Of 60 answers

The most named companies in the study, and S&P 500 financials, with how many of their 60 AI answers named them.

Back to the study
AI visibility statementFinance AI Visibility Study 2026Issued October 14, 2026
Account holder
Statement period
October 9 to 14, 2026
when the AI assistants were asked
Account no.
448 of 2,219
its rank by overall score
Closing balance

Named in

1of 60 answers

by 1 of 4 assistants

Statement note

What this means for SISA

AI assistants rarely name this fraud and identity company in their answers, mentioning it just once in 60 answers. Its overall score is 38 out of 100, which ranks 448th of 2,219 companies. The website passes 5 of 14 technical checks for readiness with AI tools.

We asked 15 unbranded questions about its market, so no question mentioned SISA by name. AI assistants named it in 1 of the 60 answers. Counting only how often it is named, it ranks 267th of 574 in its sector. The typical financial technology and data company is named in 1.7% of answers.

Named fewer than three times in total. Read this as a sign that AI assistants rarely bring this company up, not as an exact comparison with its peers.

Overall score
38Overall score
  • Visibility5
  • Citations1
  • Sentiment100
  • Site quality80
  • AI readiness36
Summary of account
Named by AI in
1.7%
1 of 60 answers
Share of mentions
25.0%
of the mentions of it and its 5 peers
AI readiness
5/14
technical checks passed. Typical for its sector: 4
Rank in its sector
127 of 574
by overall score, where the typical financial technology and data company scores 29

Get the full report on SISA as a PDF

Enter your work email to download it.

We’ll use your email to follow up about this report. Privacy policy

Line itemsSection 01

Every question, and where the company came up

SISA came up on 1 of 15 questions. Open any window to read that answer.

  1. 01Which security approaches serve as alternatives to managed detection and response for payment system protection?0/4
  2. 02What are some alternatives to AI-powered threat detection for safeguarding payment data?0/4
  3. 03How do traditional cybersecurity approaches compare with AI-driven threat detection for payment platforms?0/4
  4. 04What is the typical cost range for enterprise-level AI cybersecurity platforms for payment security?0/4
  5. 05Are there scalable pricing models for growing payment processing businesses looking for AI security solutions?0/4
  6. 06How can I determine if my payment systems need advanced AI-based cybersecurity services?0/4
  7. 07What are the best AI-powered cybersecurity solutions for protecting payment ecosystems?1/4
  8. 08How do I address frequent false positives in AI-based threat detection software for payment systems?0/4
  9. 09What features should I look for in a cybersecurity solution tailored for the financial payment ecosystem?0/4
  10. 10What are common use cases for AI-powered governance and compliance automation in payment data protection?0/4
  11. 11What steps should enterprises take if a potential breach is detected in their payments infrastructure?0/4
  12. 12Which cybersecurity tools specialize in guarding financial data in enterprise payment systems?0/4
  13. 13Is investing in AI-powered compliance tools for payment security worth it for large organizations?0/4
  14. 14What are the differences between managed detection and response services and in-house security teams for payment processing environments?0/4
  15. 15How can digital forensics and incident response help financial organizations recover from security breaches in payment systems?0/4
Total namedOpenAI: 0/15Claude: 0/15Perplexity: 1/15Gemini: 0/15All assistants: 1/60
named the companyanswered without naming itdid not answer

Columns, left to right: OpenAI, Claude, Perplexity, Gemini. Across the study, on 50% of the questions where a company is named, only one AI assistant names it.

Transaction detailSection 02

What Perplexity actually said

An answer that names the company

Perplexitynamed the company

“What are the best AI-powered cybersecurity solutions for protecting payment ecosystems?”

… Managed detection and response · SISA ProACT MXDR, Microsoft Sentinel services, CrowdStrike Falcon Complete · Organizations without a large 24/7 security operations center; useful for continuous monitoring, investigation, and response.

Payment-data and API protection · Cloudflare, Akamai, Imperva, Google Cloud Armor · E-commerce, payment gateways, APIs, bot mitigation, DDoS protection, and protecting checkout flows from automated abuse.

Identity and account-takeover prevention · Okta Adaptive MFA, Cisco Duo, BioCatch, LexisNe …

The AI’s answer as we received it, shortened and not checked for accuracy.

PayeesSection 03

Where the mentions went

The companies these answers named, and how the mentions were split between this company and its peers.

Also named

CompanyTimes named
  1. 01
    Netskope
    1

Share of mentions

CompanyShare
  1. Palo Alto Networks Cortex
    50.0%
  2. SISA
    25.0%
  3. Netskope
    25.0%
  4. Trend Micro
    0.0%
  5. Cybereason
    0.0%
  6. Trellix
    0.0%
ReferrersSection 04

Websites the AI used as sources

How many times each website was listed as a source in these answers

  1. 01
    corporate.visa.com
    15
  2. 02
    verifone.com
    6
  3. 03
    fraudio.com
    6
  4. 04
    sourceforge.net
    6
  5. 05
    listings.pcisecuritystandards.org
    4
  6. 06
    abbacustechnologies.com
    4
  7. 07
    arxiv.org
    4
  8. 08
    wjarr.com
    3
  9. 09
    mastercard.com
    3
  10. 10
    bacancytechnology.com
    3

SISA is in the study as a fraud and identity company.

By assistantSection 05

Which AI assistants named the company

Answers naming it, out of answers received

Perplexity
1/15
Claude
0/15
Gemini
0/15
OpenAI
0/15
Market comparisonSection 06

How SISA compares with all 2,219 companies

Every tower is a company in the study, taller for a higher overall score and lit where AI assistants named it. Its own group stays lit. Hover a tower to see it, and click to open it.

What makes up the score

Each part is scored out of 100.

Visibility
5

How often AI assistants name the company, and how that compares with its peers

Citations
1

How often AI uses the company's own website as a source

Sentiment
100

How positively AI describes the company

Site quality
80

How well built the website's own pages are

AI readiness
36

How easily AI tools can read and use the website

Visibility is calculated from the answers on this page. How the score is built.

Against its sectorSection 07

Compared with financial technology and data companies

574 companies in this sector. The gold tick on each bar is the typical company.

Overall score
38
Ranks 127 of 574. Typical company: 29
Named in answers
1.7%
Ranks 267 of 574. Typical company: 1.7%
AI readiness
36
Ranks 83 of 574. Typical company: 29
Against its categorySection 08

Compared with fraud and identity companies

171 companies in this category. The gold tick on each bar is the typical company.

Overall score
38
Ranks 45 of 171. Typical company: 30
Named in answers
1.7%
Ranks 85 of 171. Typical company: 1.7%
AI readiness
36
Ranks 35 of 171. Typical company: 36
AI agentsSection 09

Ready for AI agents

What this website publishes for an AI agent to read, use and pay through, on a scale of six levels.

  1. 0
  2. 1
  3. 2
  4. 3
  5. 4
  6. 5

Level 1: Basic web presence. Two of robots.txt, a sitemap and Link headers are in place.

To reach level 2:
  • Content Signals
Can an AI agent check out here?

Not by itself. SISA publishes none of the agent payment protocols we look for. An agent would have to click through the website like a person.

Agent payment protocols
  • x402Not present
  • MPPNot present
  • AP2Not present
  • ACPNot present
  • UCPNot present
Interfaces for AI agents
  • MCP Server CardNot present
  • A2A Agent CardNot present
  • Agent SkillsNot present
  • API CatalogNot present
  • llms.txtPublished
  • WebMCPCould not check
Show every check, for your technical team
Discoverability
72 of 100
  • robots.txtPassed

    A valid robots.txt with crawl rules at the site root.

    study: 86.1%
  • SitemapPartly met

    Serve an XML sitemap and reference it from robots.txt.

    study: 87.9%
  • Link headersNot met

    Send Link headers such as rel="api-catalog" or rel="describedby" on the homepage.

    study: 1.5%
  • DNS for AI DiscoveryNot present

    Advertise agent endpoints with SVCB records under _agents.

    study: 4.3%
Content accessibility
50 of 100
  • Markdown negotiationNot met

    Return a markdown version when asked with Accept: text/markdown, for example at the CDN.

    study: 6.2%
  • llms.txtPassed

    An llms.txt that tells language models what the site offers.

    study: 31.0%
Bot access control
32 of 100
  • AI bot rulesPartly met

    Add explicit robots.txt groups for the main AI crawlers.

    study: 92.7%
  • Content SignalsNot met

    Add a Content-Signal line for search, ai-input and ai-train to robots.txt.

    study: 2.3%
  • Web Bot AuthNot present

    Only relevant if you run your own agents or crawlers: publish a signing key directory.

    study: 0.3%
APIs, auth and MCP
0 of 100
  • API CatalogNot met

    List public APIs in a linkset at /.well-known/api-catalog.

    study: 1.3%
  • OAuth discoveryNot met

    Publish OpenID Connect or OAuth server metadata under /.well-known.

    study: 6.3%
  • OAuth Protected ResourceNot met

    Serve protected resource metadata at /.well-known/oauth-protected-resource.

    study: 5.3%
  • auth.mdNot met

    Publish an auth.md describing how agents sign in.

    study: 0.2%
  • MCP Server CardNot met

    Offer an MCP server and publish its server card under /.well-known.

    study: 0.8%
  • A2A Agent CardNot met

    Publish an agent card at /.well-known/agent-card.json.

    study: 0.2%
  • Agent SkillsNot met

    Publish a skills index with the main tasks agents can do.

    study: 1.0%
  • WebMCPCould not check

    Register key actions such as search or cart as WebMCP tools.

    study: 0.0%
  • ARD manifestNot met

    Publish an ai-catalog.json listing every agent interface.

    study: 0.2%

Score 35 of 100 · 97% of checks could run · checked on October 9, 2026 · sisa.ai · 2,200 companies scanned

See how every company did, and how this was measured.

AI readinessSection 10

The checks behind the AI readiness score

5 of 14 passed. This list is for your technical team. The percentage is how many companies in the study pass each check.

These 14 checks ran during the audit and are what the AI readiness part of the overall score counts. The scan above ran later and is stricter on Link headers and Markdown, so a check can pass here and not there.

Show the 14 checks

Discoverability

  • Passedrobots.txt publishedstudy: 85.1%
  • PassedXML sitemapstudy: 79.4%
  • PassedHTTP Link headers (RFC 8288)study: 42.5%

Content Accessibility

  • Not metMarkdown content negotiationstudy: 7.4%
  • Passedllms.txt publishedstudy: 28.1%
  • PassedToken budget (page weight)study: 97.3%

Bot Access Control

  • Not metExplicit AI bot rulesstudy: 11.5%

API / Auth / MCP

  • Not metAPI Catalog (.well-known/api-catalog)study: 1.1%
  • Not metOAuth Authorization Server discoverystudy: 5.2%
  • Not metOAuth Protected Resource discoverystudy: 4.6%
  • Not metMCP Server Cardstudy: 0.2%
  • Not metA2A Agent Cardstudy: 0.0%
  • Not metWebMCP toolsstudy: 0.3%
  • Not metAgent Skills declaredstudy: 0.1%
AI crawlersSection 11

What AI crawlers may read (robots.txt)

This website sets no rules for any AI crawler by name, like 86% of the websites whose rules we could read. Every AI crawler follows its general rules.

Training AI

  • GPTBotallowed
  • ClaudeBotallowed
  • Anthropic-AIallowed
  • Google-Extendedallowed
  • Applebot-Extendedallowed
  • Meta-ExternalAgentallowed
  • FacebookBotallowed
  • Bytespiderallowed
  • CCBotallowed
  • Diffbotallowed
  • Omgilibotallowed

AI search

  • OAI-SearchBotallowed
  • Claude-SearchBotallowed
  • PerplexityBotallowed
  • Applebotallowed
  • Amazonbotallowed
  • YouBotallowed

Answering questions

  • ChatGPT-Userallowed
  • Claude-Userallowed
  • Perplexity-Userallowed
  • DuckAssistBotallowed
  • MistralAI-Userallowed

Read from the website’s own rules file (robots.txt). An asterisk marks a crawler the file names. The rest follow its general rules. “Partly” means only some pages are blocked.

Take it with you

Get the full report on SISA as a PDF

Every number on this page in a PDF you can keep and share with your team. Enter your work email to download it.

We’ll use your email to follow up about this report. Privacy policy

Next step

Want AI assistants to name your company?

Book a 30-minute demo to see Rezolve Ai in action and what it can do for your business.

Request a demo