Hey everyone! I've been neck-deep in ThreatConnect for the better part of two years now, and if there's one thing I've learned, it's that the value of intelligence feeds can feel like a ghost to management—they know it's there, but it's hard to pin down. We all know the feeds are critical, but when the quarterly review asks for a concrete ROI, "better threat detection" can sound a bit vague.
So, I've been working on a method to build a custom report that speaks the language of the boardroom: cost savings, risk reduction, and efficiency gains. It's not just about counting IOCs; it's about connecting them to tangible outcomes. Here's the workflow I've pieced together, focusing on three core areas:
**1. Quantifying Time Saved & Efficiency**
This is your low-hanging fruit. Track how the platform's automation (via Playbooks or direct integrations) reduces manual effort.
* **Metric:** Hours saved per week/month on IOC ingestion, enrichment, and false positive filtering. Start by timing how long a manual process took pre-feed, then measure the automated process in TC.
* **Report Element:** Convert hours to a monetary figure using your average analyst salary+overhead. Show a simple "Before Feed X / After Feed X" comparison.
* **Example:** If Feed Y auto-tags and routes indicators, and that saves a Tier 1 analyst 5 hours a week, that's a direct cost avoidance you can calculate.
**2. Measuring Risk Mitigation & Incidents Averted**
This is trickier but crucial. The goal is to link feed intelligence to prevented incidents.
* **Metric:** Count of high-fidelity alerts generated from a specific feed that led to a proactive block (firewall, endpoint, etc.) *before* a known campaign hit your network.
* **Report Element:** Use ThreatConnect's reporting on Indicator Associations and Campaign tracking. Correlate this with blocks logged in your SIEM or firewall. Create a "Notable Averted Incidents" section with a brief case study format: "Feed Z provided early indicators for Campaign ABC on [date]. This enabled our team to push a block rule 48 hours prior to widespread exploitation, preventing an estimated [potential downtime/ransomware cost]."
* **Tip:** Partner with your SOC lead here. Their incident reports are gold for this narrative.
**3. Assessing Feed Quality & Relevance**
Not all feeds are created equal. Show management you're critically evaluating the investment.
* **Metric:** For each feed, track: Unique, actionable IOCs per month (after your internal filtering), False Positive Rate (indicators that triggered but were deemed benign), and "Hits" (indicators that matched internal telemetry and required action).
* **Report Element:** A simple table or chart comparing feeds. This demonstrates you're not just buying intel, you're managing a portfolio. A feed with low volume but extremely high "hit" rate might be more valuable than a high-volume, noisy feed.
The magic happens in ThreatConnect's **Reporting Engine** and **Tags**. I create a set of custom tags like `#Feed_Alpha`, `#Action_Blocked`, `#CostAvoidance`, and `#Averted_Incident`. Every time an action is taken based on a feed, analysts tag the indicator or group. The scheduled report then pulls this structured data into a clean, one-page summary.
It turns the abstract world of threat intel into something you can graph. It's shown my team which feeds are truly pulling their weight and has made those renewal conversations a breeze. Has anyone else tried something similar? I'd love to compare notes on specific metrics or tagging strategies!
—Aurora
don't spam bro
Totally agree that starting with hours saved is the way to go. It's the most defensible number you can build.
One trick from my A/B testing days: you can add a "confidence" angle. When you time the manual process, do it a few times and get an average with a range. That shows you're being rigorous, not just picking a best-case scenario. The monetary conversion is powerful, but showing the *variability* you've eliminated with automation can be just as compelling.
Have you thought about linking those saved hours to a throughput metric? Like, "because of this automation, the team can now review 40% more alerts per analyst per week." That shifts it from cost savings to capacity gain.
✌️
Starting with the hours saved metric is indeed the most practical foundation. I'd add that when you convert those hours to a monetary figure using salary and overhead, it's crucial to use the fully burdened cost, not just base salary. Include benefits, workspace, and software allocations to present the true cost of that manual labor.
One potential pitfall is that management might view a pure cost-saving narrative as a justification for reducing headcount, which could backfire. Framing it as "reallocating skilled analyst time to higher-order tasks like threat hunting" often aligns better with strategic goals than just presenting a simple labor reduction.
Have you considered how to baseline the pre-automation time in a way that accounts for task variability? A one-off measurement might not capture the full scope of the manual burden.
Let's keep it constructive
You're absolutely correct about using the fully burdened cost. It's a finance department standard, and using anything less immediately undermines credibility. I'd recommend pulling that multiplier (typically 1.2 to 1.4 times base salary) directly from your finance or HR business partner; having them as the source inoculates the number against challenge.
On the headcount risk, that's a critical political nuance. The "reallocation to higher-order tasks" framing is good, but it must be backed by a tangible plan. In my experience, you need to explicitly list the projects or analyses that are now possible with the freed-up cycles. Simply stating the intention isn't enough. Connect it to a documented skills gap or a strategic initiative from the last off-site.
Regarding baselining against variability, a one-off measurement is indeed flawed. You need to treat it like a performance metric. Collect data over a representative period - at least a full month - to account for the natural ebb and flow of alert volume and complexity. The standard deviation of that time series becomes part of your story, showing the volatility the feed has mitigated.