Automate Your Weekly Threat Intel Briefing With AI

Every Monday morning, someone on your security team spends half the day producing a threat intelligence briefing that most of your leadership skims in under five minutes. I know because I used to be that someone. Four hours of reading advisories, cross-referencing CVEs, summarizing vendor blog posts, and formatting a document that got a "thanks" reply-all and was never referenced again until the next Monday.

Six months ago, I rebuilt this process with AI at the center. The briefing now takes me 30 minutes. And paradoxically, the quality improved — because I spend my time on analysis instead of summarization.

The Old Process (What I'm Replacing)

Monday 7:00 AM: Open 15 browser tabs — CISA, vendor blogs, CVE feeds, Twitter/Mastodon, internal ticket queue
8:00 AM: Read everything, take notes
9:00 AM: Draft the briefing — executive summary, notable threats, CVEs relevant to our stack, recommended actions
10:30 AM: Format, proofread, add links
11:00 AM: Send to distribution list

That's four hours, minimum. On a heavy week (looking at you, every Patch Tuesday), it's closer to five.

The New Process: Sources

First, rationalize your sources. You don't need fifteen. You need the right five or six that cover your threat landscape without excessive overlap. Here's my list:

CISA Known Exploited Vulnerabilities (KEV) catalog: The single most actionable source. If it's on the KEV, it's being exploited in the wild and you need to know about it. RSS feed available.
NIST NVD feed: Filtered by CVSS 7.0+ and your technology keywords. Don't try to read the full NVD — it's thousands of entries a week. Filter aggressively.
Two vendor threat blogs: Choose vendors whose visibility aligns with your environment. If you're a Microsoft shop, Microsoft Threat Intelligence Blog is non-negotiable. Add one more that covers your primary threat landscape (CrowdStrike, Mandiant, Talos, etc.).
One industry ISAC feed: Your sector's Information Sharing and Analysis Center. Healthcare, financial services, energy — they all have one, and the alerts are pre-filtered for relevance.
Internal ticket queue: Last week's incidents and notable tickets. This grounds the briefing in what's actually happening in your environment, not just what's happening globally.

Step 1: Automated Collection (15 minutes of setup, then zero ongoing effort)

Set up RSS feeds or API pulls for your external sources. I use a simple Python script that runs Sunday night via cron, pulls the week's entries from each source, and dumps them into a single text file. Nothing fancy — the goal is a consolidated input file that I can feed to AI Monday morning.

If you don't want to write a script, a free RSS reader like Feedly can do the aggregation. Export as text or just copy-paste from the reader. The key is having all your sources in one place before you start the AI step.

Step 2: AI Summarization and Prioritization (10 minutes)

This is where the magic happens. Take your consolidated source data and feed it to Claude or ChatGPT with a structured prompt. Here's the prompt I use:

You are a senior threat intelligence analyst preparing a weekly briefing for security leadership at a mid-size [industry] company. Our technology stack includes [list key technologies: Windows/Linux, cloud provider, major applications].

Below is this week's aggregated threat intelligence from our sources. Please produce a briefing with the following sections:

1. EXECUTIVE SUMMARY (3-4 sentences, biggest takeaways only)
2. CRITICAL THREATS (anything actively exploited or targeting our industry/technology stack — include CVE IDs, affected products, and recommended actions)
3. NOTABLE VULNERABILITIES (CVSS 7.0+, relevant to our stack, not yet known-exploited — prioritized by relevance)
4. INDUSTRY TRENDS (2-3 sentences on emerging patterns or campaigns)
5. RECOMMENDED ACTIONS (specific, actionable items for this week — patch X, review Y, monitor Z)

Keep the tone professional but direct. Leadership reads this in under 5 minutes. Every sentence should earn its place.

[paste consolidated source data here]

The output from this prompt is consistently 80-90% ready to send. The AI excels at synthesizing multiple sources, identifying overlapping reports about the same threat, and structuring information hierarchically. What used to take me two hours of reading and note-taking now takes under a minute of AI processing.

Step 3: Human Analysis Layer (15 minutes)

This is the step you cannot skip. Read the AI output and add three things:

Environmental Relevance

The AI knows your technology stack from the prompt, but it doesn't know that you just migrated your payment processing to a new platform last week, or that your Azure AD conditional access policies are misconfigured and you're waiting on a change window. Add context notes: "Relevant: we run [affected version]" or "Low priority: we deprecated this system in Q3."

Internal Correlation

Check the briefing against your internal ticket queue. Did any of this week's global threats match something you saw internally? That correlation — "CISA flagged CVE-2026-XXXX this week, and we observed scanning activity targeting this service on Thursday" — is the most valuable insight in the briefing, and AI can't generate it without access to your internal data.

Judgment Calls

AI will list vulnerabilities by CVSS score. But sometimes a CVSS 7.5 matters more to your organization than a CVSS 9.0, because of your specific exposure. Reorder priorities based on your judgment. Add "Why this matters to us" notes. Remove items that are technically relevant but practically irrelevant to your environment.

Step 4: Format and Send (5 minutes)

I use a consistent template — same header, same sections, same formatting every week. Leadership knows where to look for what they need. The template hasn't changed in six months. Consistency builds trust.

My distribution is email for leadership, Slack post for the security team, and a pinned wiki entry for the archive. Same content, three channels, each formatted slightly differently. AI can help with the reformatting too:

Take the following briefing and produce three versions: 1) A formatted email with HTML styling, 2) A condensed Slack message with emoji headers and bullet points, 3) A wiki-formatted version with a table of contents.

What Changed (Besides the Time)

The counterintuitive result: the briefing got better when I spent less time on it. Here's why. When I spent four hours, I was so deep in the weeds that the executive summary was too detailed and the recommended actions were too vague — I'd run out of energy by the time I got to the actionable part.

Now, the AI handles the summarization grunt work, and I spend my 15 minutes of human analysis on the part that actually matters: connecting global intelligence to local reality. The recommended actions are more specific. The executive summary is tighter. And leadership actually responds to it, because it reads like analysis rather than a news digest.

Total Weekly Time Investment

One-time setup: 2-3 hours (source selection, RSS configuration, prompt template)
Weekly process: 30 minutes (collection is automated, AI summarization is 10 min, human review is 15 min, formatting is 5 min)
Time saved per week: 3.5 hours
Quality improvement: Measurable — leadership engagement with the briefing increased (more reply questions, more action items completed)

Thirty minutes on a Monday morning, most of it reviewing what the automation already prepared. That's the whole thing. No ML pipeline, no data lake, no six-month roadmap. Just RSS, an API call, and a human who still reads the output before it goes to leadership.