Description

A focused course, tailored for you

The Cyber Threat Analyst Merchant-Risk Intel Playbook

Turn skimmer, ATO and storefront-fraud telemetry into merchant-facing intel the fraud, trust and detection teams act on this week.

Your skimmer report sits in a ticket three teams half-read. The merchant never saw it. The same family is already on tomorrow's scrape.

$199 one-time

Tailored to your situation. Access within 24 hours. 30-day money-back.

Includes a hand-built implementation playbook delivered alongside course access, generated for your specific situation.

Why this course

Cyber threat analysts inside a global commerce platform sit on a unique signal mix: real-time storefront JavaScript scrapes, checkout-page integrity monitoring, abuse-API anomaly feeds, credential-stuffing telemetry from the login layer, payment-fraud chargeback patterns, dark-web listings of stolen card stock, and OSINT on threat actors targeting hosted merchants. The collection is rich. The problem is the translation layer. Fraud ops want a per-merchant risk score with a recommended action. Trust and safety want a takedown-ready packet for the storefront, the typosquat domain, and the C2 IP. Platform engineering wants a detection-engineering ticket with a YARA or Sigma rule that ships into the WAF or the storefront integrity service. The merchant wants a plain-language email naming the script, the deadline, and the one button they press. Without an explicit playbook, every CTI finding ends up as a Slack thread, a partially completed Jira ticket, and a follow-up that fades. This course is the playbook for converting raw merchant-risk telemetry into the four artefacts that get action this week.

What you walk away with

Ship a weekly merchant-risk brief that fraud, trust and safety, and platform engineering read end to end and act on.
Convert a single Magecart-style scrape finding into a takedown packet, a fraud advisory, a detection rule, and a merchant email in one cycle.
Score merchants on storefront-integrity risk using a defensible model that survives review by trust and safety leadership.
Hand detection engineering a ready-to-deploy YARA, Sigma or WAF rule the same day the intel lands.
Run a credential-stuffing or ATO incident as the intel owner, not the bystander, with a clear hand-off to fraud ops.

The 12 modules

Module 1. The Merchant-Risk Intel Operating Model

Map the CTI seat inside a hosted-commerce platform: who consumes your intel (fraud ops, trust and safety, platform security engineering, merchant success), what artefact each team reads, and what authority each artefact carries. You leave with a one-page intel-to-action chart pinned to the wall and a weekly cadence that matches fraud-ops standups and trust and safety review meetings. The chart names every consumer, every artefact, and the latency target for each.

Module 2. Storefront JavaScript Supply-Chain Collection

Set up systematic checkout-page and storefront JS scraping across the tenant population, with diff detection on third-party tag loads, obfuscation flags, and known skimmer indicators. You learn how to tier scrape coverage by merchant volume and PII exposure, where to push results so the abuse-API team and the storefront integrity engineers can both consume them, and how to label findings so they survive the journey to a merchant email without losing precision.

Module 3. Credential Stuffing and ATO Signal from the Login Layer

Pull credential-stuffing telemetry from login and checkout APIs, fuse it with stolen-credential dump monitoring, and turn it into a per-merchant ATO risk view. You leave with a detection model that distinguishes broad-spray attacks from targeted enumerations against high-value merchants, a hand-off pattern to fraud ops for the chargeback impact view, and a merchant advisory template that names the affected accounts without leaking authentication details.

Module 4. Dark-Web and Forum Monitoring for Hosted Merchants

Build a focused dark-web and threat-actor forum monitoring beat tuned to hosted-commerce targets: combo lists tagged with platform-specific email patterns, stolen card stock with checkout-page provenance, scraped merchant data, and chatter naming specific storefronts. You learn how to handle the source-protection side, how to write findings that survive legal review, and how to triage which findings warrant a merchant notification.

Module 5. Threat-Actor Profiling for Commerce Targets

Profile the actor clusters that target hosted commerce: Magecart families, credential-stuffing crews, refund-fraud rings, marketplace-arbitrage operators, and platform-specific scammer networks. For each cluster you build a one-pager covering tradecraft, infrastructure patterns, monetisation, and the indicators most useful for detection. The profiles become the reference material the trust and safety queue uses when triaging new takedown requests.

Module 6. Writing the Weekly Merchant-Risk Brief

The flagship artefact: a weekly brief that fraud ops, trust and safety, platform engineering, and merchant success all read. You learn the structure (top three findings, by-merchant risk movement, new actor tradecraft, action items by team), the voice that survives a forwarded read by leadership, and the discipline that keeps the brief at one screen of content. Includes the template, three worked examples, and a peer-review pattern that catches drift before it ships.

Module 7. The Fraud-Ops Advisory: Translating Intel Into Chargeback Action

Fraud ops needs a different artefact than the weekly brief: a per-incident advisory naming the affected merchants, the projected chargeback exposure, the recommended rule changes in the fraud engine, and the decision authority needed to ship them. You build the advisory template, the data joins between intel telemetry and fraud-engine outputs, and the review pattern that keeps the advisory inside the same business day as the originating signal.

Module 8. The Trust and Safety Takedown Packet

Trust and safety operators need a takedown-ready packet for storefront removal, typosquat domain action, hosting-provider escalation, and registrar abuse contact. You build the packet structure (evidence, severity, jurisdiction notes, recommended action), the artefact bundle that travels with it (scrape evidence, network indicators, contextual context for the abuse desk), and the hand-off pattern that gets a clean decision in hours, not days.

Module 9. The Detection-Engineering Ticket

Detection engineering wants a ready-to-deploy artefact, not a narrative. You learn how to convert each piece of intel into the form the detection team needs: a YARA rule for malicious script content, a Sigma rule for login-layer anomaly, a WAF signature for checkout-page injection, or a storefront-integrity ruleset update. Every module finding maps to a ticket the detection team can merge into production within the same sprint.

Module 10. Merchant Notifications That Get Acted On

The merchant-facing email is the artefact that decides whether the incident closes. You build the notification template, the severity ladder, the deadline language, and the one-button action pattern. You also build the routing logic for which merchants get a direct outreach from the merchant success team versus a self-service notification, and the measurement loop that tells you which notifications were acted on within the deadline.

Module 11. Running an Incident as the Intel Owner

When a live skimmer outbreak or a credential-stuffing wave hits, the CTA can either be the bystander writing the postmortem or the intel owner running the response. You build the incident playbook: the standing channel, the rolling indicator feed, the per-hour brief format, the hand-offs to fraud ops, trust and safety, platform security engineering, and merchant success, and the close-out artefact that becomes the case study for the next outbreak.

Module 12. Measuring Intel That Converts to Action

A CTI seat survives on demonstrable conversion. You build the measurement layer: action rate per finding, time from signal to action, merchant-protected count, chargeback-exposure prevented, detection rules shipped, takedowns executed, ATO incidents intercepted. The dashboard becomes the artefact that defends the seat in the next budget cycle and that surfaces the gaps in the intel pipeline before they become quarterly problems.

How this addresses your situation

Specific modules that map to what you said you are dealing with.

Magecart-style skimmer found on a Plus storefront, three teams looking at it, no merchant notification shipped yet.

Credential-stuffing burst against the login API targeting a known high-value merchant, fraud ops asking what to do.

Dark-web listing surfacing scraped merchant data, trust and safety wants a takedown packet and legal wants source protection.

Detection engineering asking for the YARA or Sigma rule for the actor cluster you've been tracking for two weeks.

What you get with this course

12 written modules in the Art of Service learning environment, each ending in the concrete artefact for the role.
Downloadable templates: the weekly merchant-risk brief, the fraud-ops advisory, the trust and safety takedown packet, the detection-engineering ticket, the merchant notification.
Worked examples for each artefact based on a Magecart-style finding, a credential-stuffing wave, and a dark-web listing scenario.
Reference profiles for the major actor clusters targeting hosted commerce.
Hand-built implementation playbook tuned to your merchant mix and current detection stack, provisioned alongside course access.

What you will have in hand by Day 1, Week 1, Month 1

Within 24 hours: learning environment account provisioned, all 12 modules accessible, templates and worked examples downloadable.

Within 24 hours: hand-built implementation playbook delivered alongside course access, tuned to your merchant mix and current detection stack.

Week 1: weekly merchant-risk brief structure adopted, first issue shipped.

Week 2: fraud-ops advisory and trust and safety takedown packet templates in production use.

Week 4: detection-engineering ticket pattern integrated with the detection team's intake.

Week 6: measurement dashboard live, first quarterly action-rate review scheduled.

Before and after

Before

Intel findings live in Slack threads and half-completed Jira tickets. Fraud ops, trust and safety, and platform engineering each read a different slice. Merchants find out from chargeback patterns, not from you. The CTA seat is described as valuable but cannot point to last quarter's action rate.

After

Every finding lands as four artefacts: the brief, the advisory, the takedown packet, the detection ticket. The merchant gets the notification before the chargeback. The action rate, the time-to-action, and the protected-merchant count are reported every quarter. The CTA seat is the intel owner, not the bystander.

What happens if you do not address this

The skimmer the merchant first learns about from their chargeback report becomes the trust event nobody recovers. The credential-stuffing wave that ATO'd three high-value merchants in one week becomes the executive question with no clean answer. The intel seat that cannot point to converted action becomes the line item on the next budget review.

Who it is for

You are a cyber threat analyst inside a global hosted-commerce platform. You sit between fraud operations, trust and safety, platform security engineering, and the merchant-facing relationship managers. You run open-source collection, dark-web monitoring, and storefront integrity analysis. You read scrape diffs, abuse-API alerts, login telemetry, and chargeback patterns. You write intel reports that need to be readable by fraud analysts, trust and safety operators, detection engineers, and the merchants themselves, often in the same week.

Who this is NOT for. Generalist SOC analysts in single-tenant enterprises, network defenders without merchant-facing responsibility, fraud-only analysts with no threat-intel remit, and managers who do not write intel artefacts themselves.

How it arrives

Text-based course in the Art of Service learning environment, plus downloadable templates and worked examples for every module, plus the hand-built implementation playbook delivered alongside course access.

Time investment. Roughly 14 to 18 hours across the 12 modules, plus the time you spend wiring the artefacts into your existing workflow. Most learners ship the weekly brief in week one and the takedown packet pattern by week three.

Why $199 is the right number

Generic CTI training (SANS FOR578 and similar) teaches actor tracking and finished intel writing in a single-tenant enterprise context. This course is built for the CTA seat inside a multi-tenant hosted-commerce platform, where the intel customer is fraud ops, trust and safety, detection engineering, and the merchants themselves, and where the artefact set is specifically the merchant-risk brief, the fraud advisory, the takedown packet, the detection ticket, and the merchant notification.

FAQ

Does this assume access to a specific telemetry stack?

No. The templates and patterns work against whatever you have: scraping output, login-layer telemetry, abuse-API feeds, dark-web monitoring. The implementation playbook is tuned to the stack you describe at provisioning.

Is this a tooling course?

No. It is an artefact and operating-model course. The tooling you already have is the substrate. The course teaches what to produce, for whom, on what cadence, in what shape.

How is the implementation playbook tailored?

On provisioning you describe your merchant mix, your current detection stack, and the teams the intel feeds. The playbook is hand-built to those answers and delivered alongside course access.

Can the team use this, or just one analyst?

The course is licensed per learner. The artefact templates and the implementation playbook are intended to be used by the team the learner sits in. A team licence is available on request.

30-day money-back guarantee. If after a week of working through the materials this is not what you needed, reply to the receipt email and a full refund is processed. No questions, no forms.

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.