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.
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.
Platform Scorecard
Artemis Security citation rate across AI platforms
AI Visibility Leaderboard
Who owns the AI conversation — total citations across all platforms
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.
| # | Query Topic | Cluster | Claude | ChatGPT | Gemini |
|---|---|---|---|---|---|
| 1 | Splunk Cloud cost / no-ingest SIEM | Federated 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?” | |||||
| 2 | Env-specific detection generation | Adaptive 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?” | |||||
| 3 | Threat report → shipped detections in hours | Threat 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?” | |||||
| 4 | Next-gen SIEM alternative to Splunk | Federated 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?” | |||||
| 5 | AI SOC end-to-end investigation | Autonomous 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?” | |||||
| 6 | AI-native vs bolted-on | AI-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?” | |||||
| 7 | Multi-source correlation | Multi-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?” | |||||
| 8 | Threat intel → auto coverage gaps | Threat 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?” | |||||
| 9 | Generate detections AND investigate | Autonomous 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?” | |||||
| 10 | Detect on data in own storage | Federated 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?” | |||||
| 11 | Board-level MTTD/MTTR proof | MTTD/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?” | |||||
| 12 | Natural language vs SPL/KQL | AI 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?” | |||||
| 13 | Multi-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?” | |||||
| 14 | Rule inventory + MITRE coverage | Adaptive 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?” | |||||
| 15 | Lean SOC (6 people) automation | AI 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?” | |||||
| 16 | Snowflake data lake + detection-as-code | Detection-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?” | |||||
| 17 | Investigation automation fintech/SaaS | Autonomous 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?” | |||||
| 18 | Shadow AI visibility | Environment 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?” | |||||
| 19 | Unified case mgmt + investigation + response | Autonomous 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?” | |||||
| 20 | Full SIEM+SOAR+TI+case consolidation | AI-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?” | |||||
| TOTAL | 0/20 (0%) | 0/20 (0%) | 0/20 (0%) | ||
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?”
“Is there a platform that does both — generates the detections AND investigates the alerts autonomously?”
“Which platforms let you keep your data in your own storage (Snowflake, S3) and run detections against it?”
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.
AI Topic Authority Map
Product line × platform heatmap — who owns each piece of the Artemis pitch
| Product Line | AI Leader | Artemis Status |
|---|---|---|
| Federated Query / No-Ingest SIEM | Panther (Databricks) | INVISIBLE (0/3) |
| Adaptive Detection Engineering | Anvilogic | INVISIBLE (0/3) |
| AI Threat Intel Analyst | CrowdStrike Charlotte AI | INVISIBLE (0/3) |
| AI Mode / Natural Language | Google SecOps + Gemini | INVISIBLE (0/3) |
| Autonomous Case Investigation | Dropzone AI / Prophet Security | INVISIBLE (0/3) |
| Multi-Source Correlation | Palo Alto Cortex XSIAM | INVISIBLE (0/3) |
| Environment Intelligence / Shadow AI | Reco AI / Nudge Security | INVISIBLE (0/3) |
| AI-Native Architecture | Palo Alto / CrowdStrike | INVISIBLE (0/3) |
| MTTD/MTTR Outcomes | Palo Alto Cortex XSIAM | INVISIBLE (0/3) |
| Detection-as-Code on Data Lake | Anvilogic + Panther | INVISIBLE (0/3) |
4 queries • Q1, Q4, Q10, Q16
2 queries • Q2, Q14
2 queries • Q3, Q8
1 query • Q12
4 queries • Q5, Q9, Q17, Q19
2 queries • Q7, Q13
1 query • Q18
2 queries • Q6, Q20
1 query • Q11
1 query • Q15
▹ Every Artemis revenue line is invisible on every platform. The category vacuum extends across the entire product portfolio — not just the headline message.
Methodology
How we conducted this Xtrusio AEO/GEO Audit
This research is based on Xtrusio’s proprietary AI visibility analysis framework.
Recommendations
Prioritized actions to close the AI visibility gap
- 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.
- 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.
- 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.
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.


