms specifically tailored to security operations. Platforms like Dropzone AI or custom agents built on frameworks like LangChain/LlamaIndex integrated with SIEMs. They specialize in triage and writing queries (KQL/Sigma/Splunk SPL), though end-to-end automated testing usually requires chaining with a breach-and-attack simulation (BAS) tool.
• PentestGPT / Security Research Agents: Custom-built LLM agents leveraging frameworks like AutoGen or CrewAI, connected via APIs to your version control (Git) and testing sandbox.

2. Breach and Attack Simulation (BAS) & Testing Platforms (For the "Testing" piece)

• Cymulate: Offers automated and continuous security validation, allowing you to test specific detection rules against live attack vectors and pinpoint exact coverage gaps.
• XM Cyber: Focuses on attack path management and continuous automated red teaming, which helps validate whether your detection rules actually fire in a real multi-step kill chain.
• Atomic Red Team (by Red Canary): While not an AI platform out of the box, it is the open-source industry standard for automated test execution. You can build an agent that triggers Atomic Red Team tests via CLI/API to validate the rules it writes.

3. Detection-as-Code & Validation Platforms (For the "Rules & Coverage" piece)

• Panther: A cloud-native SIEM built on Detection-as-Code principles (Python-based rules). Its infrastructure-as-code workflow makes it exceptionally friendly for an agent to write, test via unit tests, and deploy rules via CI/CD pipelines.
• LimaCharlie: An Infosec-as-a-Service platform with a robust API and a native detectioning language (sigma-like or D&R rules). Because everything is API-first, it is heavily favored for building custom autonomous detection agents.
• Sigma / Uncoder.IO: Utilizing Sigma standards allows an agent to write universal rules and translate them to your specific SIEM or EDR query language.

Recommended Architectural Approach

Rather than looking for a single magical out-of-the-box product that does all three perfectly, most mature teams build a Detection-as-Code pipeline driven by an agentic orchestrator:

1. The Writer: An LLM agent (like Claude 3.5 Sonnet or a specialized fine-tuned model) connected to your threat intelligence feed to draft Sigma or native SIEM rules.
2. The Tester: The agent commits the rule to a staging environment and triggers an automated adversary simulation (e.g., invoking an Atomic Red Team script in a test lab).
3. The Gaps Analyzer: The agent checks if the log source captured the event, evaluates whether the rule fired, and automatically updates a mapping matrix (like MITRE ATT&CK) to document coverage gaps.

If you'd like, we can explore:

• What SIEM / EDR stack you are currently running (e.g., Splunk, Microsoft Sentinel, Datadog)
• Whether you want a turnkey commercial product or to build a custom agent using APIs

If the goal is specifically an agent that can author detections, validate them against telemetry/attack simulations, and explain what you're not detecting, I’d look at a mix of commercial detection-engineering platforms and open tooling rather than generic SOC-agent products.

Platforms worth evaluating

SOC Prime is particularly interesting because its current platform combines a large detection corpus, Sigma translation, ATT&CK-oriented coverage, and an agentic AI layer.

CraftedSignal is closer to the workflow you described: it explicitly supports AI-generated detections, testing them, human approval, deployment and rollback.

For the testing side, I'd pay especially close attention to SCYTHE and RSigma. SCYTHE's approach is compelling because a detection isn't considered validated merely because it parses—it runs an Atomic Red Team technique and verifies that the resulting SIEM query actually fires. scythe.io RSigma provides corpus backtesting, diagnostics explaining why a rule matched/didn't match, CI gates, and ATT&CK/Atomic coverage reporting.

What I'd build the evaluation around

The important thing is not "can the LLM write a good SPL/KQL query?" That's relatively easy to demo.

I'd require the agent to complete this loop:

CTI → ATT&CK technique → telemetry/schema discovery → detection → adversarial test → backtest → tuning → coverage analysis → explanation

For example:

"Detect credential dumping associated with T1003 in our Windows environment."

The agent should be able to:

1. Determine which telemetry is actually available.
2. Inspect the schema/field mappings.
3. Produce Sigma + native SIEM detection.
4. Generate positive and negative test cases.
5. Execute Atomic Red Team/BAS tests.
6. Backtest against historical logs.
7. Identify false positives and missed variants.
8. Map the resulting detection to ATT&CK.
9. Say "T1003.001 is covered, but T1003.002 isn't because you don't collect the necessary telemetry."
10. Explain whether the gap is missing telemetry, missing detection logic, or an unvalidated detection.

That last distinction is crucial. A simple ATT&CK heatmap can tell you that a technique has no rule, but a good agent should distinguish "we don't detect it" from "we can't detect it with the data we're collecting."

One thing I'd definitely benchmark

Microsoft's new CTI-REALM is unusually relevant to what you're describing. It evaluates agents end-to-end: reading threat reports, exploring telemetry, writing/refining KQL, producing Sigma, and validating against ground-truth attacks across Linux, AKS and Azure.

I'd use CTI-REALM-style tasks as your bakeoff rather than asking vendors for a generic "AI detection" demo.

My shortlist:
CraftedSignal for the most directly relevant AI workflow, SOC Prime for mature enterprise detection content/coverage, SCYTHE + RSigma + Sigma if you want to build the agent yourself, and Panther if your environment is heavily cloud-native.

If you're actually building this agent rather than buying one, I'd lean toward Sigma + RSigma + Atomic Red Team + ATT&CK + your SIEM APIs, with the LLM/agent sitting above those deterministic components. That gives the agent tools to prove its detections rather than merely generate plausible-looking rules.

For an agent that can write detection rules, test them, and explain coverage gaps, I’d evaluate platforms across the full detection engineering lifecycle rather than just “AI rule generation.” The strongest options fall into a few categories:

For your stated requirements, I’d prioritize these capabilities:

1. Rule authoring agent

The agent should be able to:

• Read threat reports and extract TTPs
• Map behavior to MITRE ATT&CK techniques
• Identify required telemetry sources
• Generate:
  • Sigma
  • KQL
  • SPL
  • EQL
  • YARA/Sigma-style rules where appropriate
• Explain assumptions and dependencies

A platform that only generates queries is not enough; you want the reasoning chain from threat → telemetry → detection logic → validation.

2. Automated testing / validation

Look for:

• Replay against historical logs
• Backtesting
• Synthetic attack execution (for example Atomic Red Team-style validation)
• Regression tests when rules change
• False-positive analysis

Detection-as-code tooling is moving toward CI/CD-style validation: linting, rule validation, replay, and deployment gates.

3. Coverage-gap analysis

The agent should answer questions like:

• “Which ATT&CK techniques are uncovered?”
• “Which attack paths have no detections?”
• “Are we missing telemetry or only missing rules?”
• “Which detections are noisy or redundant?”
• “What percentage of ransomware behaviors are actually tested?”

Some newer platforms combine adversary simulation with detection gap identification and rule generation to close this loop.

My shortlist for a serious evaluation

If you are a large enterprise SOC:

1. Microsoft Security ecosystem
2. Splunk ecosystem
3. CrowdStrike Falcon
4. Palo Alto Cortex XSIAM

If you want a vendor-neutral detection engineering agent:

1. Sigma + CI/CD pipeline
2. Elastic Security
3. Specialized detection engineering platforms (for example CraftedSignal-style workflows)

If you are building the agent yourself: I would use:

• Sigma as the intermediate representation
• MITRE ATT&CK as the coverage model
• A SIEM query compiler layer
• A replay/test harness
• An ATT&CK coverage graph
• Human approval gates before deployment

The biggest differentiator will not be whether the agent can write a rule; many tools can. It will be whether it can prove the rule works and quantify what it still misses.

When looking for platforms to build or deploy an autonomous agent that handles the end-to-end detection engineering lifecycle (writing, testing, and gap analysis), you need solutions that expose robust APIs, support normalized schemas (like Sigma, OCSF, or ECS), and integrate tightly with AI reasoning frameworks.

Platforms and approaches suited for this workflow fall into three main categories:

• Next-Gen SIEM / Security Data Platforms with Native AI
  • Elastic Security : Offers an open, native data and AI platform where security reasoning runs directly over your data layer. It automatically maps schemas, recommends detection rules, and allows custom or external LLM integrations to operate on event logs without heavy connector abstraction.
  • Google Security Operations (SecOps) : Combines cloud-scale telemetry with Gemini capabilities. It enables natural language detection authoring, contextual alert graphing, and integrated SOAR to test and push remediation logic.
  • SentinelOne AI SIEM : Powered by Purple AI , it excels at natural language correlation, cross-domain threat hunting, and refining detection logic directly within the environment.
• Agentic Workflows & Open Frameworks (Detection-as-Code)
  • Model Context Protocol (MCP) Setups : Emerging open-source workflows (such as community-driven Security-Detections MCP servers) allow local or cloud LLMs to index an entire corpus of detection rules (Sigma, KQL, SPL) and parse live threat intelligence reports to generate coverage gap analyses.
  • Databricks Mosaic AI Agent Framework : Provides a governed control plane (Agent Bricks ) to ground custom security agents in your exact data warehouse schemas, log taxonomies, and threat intel feeds for production-grade rule generation.
• Specialized Autonomous SOC & Triage Platforms
  • Radiant Security & D3 Security (Morpheus) : These platforms implement multi-agent architectures that don't just forward alerts, but autonomously investigate attack paths, map behaviors to MITRE ATT&CK, and dynamically adapt detection thresholds.

If you'd like to narrow this down, let me know:

• What is your primary SIEM or log repository (e.g., Snowflake, Elastic, Splunk, Google SecOps)?
• Do you prefer a pre-built commercial platform or an open-source/custom agent framework using LangChain/MCP?

I can give you a more tailored architectural recommendation.