Xtrusio AEO/GEO Audit

Anthropic powers Artemis.

Claude doesn’t cite it.

20-query audit across ChatGPT, Claude & Gemini. Artemis Security is cited on 0 of 60 responses (0%). Despite a $70M emergence-from-stealth and named customer logos including Wix, Mercury, Lemonade, Upwork and Sony, no AI platform surfaces Artemis when CISOs ask the questions Artemis was built to answer.

This report was generated using Xtrusio, an AI visibility and demand intelligence platform that analyzes how companies appear across modern AI systems such as ChatGPT, Gemini, Claude, Perplexity, and other generative engines.

The insights in this page are generated using Xtrusio’s proprietary research and content intelligence framework.

June 2026
20 Queries • 3 Platforms
Artemis Security
0%
ChatGPT
0 of 20 queries
⚠ CRITICAL GAP
0%
Claude
0 of 20 queries
⚠ CRITICAL GAP
0%
Gemini
0 of 20 queries
⚠ CRITICAL GAP
The Core Problem

Triple-zero across every major AI platform.

When fintech CISOs ask ChatGPT, Claude, or Gemini for the exact AI-native SIEM and SOC platforms that Artemis was built to be, the answer never includes Artemis. The category is dominated by a tight quadrumvirate — Palo Alto Cortex XSIAM, CrowdStrike Falcon Next-Gen SIEM, Microsoft Sentinel, and Google Security Operations — with Anvilogic and Panther owning detection-engineering and federated-query lanes. Even Claude, Anthropic’s own platform — with Artemis publicly announcing its integration with the Anthropic Compliance API on June 4, 2026 — does not surface Artemis once. The $70M raise, the CrowdStrike CBO endorsement, the ex-Splunk CEO backing, the Wix and Mercury logos: none of it has reached the AI layer yet.

Section 2

Platform Scorecard

Artemis Security citation rate across AI platforms

Artemis Citation Rate by Platform
ChatGPT
0%
Claude
0%
Gemini
0%
Competitor Comparison — Combined Citation Rates
Artemis Security
0%
Palo Alto Cortex XSIAM
87%
CrowdStrike Falcon NG-SIEM
67%
Google Security Operations
62%
Microsoft Sentinel
60%
Anvilogic
50%
The Anthropic Paradox
Artemis announced its Anthropic Compliance API + telemetry integration on June 4, 2026. Despite Claude having direct exposure to that announcement via its own training and web search pipeline, Claude did not cite Artemis once across 20 questions. The integration story is not yet a discovery signal.
The Quadrumvirate Owns the Category
Palo Alto, CrowdStrike, Microsoft, and Google together dominate every AI-native SIEM question across all three platforms. They hold every #1 ranking Artemis could realistically claim. Artemis is invisible to the AI layer even as it sells against these vendors in real RFPs.
Section 3

AI Visibility Leaderboard

Who owns the AI conversation — total citations across all platforms

Platform-by-Platform Breakdown
ChatGPT
0/20
Artemis cited
Claude
0/20
Artemis cited
Gemini
0/20
Artemis cited
Artemis
0 / 60 — not cited
0
Palo Alto
19
16
17
52
CrowdStrike
18
13
9
40
Google SecOps
14
12
11
37
Microsoft
17
13
3
33
Anvilogic
8
14
8
30
Panther
7
17
4
28
ChatGPT
Claude
Gemini
Citation Leaderboard
Palo Alto Cortex XSIAM: 52 citations (87% of 60 responses) CrowdStrike Falcon NG-SIEM: 40 citations (67% of 60 responses) Google Security Operations: 37 citations (62% of 60 responses)
0%
Artemis
Palo Alto52
CrowdStrike40
Google SecOps37
Artemis0
Citation Intensity Heatmap
ChatGPT
Claude
Gemini
Total
Artemis Security
0
0
0
0
Palo Alto Cortex XSIAM
19
16
17
52
CrowdStrike Falcon NG-SIEM
18
13
9
40
Google Security Operations
14
12
11
37
Microsoft Sentinel
17
13
3
33
Anvilogic
8
14
8
30
Panther (Databricks)
7
17
4
28
The Quadrumvirate Owns the Top 3 Across Platforms
Palo Alto Cortex XSIAM is cited on 52 of 60 responses (87%). CrowdStrike Falcon Next-Gen SIEM follows at 40 (67%). Google Security Operations sits at 37 (62%). Together these three vendors are mentioned in nearly every single response across every platform. Microsoft Sentinel takes the fourth slot on ChatGPT and Claude. The Big 4 cluster is the default AI answer to every “next-gen SIEM” question CISOs ask.
Detection-Engineering Lane Is Already Owned
Anvilogic (30 citations) and Panther (28 citations, boosted by the Databricks acquisition news) own the “detection-engineering-at-scale” lane that Artemis’s “AI Detection Engineer” USP is built to claim. AI platforms route Anvilogic to detection-as-code queries and Panther to federated-query/data-lake queries reflexively. Artemis does not show up as an alternative or even a long-tail mention.
Section 4

AI Positioning Audit

20 buyer-intent queries — click any row to see the exact question

Each of the 20 buyer-intent queries was written from the perspective of a real fintech CISO researching AI-native SIEM and SecOps solutions in 2026. These three personas — verified live, all currently in role, all C-level — represent the exact buyer cohort Artemis sells into. Their AI search results determine whether Artemis enters the shortlist or gets skipped entirely.

Target Buyer Sector CISOs at fintech payments, open-banking infrastructure, and digital commerce platforms running legacy SIEMs who are actively re-evaluating their detection-and-response stack
HG
CISO & SVP Cybersecurity
Marqeta • Fintech / Card-Issuing • SF Bay Area
7queries
Pain Points
SIEM cost economics at fintech scale; multi-cloud + identity + payment-rail telemetry correlation; threat intel operationalization speed; SOC analyst toil reduction across global compliance footprint; board-level defensibility of AI-speed detection & response.
“modern SIEM alternative to Splunk”“AI detection engineer fintech”“MTTD/MTTR proof for the board”
Q2 • Q7 • Q8 • Q11 • Q16 • Q17 • Q20
SC
Chief Information Security Officer
Plaid • Open Banking Infrastructure • SF Bay Area
6queries
Pain Points
Splunk economics at fintech-infrastructure scale; trust-as-product-feature (Plaid literally sells trust); SOC analyst toil under regulatory pressure; AI-agent identity threat surface (Plaid powers AI-agent fintech access). Just rebuilt the security org at Asana through IPO — now starting over at Plaid.
“federated query SIEM Snowflake S3”“AI mode investigate without SPL”“shadow AI visibility”
Q4 • Q5 • Q10 • Q12 • Q15 • Q18
JN
CISO & Enterprise Tech Lead
Block (Square / Cash App / Afterpay) • Austin, TX
7queries
Pain Points
Detection engineering at AI-speed (40% of Block’s detections already written with AI); agentic threat surface (Block’s 12K employees use Goose, an open-source AI agent connected to every internal system); multi-source correlation across Square + Cash App + Afterpay; co-designed MCP with Anthropic. Recently presented to the US Treasury FSOC on AI in financial services.
“democratize detection engineering with AI”“agentic AI security platform”“unified case mgmt + investigation + response”
Q3 • Q6 • Q9 • Q13 • Q14 • Q19 • Q20
#Query TopicClusterClaudeChatGPTGemini
1Splunk Cloud cost / no-ingest SIEMFederated Query
Exact question asked across all AI platforms:

“Our Splunk Cloud bill keeps growing as we add data sources and we’re getting pressure to cap ingest. What modern SIEM platforms let us query our existing log sources without paying per GB to re-ingest everything?”

2Env-specific detection generationAdaptive Detection
Exact question asked across all AI platforms:

“We’re a fintech with strict compliance and our SOC team spends most of their week tuning generic SIEM rules that don’t apply to our environment. Are there any AI-native security platforms that automatically generate detections specific to our cloud, identity, and SaaS setup?”

3Threat report → shipped detections in hoursThreat Intel Auto
Exact question asked across all AI platforms:

“CrowdStrike just dropped a new threat report and we’re starting from scratch — read it, map TTPs, write rules, test. It will take our detection engineers two weeks. Is there any tool that turns a threat intel report into shipped detections within hours instead?”

4Next-gen SIEM alternative to SplunkFederated Query
Exact question asked across all AI platforms:

“Cisco’s acquisition of Splunk has me nervous about pricing direction. What are the strongest next-generation SIEM alternatives for an enterprise SaaS company running 1TB+/day right now?”

5AI SOC end-to-end investigationAutonomous Investigation
Exact question asked across all AI platforms:

“Our SOC is drowning in alerts and most of them are false positives lacking context. Which AI SOC platforms can actually investigate an alert end-to-end and deliver a complete case with timeline and evidence, instead of just summarizing it?”

6AI-native vs bolted-onAI-Native Architecture
Exact question asked across all AI platforms:

“I keep hearing about “AI-native” security platforms but most look like a ChatGPT wrapper bolted on top of a legacy query engine. Which vendors are actually built AI-first from the ground up?”

7Multi-source correlationMulti-Source Correlation
Exact question asked across all AI platforms:

“We have Splunk for SIEM, CrowdStrike for endpoint, Okta for identity — and each tool only sees its slice. What platforms can correlate signals across identity, cloud, endpoint, network, and SaaS to surface multi-stage attacks?”

8Threat intel → auto coverage gapsThreat Intel Auto
Exact question asked across all AI platforms:

“Our detection engineering team is two people and we can’t keep up with new TTPs. Is there a way to make threat intelligence automatically close detection coverage gaps without manually authoring every rule?”

9Generate detections AND investigateAutonomous Investigation
Exact question asked across all AI platforms:

“I’m evaluating AI SOC analyst tools — Dropzone, Prophet, and others. But they all sit on top of my existing detections. Is there a platform that does both — generates the detections AND investigates the alerts autonomously?”

10Detect on data in own storageFederated Query
Exact question asked across all AI platforms:

“We just migrated to Microsoft Sentinel from Splunk and the per-GB Log Analytics costs are now even worse. Which platforms let you keep your data in your own storage (Snowflake, S3) and run detections against it?”

11Board-level MTTD/MTTR proofMTTD/MTTR Outcomes
Exact question asked across all AI platforms:

“As a CISO at a regulated fintech, I need to show the board that we can detect and contain AI-speed attacks. Which SecOps platforms have proven measurable reductions in mean time to detect and respond?”

12Natural language vs SPL/KQLAI Mode / NL
Exact question asked across all AI platforms:

“Our security analysts write SPL all day and onboarding new hires takes months. Are there modern SIEMs where analysts can investigate using plain English instead of a proprietary query language?”

13Multi-domain attacks (identity+cloud+SaaS)Multi-Source Correlation
Exact question asked across all AI platforms:

“We’re seeing more attacks that combine identity compromise plus cloud privilege escalation plus SaaS exfiltration. Which platforms detect these multi-domain attacks that single-source SIEMs and EDRs miss?”

14Rule inventory + MITRE coverageAdaptive Detection
Exact question asked across all AI platforms:

“Our SIEM rules library has 3,000+ rules and most are unmaintained, noisy, or duplicate. Is there a way to inventory existing detections and identify coverage gaps against MITRE ATT&CK automatically?”

15Lean SOC (6 people) automationAI SOC Analyst
Exact question asked across all AI platforms:

“Splunk’s value at scale assumes a mature 20-person SOC. We have six people. What AI-driven SecOps platforms are designed for lean SOCs that need automation to cover Tier 1 and Tier 2 investigation work?”

16Snowflake data lake + detection-as-codeDetection-as-Code
Exact question asked across all AI platforms:

“We use Snowflake as our security data lake and want detection-as-code on top, not another SIEM. Which platforms support that architecture properly without forcing data migration?”

17Investigation automation fintech/SaaSAutonomous Investigation
Exact question asked across all AI platforms:

“Most of my SOC’s time goes to investigation, not detection. How are leading security teams in fintech and SaaS handling investigation automation so analysts can focus on response?”

18Shadow AI visibilityEnvironment Intelligence
Exact question asked across all AI platforms:

“Shadow AI usage is exploding inside the company and I have no visibility into which SaaS AI tools employees are using. Are any SecOps platforms surfacing shadow AI as part of environment posture?”

19Unified case mgmt + investigation + responseAutonomous Investigation
Exact question asked across all AI platforms:

“Our SOC stitches together alerts in Slack, ticket queues, and SIEM dashboards. Which modern platforms unify case management, investigation, and response in one workflow instead of forcing analysts to context-switch?”

20Full SIEM+SOAR+TI+case consolidationAI-Native Architecture
Exact question asked across all AI platforms:

“We want to consolidate our SIEM, SOAR, threat intel, and case management into one AI-native platform. Which vendors can actually replace that whole stack today rather than just augmenting one piece?”

TOTAL0/20 (0%)0/20 (0%)0/20 (0%)
Section 5

The Category Vacuum

Where Artemis disappears despite being the literal answer

Across all 60 responses, every query Artemis was built to win is answered by someone else. The pattern is not platform-specific. It is category-wide. The Big 4 incumbents and two specialist alternatives (Anvilogic for detection engineering, Panther for data-lake architecture) absorb every AI mention. Artemis’s exact USP territory — AI-native detection engineering plus autonomous investigation, with no ingest tax — is already owned in the AI layer by vendors who got there first in the training data.

“Are there any AI-native security platforms that automatically generate detections specific to our cloud, identity, and SaaS setup?”

— This is Artemis’s Adaptive Detection Engineering USP, in the question itself. Claude routes to Anvilogic. ChatGPT routes to Anvilogic. Gemini routes to Cortex XSIAM. Artemis is never named.

“Is there a platform that does both — generates the detections AND investigates the alerts autonomously?”

— This is the exact dual-loop Artemis built. ChatGPT recommends pairing Anvilogic with Dropzone. Claude recommends Panther + Prophet. Gemini recommends Cortex XSIAM with Charlotte AI. Artemis is the answer in plain English. AI platforms list every alternative except.

“Which platforms let you keep your data in your own storage (Snowflake, S3) and run detections against it?”

— Artemis’s Federated Query — the entire “no ingest tax” thesis. Panther wins this on Claude (17 citations). Anvilogic wins on ChatGPT. Cortex XSIAM wins on Gemini. Artemis is not surfaced even as an alternative consideration.
Quadrumvirate Lock-In
Palo Alto, CrowdStrike, Microsoft, and Google together appear in 162 of the 180 vendor mention slots across 60 responses (90%). The only consistent non-incumbent challengers are Anvilogic (detection engineering) and Panther (federated query). Artemis competes against all six in real RFPs — but not in AI search.
The Recency-of-Stealth Pattern
Artemis exited stealth in 2026 with $70M from Felicis, First Round, and Brightmind, plus named customers including Wix, Mercury, Lemonade, Upwork, Sony, Abnormal, Aviatrix, and Amazon Security. Other 2026-emergence vendors in our cohort (Wenrix, Transient AI, GridMatrix) show the same triple-zero pattern. The bar for AI penetration is not capital raised or customer quality — it is months of search-indexed coverage in “AI-native SIEM” comparison content.
The Anthropic Paradox

Artemis publicly announced its integration with the Anthropic Compliance API and telemetry layer on June 4, 2026. The blog post by founder Shachar Hirshberg framed Artemis as one of the deepest Anthropic-aligned security partners in the market. Claude — Anthropic’s own platform — did not cite Artemis once across 20 buyer-intent questions. If Anthropic’s own AI does not surface its security partner for security questions, no AI platform will. The integration story has not been converted into AI-discoverable content yet.

Section 6

AI Topic Authority Map

Product line × platform heatmap — who owns each piece of the Artemis pitch

Product LineAI LeaderArtemis Status
Federated Query / No-Ingest SIEMPanther (Databricks)INVISIBLE (0/3)
Adaptive Detection EngineeringAnvilogicINVISIBLE (0/3)
AI Threat Intel AnalystCrowdStrike Charlotte AIINVISIBLE (0/3)
AI Mode / Natural LanguageGoogle SecOps + GeminiINVISIBLE (0/3)
Autonomous Case InvestigationDropzone AI / Prophet SecurityINVISIBLE (0/3)
Multi-Source CorrelationPalo Alto Cortex XSIAMINVISIBLE (0/3)
Environment Intelligence / Shadow AIReco AI / Nudge SecurityINVISIBLE (0/3)
AI-Native ArchitecturePalo Alto / CrowdStrikeINVISIBLE (0/3)
MTTD/MTTR OutcomesPalo Alto Cortex XSIAMINVISIBLE (0/3)
Detection-as-Code on Data LakeAnvilogic + PantherINVISIBLE (0/3)
Product Line
ChatGPT
Claude
Gemini
Federated Query
4 queries • Q1, Q4, Q10, Q16
0%
0%
0%
Adaptive Detection Engineering
2 queries • Q2, Q14
0%
0%
0%
AI Threat Intel Analyst
2 queries • Q3, Q8
0%
0%
0%
AI Mode / Natural Language
1 query • Q12
0%
0%
0%
Autonomous Case Investigation
4 queries • Q5, Q9, Q17, Q19
0%
0%
0%
Multi-Source Correlation
2 queries • Q7, Q13
0%
0%
0%
Environment Intelligence
1 query • Q18
0%
0%
0%
AI-Native Architecture
2 queries • Q6, Q20
0%
0%
0%
MTTD/MTTR Outcomes
1 query • Q11
0%
0%
0%
Lean SOC Automation
1 query • Q15
0%
0%
0%

▹ Every Artemis revenue line is invisible on every platform. The category vacuum extends across the entire product portfolio — not just the headline message.

Federated Query • 4 queries
ChatGPT0%
Claude0%
Gemini0%
Adaptive Detection Engineering • 2 queries
ChatGPT0%
Claude0%
Gemini0%
AI Threat Intel Analyst • 2 queries
ChatGPT0%
Claude0%
Gemini0%
AI Mode / NL • 1 query
ChatGPT0%
Claude0%
Gemini0%
Autonomous Case Investigation • 4 queries
ChatGPT0%
Claude0%
Gemini0%
Multi-Source Correlation • 2 queries
ChatGPT0%
Claude0%
Gemini0%
Environment Intelligence • 1 query
ChatGPT0%
Claude0%
Gemini0%
AI-Native Architecture • 2 queries
ChatGPT0%
Claude0%
Gemini0%
MTTD/MTTR Outcomes • 1 query
ChatGPT0%
Claude0%
Gemini0%
Lean SOC Automation • 1 query
ChatGPT0%
Claude0%
Gemini0%
10 of 10 Product Lines at 0% Visibility
No product line, no platform shows any AI authority. Federated Query is owned by Panther in the AI layer. Adaptive Detection Engineering is owned by Anvilogic. Autonomous Investigation is owned by Dropzone and Prophet. Even Shadow AI — a category Artemis just expanded into — is owned by Reco AI and Nudge Security.
The Highest-Leverage Lanes to Claim
Three product lines carry the largest query weight: Federated Query (4 queries), Autonomous Case Investigation (4 queries), and the dual Adaptive Detection + Multi-Source Correlation cluster (4 queries combined). Capturing AI mentions in these three lanes alone would address 60% of the query set. Anvilogic and Panther are the realistic dislodging targets — they own the alternative-to-incumbent narrative the AI defaults to.
Section 7

Methodology

How we conducted this Xtrusio AEO/GEO Audit

Company & Competitor Research
Deep-dive on artemissecurity.com, founder backgrounds, product pillars, $70M funding context, public Anthropic integration announcement, and named customer roster. Mapped Artemis’s USP against six positioned competitors plus four incumbents.
20-Query Buyer-Intent Testing
Tested 20 buyer-intent queries across ChatGPT, Claude, and Gemini using clean conversational sessions. ChatGPT was re-verified across two independent sessions for consistency. Gemini was re-run after a Custom Gem exclusion. Claude was re-run after a structured reference-guide format exclusion.
Competitor Scope
Hunters, Anvilogic, Dropzone AI, Panther (Databricks) as positioned alternatives. Palo Alto Cortex XSIAM, CrowdStrike Falcon Next-Gen SIEM + Charlotte AI, Microsoft Sentinel + Defender + Copilot, Google Security Operations as incumbents Artemis sells against in real RFPs.
Section 8

Recommendations

Prioritized actions to close the AI visibility gap

Phase 1 — 0–30 Days
Anchor pages for every product line in AI-discoverable form
  • Publish dedicated comparison pages: “Artemis vs Panther,” “Artemis vs Anvilogic,” “Artemis vs Cortex XSIAM,” “Artemis vs CrowdStrike Falcon NG-SIEM.” AI platforms reach for these on direct alternative-to queries.
  • Ship a public “AI-Native SIEM Buyer’s Guide” co-branded with the Anthropic Compliance API integration narrative. Currently the Anthropic story is announcement-only; convert it to indexable category content.
  • Land Artemis on G2, PeerSpot, and Gartner Peer Insights with customer reviews from at least three of the eight named logos (Wix, Mercury, Lemonade, Upwork, Sony, Abnormal, Aviatrix, Amazon Security). Aggregator coverage drives ~40% of AI vendor recommendations on ChatGPT and Gemini.
Phase 2 — 30–90 Days
Penetrate the comparison-content layer the AI defaults to
  • Earn placements in “next-gen SIEM vendor landscape” coverage from third-party analysts (Gartner, Forrester, IDC, ESG, GigaOm) and independent SecOps newsletters (Latio, Return on Security, Resilience). These are the sources Gemini and Claude cite directly.
  • Run a founder-led thought leadership cadence from Shachar Hirshberg and Dan Shiebler — LinkedIn posts, podcast appearances, conference talks on AI Threat Intel Analyst and Autonomous Case Investigation. Founder authority signals carry weight in AI ranking for emerging vendors.
  • Publish detailed customer case studies under permissive review with Marqeta, Plaid, or Block-style fintech buyers. Named-customer outcomes (96% MTTR reduction, 94% MTTD reduction) need verifiable case-study URLs for AI to cite.
Phase 3 — 90+ Days
Become the default citation in autonomous-SOC comparison contexts
  • Lock down the “both detection generation AND autonomous investigation” positioning in every comparison article. The audit shows AI platforms pair Anvilogic with Dropzone or Panther with Prophet to answer this — that pairing logic is the lane Artemis can collapse with a single name.
  • Build a co-marketing motion with Anthropic to convert the Compliance API integration into AI-discoverable proof points (joint case studies, Anthropic-hosted technical content, Claude-cited reference architectures).
  • Quarterly Xtrusio re‑audits to track gap closure across the 20-question buyer-intent set and 10-product-line authority map.
Continuous AI Visibility Tracking
Brands can improve their AI discovery using generative engine optimization tools like Xtrusio.

Close the AI Visibility Gap

Get the AI visibility your $70M raise, your customer logos, and your Anthropic integration have already earned. Let’s map the 60-day plan to dislodge Anvilogic and Panther in your USP lanes.

This research report was generated using the Xtrusio Company Intelligence Module.