People who search for threat intelligence feed usually need a working definition fast: a feed is structured intelligence that analysts import into CTI platforms to enrich records, correlate activity, and support investigations. The key questions are practical ones: what data is in the feed, how it is collected, how much context comes with it, and whether it fits operational workflows.
This article is written for analysts evaluating feeds for ingestion and use. It uses one collection model from Active Defense, points to weekly scam briefings from live conversations, and references monthly scam data reports.
Read more: What Is an IoC Feed?
The sections below focus on:
- contents
- sources
- quality checks
- formats and delivery
- when a specialized feed is worth adding
What a threat intelligence feed contains
The basic unit in a threat intelligence feed is the indicator. An indicator is a piece of data that may help identify malicious infrastructure, accounts, payment routes, or artifacts tied to an operation.
Common indicator types include:
- URLs and domains
- IP addresses
- Email addresses
- Phone numbers
- File hashes
- Account identifiers
- Payment identifiers
- Related metadata
A feed becomes more useful when it carries context, not just raw values. Analysts usually need details such as first-seen time, source, confidence, type, relationships, and notes that help them decide whether to block, enrich, watchlist, escalate, or ignore a record. Without that context, a feed is harder to tune and easier to misuse.
This applies beyond classic malware or network telemetry. Scam operations also expose indicators that matter in investigations and correlation work. Those can include crypto wallets, bank details, messenger accounts, gift cards, payment handles, and photo or file hashes.
Active Defense is one example of this collection model. It runs AI decoy personas that keep scammers talking on Telegram and by email, then turns what the scammers hand over into structured intelligence. From every message, Active Defense extracts crypto wallets, bank details, phone numbers, messenger accounts, links, gift cards, payment handles, and file and photo hashes.
Each extracted indicator is scored with a confidence score. That matters in downstream workflows because analysts do not have to treat every record the same way. They can filter for higher-confidence records, send mid-confidence items for review, or use lower-confidence data for enrichment and actor tracking rather than direct blocking.
Where threat intelligence feeds come from
A threat intelligence feed can start from very different collection methods, and that origin shapes what analysts can do with the data after ingestion.
Common sources include:
- network and endpoint telemetry
- malware and file analysis
- abuse reports and user submissions
- incident response casework
- direct interaction with malicious actors
For CTI teams, source matters because it affects the kind of evidence a feed can carry. Passive collection often captures technical artifacts seen after delivery or execution. Direct interaction can surface operational details earlier: the accounts, payment routes, contact points, and media an actor sends during the exchange itself.
That is the model Active Defense uses. It runs AI decoy personas on Telegram and by e-mail and turns what scammers send into a threat-intelligence feed. Extracted data includes crypto wallets, bank details, phone numbers, messenger accounts, links, gift cards, payment handles, and file and photo hashes. Each indicator is assigned a confidence score.
The collection method also affects provenance. In Active Defense, every message is stored the moment it arrives. If a scammer later uses a delete function in the chat, that does not remove the stored copy. For analysts, that supports source review and consistent reconstruction from message history.
The interaction itself is controlled. Replies are drafted by AI and approved by a human operator by default. That matters when evaluating how the underlying conversation data is produced and how much human oversight exists at the point of collection.
How analysts judge feed quality
For CTI teams, feed quality is less about volume than about whether records survive operational use. A feed has to import cleanly, stay consistent across reingestion, and give analysts enough detail to decide what to do next.
Useful checks usually include:
- clear typing of each indicator
- confidence scoring
- enough context for triage
- repeatable history
- relationship data for clustering
- exports that fit the CTI stack
One quality test is whether the feed preserves source history instead of only the latest transformed record. In Active Defense, every message is stored the moment it arrives. If a scammer later uses "delete for everyone," the stored copy remains. For analysts, that supports review of extraction results and consistent rebuilds from the original message stream.
Another test is whether the feed captures more than simple strings. Active Defense keeps photos, voice messages, and files by SHA-256, and photos also with a perceptual hash. That gives analysts more ways to match reused media across cases.
Scoring also matters when teams need different handling paths. In Active Defense, indicators extracted from every message include crypto wallets, URLs, e-mail addresses, phone numbers, messenger accounts, bank details, gift cards, payment handles, and file and photo hashes. Each gets a confidence score from a structural check and an LLM. That lets teams separate records better suited to direct action from those better used for enrichment, review, or clustering.
A final quality signal is whether the feed connects related activity. Here, chats that share an indicator, a reused photo, or a forwarded account are linked to one actor. That helps analysts pivot from a single record to an actor-level view inside their CTI workflow.
Formats, delivery, and integration into a CTI stack
For analysts, the question is not just whether a threat intelligence feed has useful indicators. It is whether the feed reaches the CTI stack in a form that can be ingested, mapped, and reprocessed without extra cleanup.
Common delivery needs are straightforward:
- a structured format for CTI platforms
- a flat format for testing, validation, or side workflows
- stable exports that can be rebuilt from source history
That is where delivery design matters. Active Defense provides each customer with their own feed, not a shared stream. Exports are available in STIX 2.1, TAXII 2.1, and CSV.
Customer-specific delivery helps when teams want their own ingestion rules, retention choices, or downstream mappings. It also makes it easier to align feed handling with internal casework and platform schemas.
Rebuildability matters as much as the export format. Because the feed can be rebuilt from stored history, teams can reingest older data after schema changes, pipeline updates, or platform migrations without depending on a one-time export.
For teams evaluating integration work, there are low-friction ways to test fit before a broader rollout:
- a free feed sample for qualified leads
- a 90-day pilot for up to 500 conversations at 1,900 EUR net, credited to the subscription afterwards
The Console and API run on Azure in Germany.
When a specialized feed is worth adding
A specialized threat intelligence feed is worth adding when your CTI platform already handles broad indicators well, but your team still lacks coverage for a specific abuse workflow.
One common gap is scam operations. Analysts may already ingest domain, malware, and abuse data, yet still miss the payment and identity layer scammers reveal during direct contact. That layer can help with case linking, fraud analysis, and actor tracking.
This is the kind of coverage Active Defense provides. It turns scam conversations on Telegram and by e-mail into a customer-specific feed. From those chats, it extracts and scores:
- crypto wallets
- bank details
- phone numbers
- messenger accounts
- links
- gift cards
- payment handles
- file and photo hashes
The feed is more useful when your team needs to answer questions such as:
- Which indicators belong to the same actor?
- Which payment routes recur across cases?
- Which accounts or media appear again under new pretexts?
Because chats that share an indicator, a reused photo, or a forwarded account are linked to one actor, the feed can support clustering work that simple indicator lists do not.
For evaluation, the practical path is small-scale first: request a free feed sample if you qualify, or test a 90-day pilot for up to 500 conversations at 1,900 EUR net, credited to the subscription afterwards.
Questions analysts ask about threat intelligence feeds
How fresh does a threat intelligence feed need to be?
Freshness depends on the use case. For blocking and rapid triage, teams usually want delivery as soon as possible after collection. For research, actor tracking, and retrospective correlation, slightly older data can still be useful if the history is complete and consistent.
What matters is knowing how the feed is produced and whether the underlying record can be reconstructed later.
Can analysts trace an indicator back to source evidence?
They usually need to. A feed is easier to trust when the source record is preserved and can support review of extraction and relationships.
In Active Defense, every message is stored the moment it arrives, so a scammer’s later “delete for everyone” does not remove the copy. Photos, voice messages, and files are also retained as artifacts for later analysis.
Is a shared feed enough, or do teams need their own feed?
That depends on workflow. A shared feed can help with broad visibility. A customer-specific feed is often better when a team wants its own ingestion rules, mappings, and reprocessing path inside the CTI platform.
Active Defense gives each customer their own feed with STIX 2.1 export.
What kind of analyst review exists in the collection process?
Collection method affects how teams interpret the output. In Active Defense, replies to scammers are drafted by AI and approved by a human operator by default. For analysts evaluating source quality, that means there is human review in the interaction loop rather than fully unattended outbound messaging.
A practical definition to keep
A threat intelligence feed is a structured, consumable stream of indicators plus context that helps analysts detect, enrich, correlate, and investigate malicious activity.
What makes a feed useful is not just how many records it contains. Analysts usually get more value from data they can work with in practice:
- clear context
- confidence scores
- relationships between records
- exports that fit existing CTI workflows
Those qualities make it easier to triage records, tune handling, and decide what belongs in blocking, enrichment, watchlists, or case work.
In this article’s example, Active Defense turns scam conversations on Telegram and by email into a customer-specific feed of scored indicators and linked actor data.
If your team is assessing scam-focused intelligence, a practical next step is to review sample data and compare pilot options against your current sources before deciding whether the feed adds useful coverage to your stack.