Cyber threat intelligence software should help analysts judge whether a feed will be useful inside a CTI platform before they spend time importing it. This guide looks at scam-focused collection from that angle: what data arrives, how it keeps source history, and how it fits analyst workflows.
If you want a deeper feed checklist, current weekly scam briefings, or monthly scam data reports, those are separate resources. To review Active Defense, including a free feed sample for qualified leads and a 90-day pilot for up to 1,000 sessions at a fixed price, start on the Active Defense home page.
A Feed Has to Deliver More Than Raw Indicators
Cyber threat intelligence software is only as useful as the structure and relevance of the data it receives. For analysts importing into a CTI platform, a feed needs to support action, not just collection. A flat dump of disconnected values creates more triage work than intelligence value because analysts still have to determine what matters, how items relate, and where to start.
In scam investigations, the most useful indicators often come straight from scammer conversations. These include:
- crypto wallets
- bank details
- phone numbers
- messenger accounts
- links
- gift cards
- payment handles
- file hashes
- photo hashes
That mix matters because it reflects how scam operations actually work. Some values point to payment rails, such as crypto wallets, bank details, gift cards, and payment handles. Others map communication channels, including phone numbers and messenger accounts. Links can show delivery infrastructure, while file and photo hashes help identify reused media across cases.
For a feed to be operational, those indicators need enough context to be usable inside the analyst workflow. Teams should not have to return to raw chat logs for every triage step just to understand what a value is, why it was extracted, or how it may connect to a broader case. Good structure turns collection into something searchable, filterable, and ready for enrichment.
Breadth also matters. Scam conversations rarely stay in one channel or one payment method. During a single engagement, an operator may move from chat to email, shift from one payment rail to another, or reuse the same media and accounts across multiple approaches. A useful feed has to reflect that movement rather than reducing it to isolated observables.
Evidence Retention and Lineage Matter as Much as Collection
Cyber threat intelligence software should preserve source evidence in a form analysts can revisit, test, and export again later. For CTI teams, lineage is part of data quality: an indicator is more useful when its origin is retained alongside the extracted value.
Active Defense stores each message as it arrives. That preserves the original record even if a scammer later removes it from the chat with Telegram’s "delete for everyone." In scam work, that matters because source conversations can change after first contact, while analysis and correlation often happen later.
Retention also needs to cover more than message text. Scam operators reuse media, attachments, and account handoff patterns across conversations, so preserved evidence should include the objects that support later matching and review.
In Active Defense, stored source material includes:
- messages captured on arrival
- photos, voice messages, and files kept by SHA-256
- photos also kept with a perceptual hash
Those records support two different review tasks. SHA-256 helps with exact reuse of a file or image. A perceptual hash helps surface a photo that was edited but still appears to be the same underlying asset.
Lineage matters for export as well. Each customer gets a feed with STIX 2.1 export that can be rebuilt from the stored history. That gives analysts a way to regenerate output when they need to revisit prior collection, rerun matching logic, or trace an indicator back to preserved source material instead of relying only on a derived entry in the feed.
Scoring and Actor Linking Make a Feed Operational
Cyber threat intelligence software should help analysts prioritize, not just collect more observables. In practice, a feed becomes useful when it helps a CTI team decide what to review first, what to correlate next, and what can wait.
In Active Defense, each extracted indicator gets a confidence score. That score is based on two inputs at a high level:
- a structural check
- an LLM
This matters because scam-derived data is messy. A value may look like a wallet, account, phone number, or bank detail at first glance, but analysts still need a way to judge how much weight to give it in triage. Scoring makes that possible. Teams can sort, filter, and review indicators by confidence instead of treating every extracted value as equally useful.
The wording matters here. Indicators are scored, not verified. That distinction is important for analysts who need to assess evidence and decide how an item should be used downstream inside their CTI platform.
Scam investigations also need clustering, because separate chats often turn out to belong to the same operator or crew. Looking at isolated conversations can hide that connection.
Active Defense links chats to one actor when they share:
- an indicator
- a reused photo
- a forwarded account
That linking changes the value of the feed. Instead of working from one wallet, one handle, or one phone number at a time, analysts can move toward campaign-level understanding. They can see reuse across conversations, identify patterns that repeat under different pretexts, and avoid doing the same case-building work more than once.
For CTI teams, that combination of scoring and actor linking turns extracted data into something more operational: prioritized indicators with relationships that help explain how separate scam conversations fit together.
Integration Has to Fit the Way CTI Teams Work
Even strong collection loses value if the output is difficult to import into the tools analysts already use. For CTI teams, format and delivery are part of the product, not an afterthought. If a feed creates extra transformation work before it can be ingested, searched, or matched, it slows down the workflow it is supposed to support.
Each Active Defense customer gets their own feed with STIX 2.1 export. That matters because teams do not all work the same way. Collection priorities differ. Matching rules differ. Downstream workflows differ. A customer-specific feed is more useful than a one-size-fits-all stream when analysts need data that fits how their platform and process already operate.
Structured export also reduces manual handling. Instead of spending time reformatting values from chat-derived collection into something a CTI platform can accept, teams can move more quickly into enrichment, triage, and correlation. In practice, that makes scam-derived intelligence easier to operationalize.
Rebuildability matters here as well. Because feed output can be rebuilt from stored history, teams can regenerate exports when operations change, such as after schema updates, revised matching logic, or retrospective investigations that require older source material to be expressed again in a new format.
For buyers evaluating where the service operates, the console and API run on Azure in Germany. Analysts who want to follow ongoing scam patterns derived from live conversations can use the site’s briefing coverage alongside the feed itself.
Human Review and Buying Friction Affect Trust
Trust in cyber threat intelligence software depends on more than export format and indicator coverage. Analysts also need to understand how the data is generated, because collection method shapes how much confidence they place in the feed and how they use it inside a CTI platform.
Active Defense runs AI decoy personas on Telegram and by e-mail to keep scammers talking and turn what they send into a threat-intelligence feed. Replies are drafted by AI and approved by a human operator by default.
That human review matters in practice. It helps maintain control over outbound engagement while still supporting scalable collection. For analysts, that means the feed is not only structured for ingestion, but also produced through a process with visible oversight at the response stage.
Buyers also usually want a low-friction way to judge feed quality before committing to a broader rollout. The two evaluation paths are:
- a free feed sample for qualified leads
- a 90-day pilot for up to 1,000 sessions at a fixed price
Those options give teams a practical way to assess whether the output fits their platform, workflow, and prioritization needs.
Questions analysts ask about cyber threat intelligence software
How is scam intelligence different from a general indicator feed?
Scam-focused cyber threat intelligence software collects directly from live conversations with scammers, not only from crawls, reports, or blocklists. That changes the mix of data in the feed. Analysts often need payment and contact artifacts that appear during the scam itself, such as:
- crypto wallets
- bank details
- phone numbers
- messenger accounts
- links
- gift cards
- payment handles
- file and photo hashes
For CTI teams, that makes the feed useful for matching scam activity that would be missed by feeds centered on malware or infrastructure alone.
What does human review change in a scam-collection workflow?
Collection method affects how analysts judge a feed. In Active Defense, AI decoy personas keep scammers talking on Telegram and by e-mail, but replies are drafted by AI and approved by a human operator by default. That means outbound engagement is not fully unattended. For analysts evaluating a source, this helps explain how conversations are maintained and how collected material reaches the feed.
Can the feed support case building, not just matching?
A CTI feed is more useful when it helps analysts assemble a case as well as search for single observables. Active Defense links chats to one actor when they share an indicator, a reused photo, or a forwarded account. That gives analysts a way to group related conversations and review scam activity at the actor level instead of handling each chat as a separate event.
What practical checks matter before a pilot?
Before testing cyber threat intelligence software, analysts usually want to confirm a few basics:
- whether each customer gets their own feed
- whether STIX 2.1 export is available
- where the console and API run
- what evaluation path is offered
Active Defense provides a customer-specific feed, STIX 2.1 export, runs its console and API on Azure in Germany, and offers a free feed sample for qualified leads plus a 90-day pilot for up to 1,000 sessions at a fixed price.
Choosing Software That Produces Usable Intelligence
Cyber threat intelligence software should reduce analyst work after ingestion, not move it downstream. For CTI teams importing feeds into a platform, the key question is whether the output arrives ready to sort, compare, and investigate.
A practical review point is whether the software gives you:
- indicators that match scam operations
- confidence scores for triage
- actor-level grouping from shared indicators, reused photos, or forwarded accounts
- preserved source history that supports rebuilding the feed
- STIX 2.1 export for each customer’s feed
Active Defense collects from Telegram and e-mail scam conversations and outputs customer-specific feeds built for that workflow.