Security Signal Intelligence
Which security event this week is worth a conversation, and for which accounts?
- Who uses it
- A rep preparing an account call, or a field marketer who cannot monitor every advisory alone.
- Decision it enables
- Whether to open a conversation, and what we are allowed to say.
- Working today
- Nothing in this repository. Rapid7 playbooks and Darktrace industry training were field work. They are not this product.
Workflow and next steps
- Inputs → review → output
- A fixed list of public sources → a person reviews the item → a note that says whether a rep should use it. That path is not built.
- Planned next
- A reviewed weekly note. I have not turned the source list into software.
- Technologies in this repo or a linked project
- None. This page is the note.
What a team actually used
Rapid7 threat-intelligence playbooks and Darktrace industry training.
Proposed software, separate from that asset
Not designed as a product yet.
Where it stands
Concept. The playbooks and the training were real. I have not completed a design for this product, and I have not built it.
My role
At Rapid7 I was a threat-intelligence resource for enterprise accounts, partners, and customers, and I wrote playbooks the portfolio team used. At Darktrace I built industry-knowledge training. A shared signal product is still an idea.
Metric to test
No product metric. I am not counting the playbooks or the training as usage of this idea.
Links
No public demo or repository beyond this page.
How it works
The problem I kept seeing
Relevant security news, breaches, and market events are difficult for GTM teams to monitor and interpret consistently.
Timely conversations need a shared, reviewed digest — not 20 people refreshing the same feeds.
Without a repeatable answer: Inconsistent awareness. Missed campaign windows.
What I was working from
Threat-intelligence playbooks at Rapid7 and industry training at Darktrace existed because the field could not monitor the market consistently on its own.
Workflow
Sources → Collection → Normalization → Deduplication → Classification → Relevance scoring → Review → Digest / alerts
In plain language
Sources → Normalize → Classify → Review → Digest
Described stack — not what this repository runs
Names below are a sketch. They are not completed integrations unless the technology section says Built with.
Source collection · Normalization · Classification · Review queue · Digest delivery
Source · Event · Account relevance · Review · Digest
Next. Not started as software.
Goals
- Collect, deduplicate, classify, score relevance, and review before a digest goes out.
Out of scope
- A news product.
- Unreviewed alerts to customers.
Smallest version
A reviewed weekly digest with account/persona tags.
Question that version tests: Does a shared, reviewed signal feed change conversation quality?
Metrics I would watch
These are not results unless the section above says they are on record.
- Faster market awareness
- Digest usage
- Campaigns launched from signals
- Hypothesis: stronger customer conversations and timely campaigns
If market events are reviewed and tagged, GTM should respond faster and more consistently. Next — not built. Human review stays in the loop.
Tradeoffs
- Reviewed digest over real-time unfiltered alerts.
- Deterministic source list over open-ended crawling.
What I would build next
- Source list
- Review queue
- Account tagging
Explore related work
- Designed
Account Intelligence
Watch assigned accounts, separate a business change from a hypothesis, and draft outreach a person still has to send.
- Professional field system
Competitive Intelligence Engine
Turn competitor research into a source-backed brief and field guidance. Monitoring and automatic updates are designed, not running.