Threat Intelligence Dashboard Project

What a threat intelligence dashboard is

A threat intelligence dashboard ingests feeds of indicators — malicious addresses, domains, file hashes, and vulnerability reports — alongside analyst-written reports about attacker groups and campaigns. Raw feeds alone overwhelm teams: thousands of indicators arrive daily, most irrelevant to any single organisation. The dashboard's job is relevance filtering: matching indicators against the organisation's own assets, software versions, and industry, and promoting only the matches and the campaigns that target similar organisations.

The second job is action. A relevant indicator should become a block, a detection rule, or a hunt task with one step, and the dashboard should show whether that action happened and whether the indicator was later seen internally. Over time, the dashboard also tracks which sources produce useful warnings and which produce noise, so subscriptions and feeds can be pruned on evidence.

Core features

Intelligence only helps when it is filtered, contextualised, and turned into work.

• Feed ingestion and deduplication: Ingest commercial, open-source, and community feeds in standard formats, deduplicate overlapping indicators, and track each indicator's first-seen and expiry dates.
• Relevance matching: Match indicators and vulnerability reports against asset inventory and software versions, promoting threats that touch systems you actually run.
• Campaign and actor profiles: Summarise attacker groups, their techniques, and their target industries, linked to the indicators and reports that evidence each profile.
• Action workflow: Turn a relevant indicator into a blocklist update, detection rule, or hunt task, and track completion so intelligence does not sit unread.
• Internal sighting search: Search historical logs for past sightings of an indicator, so a new warning can reveal whether the threat already touched the estate.
• Source quality reporting: Report per-source precision — how often its indicators proved relevant or led to action — to guide renewal and pruning decisions.

How it is built

Start with relevance, not volume. A dashboard showing ten threats that matter beats a feed of ten thousand indicators nobody reads.

1. Select initial sources: Choose a small set of reputable feeds and reports covering your industry and technology stack, with clear criteria for adding or dropping sources.
2. Build ingestion and storage: Implement feed parsers, indicator normalisation, deduplication, and expiry handling, with storage that supports fast lookup by address, domain, and hash.
3. Add relevance matching: Connect asset and software inventory, implement matching rules, and validate matches with analysts before they drive automated blocking.
4. Build action workflows: Add blocklist, detection, and hunt task creation, with approval steps for automated blocking to avoid disrupting legitimate traffic.
5. Measure and prune: Review source quality and analyst outcomes monthly, drop noisy sources, and add coverage where relevant threats were missed.

Technology considerations

Indicator formats and feed reliability vary widely. The platform needs tolerant parsers, handling for malformed feeds, and fallback behaviour when a feed goes silent — silence should alert, not look like safety. Indicator expiry matters: blocking lists built from stale indicators grow until they block legitimate services. Storage should support time-range queries, since the question is often whether an indicator appeared in logs months ago.

Automation boundaries need care. Blocking based on a single low-confidence feed risks outages, so high-confidence, corroborated indicators should drive automation while weaker ones go to analyst review. Sharing intelligence externally, if the organisation participates in sharing communities, requires sanitisation so internal details never leak into shared indicators.

Challenges to plan for

• Feed overload: Unfiltered feeds bury analysts. Relevance matching and source pruning are not optional extras; they are the product.
• Stale indicators: Old indicators cause false blocks and wasted hunts. Enforce expiry and confidence decay, and revalidate before reuse.
• Proving value: Intelligence value is hard to show when the benefit is an attack that did not happen. Track actions taken and incidents prevented or detected early.

Who uses it and what it typically costs

Cost depends on feed subscriptions, matching complexity, and automation depth. A dashboard over open feeds with manual action workflows is a contained build, while a platform with commercial feeds, automated blocking, and hunt integration is larger and carries ongoing subscription costs. Ask for a quote that separates build cost from feed subscriptions, since the latter recur annually.

First release scope

A sensible first release ingests three reputable feeds, matches indicators against a real asset inventory, and turns matches into block or hunt tasks, before adding commercial feeds or automated blocking. Launch criteria should include a month of source-quality data showing which feeds produced action, a confirmed internal sighting search for a past incident, and analyst feedback that relevant threats are surfacing without noise. Resist ingesting every available feed at launch; volume without relevance is the fastest way to make analysts ignore the dashboard entirely.

Frequently asked questions