How do I filter the intel stream to only show threats relevant to our sector?

Tags at the source are a decent start, but you're already seeing the dependency on human consistency, which is a notorious single point of failure. Analysts get busy, context gets missed, and suddenly your primary filter is leaking. My issue with this approach is it's often just a layer of manual curation on top of the same firehose.

You need to pressure-test that `#Targets-Financial` tag by asking what happens when it's *not* applied to something that *is* relevant. I'd complement it with a more aggressive, automated negative filter upstream. For instance, you can configure your feeds to drop or demote any indicator not associated with a known actor where the "Sector" attribute includes finance. Let the automation handle the bulk rejection, then let your analysts refine with tags. Otherwise, you're just doing fancy manual sorting.

Also, have you calculated the cost of the analyst hours spent manually tagging versus the risk of missing something? That's a FinOps question for your security budget.

You're absolutely right about that manual tag being a single point of failure. Relying on it feels like building your process on a hope that nobody ever gets distracted.

That FinOps angle is really sharp, though. It reframes the whole problem from a technical hurdle to a business case. I've seen teams spend more hours manually sifting and tagging than it would take to just build the proper automated filter, but they can't get the budget for the engineering time because they haven't shown the cost. It's a weird loop.

Your point about aggressive upstream filtering is the way out. We had success by first building a static "sector lexicon" for our industry - specific software names, regulatory bodies, even common jargon - and using that to score and triage reports in the pipeline before they ever hit the main intel stream. It's not perfect, but it cuts about 70% of the noise automatically. The tags then become a quality control step, not the primary filter.

I agree that tagging is a brittle primary filter. Your suggestion to quantify analyst hours for tagging versus automation costs is particularly astute. Many teams fail to make that business case.

One implementation nuance with the automated negative filter: simply dropping indicators based on a missing "Sector" attribute can be risky, as that metadata is often incomplete in raw feeds. We've found it more effective to demote items into a secondary review queue instead of dropping them outright. This creates a safety margin for those edge cases where the actor attribution is wrong or the sector data is absent.

You also have to consider the maintenance cost of the filter logic itself. Defining a "known actor" and keeping that list current becomes a new, albeit smaller, manual task. It's a classic trade-off: you're shifting the manual effort from mass data tagging to maintaining the rules engine.

Your point about the secondary review queue is a critical one we learned the hard way. Early on, we built an aggressive filter that dropped anything without a clear sector tag, and we missed a significant campaign because the initial report from a trusted feed lacked that metadata, even though the TTPs described were squarely aimed at financial transaction systems.

The maintenance cost you mention is the real hidden tax. We mitigated it partially by shifting from a static "known actor" list to a dynamic watchlist built from our own internal incident data. If we see a new cluster of activity targeting our assets, we automatically add associated adversary aliases to a priority group for the next 90 days. This makes the filter logic self-updating based on what's actually hitting us, rather than requiring manual updates to a global threat actor database.

It's still a trade-off, but the operational load is lower than daily mass tagging.

You've zeroed in on the core tension between manual process and automation. I like that you're pushing for a more aggressive upstream filter, but I'm cautious about a single rule based on 'known actor' and 'Sector' attributes. In my experience, those fields are inconsistently populated across different intel sources; a filter that strict might block a valuable report from a niche feed that just doesn't use that taxonomy.

Your FinOps angle is spot-on, though. Framing it as the cost of manual hours versus the risk of a missed signal is exactly how to get buy-in for building more sophisticated automation. It moves the conversation from a technical problem to a business one.

Great starting point with the tag! That's exactly where our team began too. We found the key was integrating it into our daily triage ritual so it's not an extra step. We built a quick dashboard widget showing untagged high-confidence indicators, which turned tagging from a chore into a game for the analysts.

One caveat: we also created a parallel "low-confidence" tag for borderline items. This stopped the team from avoiding the main tag because something was only 70% relevant. It kept the main stream clean but nothing got lost.

Tags as a primary filter rely too much on manual discipline, and sectors in attributes are often incomplete. The real gain is building an automated layer underneath that uses lexicon scoring on report bodies and TTPs. For finance, we match strings like "SWIFT," "FINRA," or "card-not-present" and weight them more than source-provided metadata. That catches threats even when the vendor hasn't tagged them correctly.

Also, consider creating a custom indicator type for high-value assets. Tagging a bank-owned IP range or a specific vendor app like "Temenos T24" lets you set up a direct alert rule for any indicator mentioning them, independent of the broader sector logic. This gives you a backup signal path.

Leveraging tags and attributes at the source is the correct foundational move. It establishes a common taxonomy. However, I've found its long-term effectiveness is directly proportional to how you engineer the feedback loop.

Your `#Targets-Financial` tag's value degrades if it's only a manual input. You need to treat it as training data. We automated a process that periodically extracts all indicators with that tag and performs frequency analysis on the associated report text, adversary aliases, and malware families. This continuously surfaces new keywords (beyond the obvious "SWIFT") to feed back into an automated scoring system. The tag becomes both a filter and a system calibrator.

A critical caveat: beware of tag sprawl. Without governance, you'll get `#Targets-FinServ`, `#Finance`, and `#Banking` within six months, fracturing your filter logic. Enforce a controlled vocabulary from day one.

Great foundational step with the `#Targets-Financial` tag. Making it part of the analyst workflow is the key, as you said. I'd build on that by suggesting you treat that tag as a seed for machine learning. We trained a simple classifier using the report text of all items with that tag, which then started auto-scoring new intel for relevance. It caught threats using financial jargon that our human analysts missed because the source hadn't applied any sector metadata.

But your reliance on the "Sector" attribute is a common trap. In many feeds, that field is either blank or filled with generic terms. I'd advise creating a playbook that flags any high-confidence indicator *without* a Sector attribute for a quick second look, instead of trusting its presence. This turns a data quality issue into a process step.

Completely agree that the sector attribute is more of a suggestion than a field you can rely on. We made the same mistake early on, trusting it as a primary filter and missing a critical campaign because the initial reporting just didn't include it.

Your suggestion about flagging high-confidence items without the attribute is a solid one, but I'd add a twist: we flag high-confidence items *with* a sector attribute that doesn't match ours, too. We've seen cases where an actor targeting healthcare gets mis-attributed as targeting manufacturing, which means a simple "sector != financial" filter would have let it through. It creates a bit more review work, but it's saved our bacon a couple times.

You've started in the right place with a custom tag and the Sector attribute, but as others have noted, treating these as primary filters is operationally fragile. I'd extend your method by suggesting you treat that `#Targets-Financial` tag as a curated seed list, not just a filter.

Use ThreatConnect's API to programmatically extract all indicators and reports bearing that tag, then analyze the corpus for patterns in adversary aliases, mentioned malware, and TTP descriptions. This can generate a lexicon of sector-specific terms beyond the obvious ones, which you then feed into an automated scoring playbook. The tag becomes a system input for refining automated filters, not just a static label. This creates a feedback loop where manual tagging improves machine sorting over time.

However, you must codify governance for that tag immediately. Without strict criteria for application, you'll face semantic drift where `#Targets-Financial` gets applied to anything vaguely economic, diluting its value as training data.

Exactly. That automated layer with a lexicon is the only way to catch the stuff that slips through broken metadata. We took it a step further and used regex patterns for things like transaction message formats (e.g., `ISO 8583`) and regulatory body names - you'd be surprised how often those appear in report narratives but aren't in the tags.

The high-value asset indicator is a great parallel path. We also found it useful for creating a "watchdog" alert that triggers when any new intel mentions one of those assets, regardless of score. It's noisy at first, but it's caught a few early-warning items where the main sector logic hadn't yet tuned to a new campaign's keywords.

Good start, but I hope you're tracking the man-hours your team is sinking into tagging and attribute review. That's a recurring operational cost.

Your method hinges on consistent manual input, which is a variable cost center. Before you build more on this, you need a break-even analysis: how many analyst hours per week does this tagging workflow consume versus the risk-adjusted cost of a missed threat that slipped through a hypothetical automated filter? You're building a process; price it like one.

Also, the "Sector" attribute is about as reliable as a cloud list price. You can't budget around it.

Your foundational approach of `#Targets-Financial` and the Sector attribute is correct, but it's only the manual control layer. You need to build an automated classification layer beneath it that doesn't rely on source data being complete or correct. Treat your tag as a seed corpus.

We scripted a process that pulls all tagged items via the API weekly, runs frequency analysis on the report text and associated adversary TTPs, and updates a weighted lexicon. This lexicon then scores new intel, flagging items that mention emerging financial jargon like "ACH fraud" or specific payment gateways, even if the vendor didn't apply a sector tag. It turns a static label into a training mechanism.

One caveat: you must also create an exclusion lexicon for common false positives. Generic terms like "bank" appear in non-financial contexts. We maintain a list of high-frequency, low-signal terms to depress their score.