An ioc feed is the stream of indicators a CTI team imports into its platform to enrich cases, correlate activity, and decide what to act on. When analysts search for “ioc feed,” they usually need a practical answer: what data belongs in the feed, whether it is structured well enough to ingest cleanly, and whether it helps investigation instead of adding review work.
For scam investigations, useful indicators often come from live conversations. Active Defense runs AI decoy personas on Telegram and by e-mail, keeps scammers talking, and turns what they send into a customer-specific feed. The feed can include:
- crypto wallets
- bank details
- phone numbers
- messenger accounts
- links
- gift cards
- payment handles
- file and photo hashes
What an IOC Feed Is and Why CTI Teams Use One
An IOC feed is a continuously updated source of indicators of compromise or abuse that can be imported into a CTI platform, TIP, SIEM, SOAR workflow, or an internal enrichment pipeline. In plain terms, it is a way to get structured signals into the systems analysts already use, without rebuilding collection and parsing from scratch for every case.
CTI teams use feeds to cut manual collection work, standardize incoming indicators, and speed up triage and correlation across investigations. But an IOC feed only becomes operationally useful when the data arrives in a form analysts can filter, score, deduplicate, and pivot from inside their tooling. That is one reason feed design matters as much as feed volume, especially for teams evaluating what intelligence software needs.
A raw list of strings is not the same as a usable feed. A usable feed includes:
- structured indicator types
- confidence information
- enough context to decide whether to act
- a stable way to deliver and re-ingest the data
Different missions also care about different indicator families. Fraud and scam investigation often focuses on wallets, payment routes, contact points, and delivery infrastructure rather than only malware signatures. That changes what analysts want to ingest and how they prioritize it, as seen in feeds for scam indicators and in research on what scammers’ crypto wallets reveal.
No single feed covers every threat type or every collection gap. Most CTI teams combine multiple sources and judge each one by how well it answers operational questions in their own environment. For a broader checklist of fields and metadata to look for, see what should be included in a feed.
What CTI Teams Need in the Data They Ingest
At ingest, CTI teams usually care about a small set of data qualities first: consistent indicator types, normalized values, timestamps, source attribution, confidence, and enough metadata to support filtering and correlation. Those basics determine whether a feed can be routed cleanly into a TIP, SIEM, enrichment pipeline, or the wider stack described in CTI software needs.
Typed fields matter because platforms do not treat all indicators the same way. A BTC wallet, an e-mail address, an IBAN, a URL, and a file hash each follow different matching logic, different validation rules, and different enrichment paths. If they arrive as loosely labeled text, analysts lose precision at the point where automation should help.
From scam conversations, Active Defense extracts these indicator families:
- crypto wallets on BTC, ETH, TRON, and SOL
- URLs
- e-mail addresses
- phone numbers
- messenger accounts
- bank details including IBAN and SWIFT
- gift cards
- payment handles
- file and photo hashes
Each extracted indicator is scored with a confidence score derived from a structural check and an LLM. That matters operationally because not every string that looks like an account number, wallet, or handle should trigger the same downstream action. Scoring helps analysts decide what to automate immediately, what to queue for review, and what to hold for correlation with later evidence seen in live scam briefings or broader scam data reports.
Attachments and media also matter in this problem set. Scam infrastructure often shifts across accounts while reusing files or images, so a feed that ignores media leaves out a useful path for correlation. Photos are retained with both SHA-256 and a perceptual hash, which supports exact matching and similarity-based reuse detection.
Ingestion gets stronger when a feed contains actor-level linking as well as isolated indicators. Active Defense links chats that share an indicator, a reused photo, or a forwarded account to one actor. That gives analysts something more useful than a flat list: a way to pivot from single indicators toward an operator pattern, then carry that structure forward into export workflows such as STIX 2.1 for scam indicator feeds.
How to Judge Feed Quality Beyond Indicator Volume
It is easy to judge an IOC feed by how many indicators it emits. That is usually the wrong first test. Volume without relevance, traceability, or enough context can increase analyst workload instead of reducing it.
A better question is how the feed is collected. CTI teams should ask:
- where the indicators come from
- how early they are captured
- whether there is a clear chain back to source events
Those points matter because feed quality starts before extraction. If collection is weak, scoring and formatting later will not fix the underlying problem.
In Active Defense, every message the decoy receives is stored the moment it arrives. That means the received content is preserved even if a scammer later uses a messenger feature such as “delete for everyone.” For CTI work, that event-level retention matters because it preserves collection history, supports reconstruction, and lets teams revisit extraction decisions later if parsing rules, analyst judgments, or internal priorities change.
Feed quality also improves when the data can be reproduced from source history rather than treated as a one-time snapshot. Each customer gets their own feed, and that feed can be rebuilt from the stored history. In practice, that matters for auditability inside intelligence operations and for replay into downstream systems after schema changes or parsing updates.
Another quality signal is what happens at first contact. A provider should be able to separate uncertain conversations from engaged ones instead of forcing every inbound message into the same workflow. Here, each new chat is checked at first contact, and on messengers the chat is left alone when the system is unsure.
Operational restraint is also part of quality. Replies are drafted by AI and approved by a human operator by default before sending. That human review step helps keep collection tied to oversight rather than treating raw automation as sufficient on its own.
Formats, Exports, and Ingestion Workflow
CTI teams should confirm export formats early in an evaluation. Feed usefulness drops fast when analysts have to manually reshape data before every import, especially when the same indicators need to move through a TIP, SIEM, case workflow, and internal enrichment steps.
Each customer gets their own feed with STIX 2.1 export. That matters because structured exchange makes it easier to map indicators into existing schemas, keep confidence and related metadata attached, and move the same data across internal tools without rebuilding the record each time.
Format alone is not enough, though. Analysts should also look for reproducibility in the ingestion workflow. If internal mappings change, if a parser is updated, or if a team decides to normalize fields differently, the provider should be able to rebuild exports from source history rather than relying on one-off snapshots.
That is especially useful for reprocessing and backfills. Earlier in this article, the key quality point was that the feed is rebuildable from stored history. In practice, that gives CTI teams a cleaner way to replay data into downstream systems after schema changes or updated extraction logic.
Some teams also care where the service interface is hosted. The console and API run on Azure in Germany.
A per-customer feed can simplify operations in a few ways:
- clearer segregation between environments
- easier testing before broader import
- simpler downstream handling than one undifferentiated shared stream
For teams reviewing this area in more detail, the useful next question is not just whether a provider says “STIX,” but how that export fits a repeatable ingestion process and preserves the context analysts need.
Common Questions About an IOC Feed
How fresh should an IOC feed be for CTI work?
Freshness matters because analysts use an IOC feed to enrich active cases, match new reports, and spot reuse before an actor changes accounts or payment routes again. For scam investigations, delay reduces value.
A practical question is not only when indicators are exported, but when source events are preserved. If a message is stored when it arrives, later cleanup by the sender does not remove that source record from the collection history.
Can an IOC feed support more than blocking and alerting?
Yes. Many CTI teams use an IOC feed first for enrichment, clustering, and case correlation rather than direct enforcement.
That is especially true in scam work, where the useful signal is often a pattern across chats:
- a wallet reused across conversations
- the same photo attached by different accounts
- a forwarded account connecting separate chats
- payment details that reappear under new identities
This kind of use depends on preserving relationships, not just shipping isolated values.
What makes scam indicators different from malware-heavy feeds?
Scam-focused ingestion usually centers on payment and contact infrastructure. Analysts often need to process wallets, bank details, phone numbers, e-mail addresses, messenger accounts, gift cards, payment handles, URLs, and file or photo hashes.
That shifts the evaluation standard. The question is less about signature coverage and more about whether the feed captures the routes an operator uses to get paid, make contact, and reuse assets.
What should analysts check in a feed sample?
A feed sample should show whether the data fits real ingestion and triage work. Before a pilot, analysts should inspect:
- indicator typing
- normalization
- confidence scoring
- relationship fields
- timestamps
- source context
- export structure
Active Defense offers a free feed sample for qualified leads.
What does a pilot need to prove?
A pilot should answer operational questions: whether the feed imports cleanly, whether analysts can filter and pivot without extra cleanup, and whether the data is useful in live investigations.
Active Defense offers a 90-day pilot for up to 1,000 sessions at a fixed price. Each customer gets their own feed with STIX 2.1 export.
Choosing an IOC Feed That Analysts Will Actually Use
Adoption usually comes down to whether the feed fits analyst workflow without extra cleanup. Teams are more likely to keep using an IOC feed when it supports three routine jobs:
- import into the CTI platform in a structured format
- sort indicators by confidence
- pivot from a single value to related activity
For scam work, that means a feed should help analysts move from one wallet, handle, phone number, or image hash to the wider actor footprint behind it.
Active Defense is built for that use case. It turns scammer interactions on Telegram and by e-mail into a customer-specific feed, links related chats to one actor when signals match, and provides STIX 2.1 export. For evaluation, qualified leads can request a free feed sample or run a 90-day pilot for up to 1,000 sessions at a fixed price.