Behind many online trading scams sits a question most victims never think to ask: who built the website they trusted? On 3 November, authorities in seven countries moved against a company suspected of supplying exactly that. The action against an investment fraud scheme with potential victims in more than 50 countries led to the seizure of over 100 servers and the arrest of seven suspects.
The operation was led by Germany's Itzehoe Public Prosecutor's Office, with support from Europol and Eurojust. Authorities in Albania, Cyprus, Finland, Germany, Israel, Kosovo and North Macedonia took part.
They searched 17 locations and seized servers, evidence and assets. The seven suspects were arrested with a view to extradition to Germany. Investigators estimate losses of more than EUR 4 million.
The Investment Fraud Supply Chain
The target was not a single scam group. Investigators say the company supplied fake trading platforms to criminal groups, operating what they describe as a crime-as-a-service model from Cyprus and Israel.
Its suspected clients ran fraudulent call centres in Albania, Kosovo and North Macedonia. Those call centre operators allegedly persuaded victims to hand over their savings. In other words, one business built the storefront, and others did the selling.
How Victims Were Drawn In
The scheme promised large, quick returns from trading in cryptocurrencies, foreign currencies or stocks.
It started with an advert. The investigation found that affiliate marketing was abused to attract potential victims, with deceptive ads steering people towards fraudulent investment platforms and exploiting their trust in online promotions.
The personal data collected this way was then sold to the criminal groups running the call centres. Operators contacted potential victims with supposed investment opportunities and turned that first flicker of interest into repeated demands for money.
Why Victims Kept Paying in This Investment Fraud
The method relied on making the scam look like it was working.
Victims were first encouraged to transfer a small amount. Their online accounts then showed fictitious profits, which made the platform seem credible and the investment successful. Supposed brokers used those fake gains, along with constant psychological pressure, to push victims into sending larger and larger sums.
The problem surfaced when victims tried to withdraw. Operators used delaying tactics to block payments. Eventually, contact stopped altogether, and victims were left unable to recover their money.
How a Couple's Complaint Exposed the Network
The case began in 2022, when a German couple reported that they could not withdraw money they had invested through an online trading platform.
A first international action day that year led to two arrests and seized evidence. With Europol-supported analysis, investigators identified seven more suspects, including suspected call centre managers. A second action day in 2025 brought further arrests and seizures of money and evidence. Analysis of that material exposed the suspected infrastructure provider and its links to call centre operators, setting up this month's operation.
In total, the case took four years to reach the company allegedly behind the platforms.
The Role of Europol and Eurojust
Europol's European Financial and Economic Crime Centre analysed the data, cross-checked it against Europol's databases and brought national investigators together to plan joint action. On the action day, Europol sent experts to Albania and Kosovo to support digital forensic work, and its Virtual Command Post supported the coordinated intervention.
Eurojust supported the execution of European Investigation Orders, European Arrest Warrants and freezing orders. Two joint investigation teams were set up at the agency: one involving Albania, Germany and Poland, and another involving Albania and Kosovo. Judicial actions on 3 November were coordinated from Eurojust's headquarters in The Hague.
David Ebner, president and founder of Content Workshop, is back on the show to talk about AI systems.
Content Workshop just launched the AI Maturity Model, a 10-question survey that benchmarks where your marketing team sits on the AI adoption scale compared to peers. David walks through the five stages, from solo prompting to fully systematized loop factories where agents handle the work and a QA agent named Karen checks everything. He also covers the new AI marketing courses Content Workshop is offering and what he is bringing to CyberMarketingCon including MD file giveaways that replace the traditional worksheet and a mysterious third booth.
Gary Survis is an operating partner at Insight Partners who has spent years on both sides of the table, as an operator and as a board member evaluating CMOs. He joins Gianna and Charles on CyberCMO Confidential to talk about what investors want from marketing leaders and why most CMOs miss it. The conversation covers board meetings, CMO interviews, eighteen-month tenure, and why the CMO role is horizontal by nature and what happens when you treat it like it isn’t.
Every year, DataTribe reviews hundreds of early-stage cybersecurity startup submissions for the DataTribe Challenge. Leo Scott, Managing Director and Chief Innovation Officer, sees the market shift before most people do.
He joins Gianna ahead of the Cyber Innovation Conference October 26th and 27th in Washington, DC, to talk through this year’s five finalists. The submissions are full of AI. Leo finds the companies one level down more interesting, solving what AI is changing about how attacks happen, how humans get tricked, and how agents get out of control. That is where the 2026 class points.
If you want to see them pitch live, the conference is October 26th and 27th in Washington, DC, at cyberinnovationconference.com.
Picture a regional marketing director on a Monday morning. She’s got 80 locations, a cup of coffee, and a nagging feeling that something is off in the Southeast. Reviews seem quieter. A couple of stores got new managers last month. Are those two things related?
Not long ago, finding out meant pulling an export, building a pivot table, pulling a second export from another system, and stitching it all together in a spreadsheet. Or, more realistically, filing a request with an analyst and waiting. Most weeks, the question simply never got asked.
Now she types it into the AI assistant her company already rolled out, and it answers using live data from the systems where that data actually lives.
The technology making that possible is the Model Context Protocol, or MCP. You’ve probably read plenty of “what is MCP” explainers by now, so I won’t add another. Instead, I want to talk about what changes for marketing teams, especially those managing lots of locations, when a cross-system question gets this cheap to ask. And what CIOs need to get right before it becomes the normal way marketing works.
The real shift: the join moves into the conversation
Here’s the short version of how MCP works. Your AI assistant (Claude, ChatGPT, Copilot, Gemini, or something your team built) is the host. Each business system, like your reputation platform, ad account, or CRM, publishes an MCP server. That server tells the assistant, in plain language, what it can do: “I can summarize reviews for a group of locations,” or “I can check whether your listings match across Google and Apple Maps.” The assistant reads that menu, picks the right tools for the question, and calls them.
That’s really it. But two things fall out of it that matter a lot for marketing.
First, the integration math gets much friendlier. Connecting four assistants to ten marketing systems used to mean up to 40 custom integrations. With a shared protocol, each assistant learns MCP once, and each system publishes one server. The conversation shifts from “should we build this integration?” to “which servers do we approve?”
Second, and this is the one people underestimate, the join moves. In a traditional stack, combining review data with POS and CRM data happens in a warehouse, on a schedule, through a pipeline someone has to own. With MCP, a regional leader can connect three systems to the same assistant and ask one question across all of them. The model does the join on the spot, right in the conversation, using that person’s own permissions.
That doesn’t replace your data warehouse. What it does is make a long tail of one-off, “I just want to know” questions affordable for the first time. In multi-location marketing, that long tail is where a lot of the useful insight lives.
It’s also not a science project anymore. Anthropic donated MCP to the Linux Foundation’s Agentic AI Foundation last December, with OpenAI, Google, Microsoft, and AWS all involved. And the July 2026 spec update made it far more enterprise friendly. It now scales on ordinary infrastructure, lets IT control which tools can run at the gateway, and gives tools a standard way to stop and ask a human, “Are you sure?” before doing anything.
Location marketing is the ultimate stress test
If MCP works for brick-and-mortar marketing, it’ll work almost anywhere. Do the math on a mid-sized brand. Three hundred locations, each listed on Google, Apple Maps, Bing, and Yelp, add up to 1,200 profiles that all need the same name, address, phone number, and hours. Every location collects reviews every week on multiple sites. And each one sits inside a hierarchy of district, region, brand, and maybe franchisee, where every level is supposed to see a different slice.
On top of that, the questions people ask are almost always comparative. Nobody asks, “What’s store 114’s rating?” They ask, “Which stores in the Southeast are slipping, and why?” Those are exactly the questions dashboards handle badly, and exports handle slowly.
My colleague Kristi Melani recently wrote here about how reputation data is now moving in two directions: outward to the AI engines customers use, and inward to the assistants your own teams use. MCP is the plumbing for that inward direction, and it raises the stakes on data quality on both sides. A wrong Sunday hour on Apple Maps used to mislead one customer. Now it can mislead that customer’s AI and your CMO’s AI at the same time. Customers already punish that mistake: Rio SEO found that 53% of consumers are unlikely to visit a business with incorrect listings.
What this actually looks like
The pattern I’m seeing in multi-location brands is pretty consistent. MCP doesn’t replace the reputation platform, the listings tool, or the ad account. It changes who gets to ask them questions, how often, and in what combinations. A few examples:
The field leader asks, “Which of my locations collected zero reviews this month, and what happened to their scores?” That used to be a weekly export, filtered by hand.
The local search lead asks, “Where do our hours or phone numbers disagree across Google, Apple Maps, Bing, and Yelp badly enough that an AI assistant would get them wrong?” That used to be a quarterly audit.
The paid media lead asks, “Which markets are we spending the most in where our average rating dropped below 4.0?” Google already publishes an official Google Ads MCP server, so that’s now one question instead of two dashboards and a spreadsheet.
The CMO asks for the first-half reputation readout by market, with the three biggest opportunities. That used to be two or three days of deck-building.
The industry-specific versions get even more interesting. A dealer group can read service department reviews next to repair order data. A property manager can line up resident complaints with open maintenance tickets, building by building. A restaurant brand can check whether a spike in “cold food” reviews lines up with a change in delivery partners. None of that needed new infrastructure. It just needed both systems to have a server, and the person asking to have access to both.
Tool design matters more than tool count
Here’s something that surprises a lot of teams once they start piloting: the shape of the tools matters more than how many there are.
An AI assistant works within a limited context window and a limited number of tool calls. If a regional director asks about 300 locations and the server only offers a one-location-at-a-time lookup, the model has to make 300 calls and juggle 300 results. It’ll be slow, it’ll be expensive, or it’ll quietly skip some. A server that can roll up an entire region in one call hands the model a small, tidy result it can actually reason about.
The same goes for definitions. If “sentiment” is calculated differently on every review site, the model will happily compare numbers that aren’t comparable. MCP can’t fix that, because it just moves data. Consistent metrics have to exist before the assistant ever sees them.
So when you evaluate a marketing MCP server, don’t ask how many tools it has. Ask whether it can answer a portfolio-level question in one or two calls, using metrics that mean the same thing everywhere.
Connecting is easy. Governing is the real work.
Hooking an assistant up to a marketing system now takes minutes. Deciding whether you should is where CIOs and AI governance teams are spending their time. Four questions come up in nearly every conversation I have.
Whose permissions does the assistant use? The safest setup is for each person to sign in as themselves, so the assistant inherits their existing access. A district manager who sees 12 locations in the platform should see 12 through the assistant, not 300. What you want to avoid is a shared API key baked into a connector, which quietly gives everyone the widest access anyone has.
Can it change anything? Reading a review summary and posting a public reply to a customer are very different levels of risk. Review responses and listing fixes are where AI promises the biggest time savings, but they’re also public and attached to your brand. Plenty of organizations are starting read-only and adding write access one tool at a time, with a human approving each action.
What is the assistant actually reading? This one is specific to reputation data, and it doesn’t get talked about enough. Reviews are written by the public. A review can include text aimed at an AI rather than a person, essentially an instruction hiding in plain sight. If the same conversation also has tools that can send email or edit records, that’s a real risk. Keeping reputation access read-only, and keeping anything that publishes behind a confirmation step, shrinks that exposure a lot.
Where does the data go? Review text can contain names, health details, and other personal information. In healthcare, a review can effectively confirm that someone is a patient. Governance teams need to know which AI tools are approved for which kinds of data, and what each AI vendor’s enterprise agreement says about retention and training.
Underneath all four is a simpler truth: MCP makes your data easier to reach. It doesn’t make it more accurate. Gartner predicts that through 2026, organizations will abandon 60% of AI projects that lack AI-ready data. For local marketing, “AI-ready” means one location list every system agrees on, consistent metrics, and listings that are actually correct. If your store IDs don’t match between the reputation platform, the POS, and the CRM, the assistant’s answers will break in exactly the spots an analyst used to patch by hand.
Where to start
The organizations moving fastest treat MCP as a joint project between IT and marketing, not a feature marketing flips on by itself. Here’s the path I’d recommend:
Start with the questions, not the systems. Ask your regional leaders for the ten questions they ask most and wait longest to get answered. That tells you which servers actually matter.
Go read-only, per user, in one region. Approve a small set of servers that inherit existing permissions, and pilot with a team that’s already using an enterprise AI assistant.
Clean up your location list before you scale. It’s unglamorous work. It’s also what decides whether cross-system answers are right.
Add write access slowly, with a human in the loop. Review responses and listing corrections is the obvious next step. Set your brand voice and compliance rules before the first reply goes out.
Whatever servers you evaluate, make sure they work across every major assistant. An open standard is what saves you from rebuilding everything the next time your company switches AI vendors.
From questions to agents
Right now, most marketing use of MCP is a person asking a question. The next phase is software asking on a schedule: an agent that checks every location each morning, flags a listing that’s drifted, or drafts replies to overnight reviews for a manager to approve. Those agents will run on the same servers answering human questions today.
It’s already happening in other parts of the business. When the latest MCP spec was announced, Honeycomb shared that nearly 20% of its monthly interactive queries now come from agents, not people. Marketing data won’t be far behind.
That’s why the choices made in this first wave matter so much. When we built the Reputation MCP Server, we made it read-only and permission-based from day one, because an agent is only as trustworthy as the data it can reach and the access it’s allowed.
The protocol is open to everyone. What will set brands apart is the quality of the data they plug into it and how carefully they govern it. For marketers responsible for hundreds of storefronts, the questions that used to wait for the quarterly review are about to get asked every Monday morning. The brands that get their data ready will see their locations more clearly than ever.
Kristi Melani is a transformational marketing executive with 30+ years of experience building high-performing teams, scalable marketing operations, and revenue-driving programs across high-growth startups and billion-dollar global enterprises. As CMO of Reputation, she leads global marketing strategy with a focus on brand trust, go-to-market excellence, and customer-centric growth.
Kristi is known for building marketing organizations from the ground up—creating clear processes, measurable frameworks, and cultures rooted in accountability, transparency, and performance. Prior to Reputation, she served as Chief Marketing Officer at Telesign and held senior marketing leadership roles at Anaplan, Poly, and Plantronics, where she led global teams, drove significant pipeline growth, and supported large-scale transformations and integrations.
Tracey Moon is a multi-time CMO and currently leads marketing at BlackCloak, a company that protects executives and their families in their personal lives to prevent corporate breaches. She joined Gianna and Charles on CyberCMO Confidential to talk about why she inserts herself into every part of the business, how she builds credibility before she rolls out her real plan, and why she calls her approach UMA: Unsolicited Moon Advice.
Gianna and Maria are together on the pod to talk about CyberMarketingCon 2026, happening November 15th through 18th in Austin, Texas.
The theme this year is Create Your Own Adventure. They talk about sessions, tracks, career stories from past conferences, the buddy program, the WhatsApp group, and why Maria is starting a petition to rename customer marketing to community building. She will have a clipboard.
There is also a sloth named Chompers. And Maria has a co-founder discount code for anyone who DMs her.
Lenin Alevski is an AI Security Engineer at Google who spent a week building a text-based cybersecurity survival game in his spare time. Accidental CISO puts you in the seat of the first security hire at a company where everything is on fire, and every fraught and heart-pounding decision leads to one of eight endings.
He sat down with Gianna to talk about how the game came together, why spec-driven development matters more than vibe coding alone, and what 12,000 players and zero marketing budget taught him about building indie cyber games.
Gianna sits down with Vab Dwivedi, VP of Digital Experience at CrowdStrike, Sandra Liu, a cybersecurity creator with over 200,000 LinkedIn followers, and Eric Becker, Enterprise Account Director at LinkedIn. They covered the CrowdStrike and Sandra Liu creator campaign, why LinkedIn reaches cybersecurity buyers differently than any other platform, how Vab thinks about measurement when the buying journey isn't linear, and why content quality will always matter more than volume.
When David Politis sits down with Gianna and Charles on CyberCMO Confidential, you’re getting something rare: A CEO who loves marketing telling you exactly what he thinks when he watches CMOs operate. Why the CRO wins if you let them, why he stops listening the moment someone says they have a playbook, why the best CMOs he ever wanted to hire became CEOs. And what one engineer just built for 2,000 accounts that made him realize most companies are nowhere near what is now possible.
Yael Grauer won a Pulitzer and got laid off in the same year. She is a freelance investigative reporter covering cybersecurity, privacy, and surveillance. She sat down with Gianna to talk about what companies get wrong when journalists come knocking, why lying in that moment makes everything worse, and what it looks like from the other side of every pitch, every breach response, and every "no comment" that ends up in a story anyway.
Brian Reed has been a CMO six times. He's also watched AI flatten marketing for the past two years and has said so out loud, including in a manifesto he published called AI is Flattening Marketing: Cue the Return of Mad Men.
On this CyberCMO Confidential episode, Brian, Gianna, and Charles Gold get into why storytelling and community are the two things AI cannot commoditize. Brian also talks about how he built a “crusty OT guy” persona to validate messaging authenticity, and what community-driven marketing looks like when your buyers wear steel-toe boots and aren’t online very often.
Gemma Goldstein had a quiet Thursday in the office, a domain someone spotted for sale, and an idea. A few short weeks later, Check Point's Exposure Management team had a browser-based arcade game live at exposuremanagement.ai with 11,000 players and a leaderboard.
The game launched as part of a broader campaign, and MQLs went up 100% that month. Gemma is careful not to credit the game alone. She and Gianna cover how the campaign came together across multiple channels, what she would do differently now, and how vibe coding an ROI calculator in an hour replaced a project quoted as taking weeks.
About Gemma: Gemma Goldstein is Head of Growth for Exposure Management at Check Point, a role she carried over from Cyberint after they were acquired in 2024. Before Cyberint, she spent three years at Envy, a B2B digital marketing agency focused on cybersecurity. Her background spans SEO, paid media, content, and creative strategy.
Andy Ellis has a roundup of the security vendors at Black Hat this year.
Key Takeaways: We have entered into an AI world. While nearly half of booths didn’t directly mention AI or agents in their taglines, the effects of AI are everywhere. Multiple spaces (Identity, SaaS, AppSec, Data) have almost every vendor leading with AI; existing unsolved problem areas just got worse.
At the same time, there’s a clear trichotomy in the market: tools that tell you how bad things are; tools that stop adversaries, and tools that prevent problems from occurring. While you’d suspect that the tools that fix things would dominate, the tools that merely tell you how bad things are seem to be frustratingly plentiful.
AI models weight trust signals differently in cybersecurity. A comprehensive framework for building entity authority as a security vendor, covering third-party corroboration, author entities, community presence, research credibility, & authority flywheel that compounds citation share.
Entrar
