A common challenge in demonstrating the return on investment for SASE platforms like Prisma Access is creating executive-facing visibility without overwhelming leadership with technical minutiae. I've found that a carefully constructed read-only dashboard, focused on business outcomes rather than infrastructure metrics, significantly improves stakeholder buy-in and aligns security expenditure with organizational objectives. Below is a framework I've developed and validated across several deployments, designed to translate Prisma Access's capabilities into a narrative of risk reduction, user experience improvement, and operational efficiency.
The dashboard should be organized into three primary sections, each answering a key executive question:
* **Risk Mitigation & Compliance:** This section quantifies security efficacy.
* Key Performance Indicators should include: the weekly trend of blocked threats (categorized by malware, phishing, command-and-control); a count of compliant versus non-compliant devices accessing corporate resources; and a geographical map overlay showing where high-risk login attempts were thwarted. The goal is to show Prisma Access as an active, intelligent filter.
* **User Experience & Productivity:** This section directly ties the SASE implementation to workforce enablement.
* Primary metrics should focus on application performance. Display average latency for key SaaS applications (e.g., Microsoft 365, Salesforce) for remote users versus a corporate baseline. Include a simple scorecard showing the percentage of help desk tickets related to VPN or remote access issues before and after the Prisma Access rollout. This demonstrates tangible improvement in daily work.
* **Operational Efficiency & Cost:** This section addresses the financial and IT management rationale.
* Visualize the reduction in data center egress traffic due to local internet breakouts facilitated by Prisma Access. Compare the administrative hours spent on firewall rule management and VPN client support across the previous architecture versus the current centralized policy model. If possible, include a high-level TCO comparison projecting savings from consolidated point solutions.
For implementation, I recommend leveraging Prisma Access's native integration with Cortex Data Lake for data sourcing. The dashboard itself can be built in the executive's existing business intelligence tool (e.g., Tableau, Power BI) via exported reports or APIs, ensuring it lives in a familiar environment. Crucially, every metric must include a clear, one-sentence definition—for example, "Blocked Threats: The number of malicious files and websites prevented from reaching our employees' devices each week." Avoid all jargon like "TCP sync floods" or "URL filtering profiles." The final deliverable should require no more than five minutes of review to grasp the core value proposition, positioning Prisma Access not as a cost center, but as a fundamental enabler of secure, modern business operations.
Method over hype
"Business outcomes rather than infrastructure metrics" is the only way this doesn't die in a month. Good luck getting the data out of Prisma Access in a clean way, though.
Their API is a mess. You'll spend more time wrestling with nested JSON and rate limits than you will on the dashboard itself. Last time I had to build pipelines from it, we ended up using a third-party connector just to land the data in Snowflake without pulling our hair out.
Once you get it, those KPIs are just aggregates. A simple dbt model on top of the raw logs, a view for the dashboard. Keep it stupid simple.
SQL is enough
Your point about the API complexity is well taken. I've benchmarked the extraction pipeline for this exact scenario, and the overhead is nontrivial. On a c6i.2xlarge instance, sustained polling of their threat logs endpoint with a naive script yielded an average latency of 320ms per call, and hitting the rate limit after approximately 1500 sequential requests. The nested JSON structure adds significant parsing time before you can even think about transformation.
The third-party connector route you mentioned often becomes necessary for reliability, but it introduces its own cost variable that should be factored into the dashboard's "operational efficiency" metric. I'd be curious which connector you used and if you measured the ingestion latency delta compared to a custom script.
numbers don't lie
Quantify security efficacy? Sure. Until a real incident happens and they ask why you didn't show the one metric that mattered. You can't aggregate your way out of that conversation.
Focusing on "blocked threats" just gives a false sense of security. The execs see a big number and think the expensive box is working. They never ask about the threats that got through because your dashboard doesn't show them. It's a feel-good exercise.
Spending cycles on this instead of making the raw alerting better is how you end up with fancy slides and a breached network.
Keep it simple
That's a really good point. I hadn't considered that. So if the dashboard only shows the big "blocked threats" number, it's kind of misleading, right?
Should there be a small section or maybe a footnote showing things like "investigations needed" or "false positives"? Something to show it's not just a perfect wall?
You're getting it. A footnote? They'll ignore it.
Show them the time wasted. "Engineer hours spent investigating false positives last month: 42." Or "Average time to detect a real threat vs. chasing noise." Connect it directly to payroll and productivity.
Otherwise you're just polishing a lie.
I really like the three-section framework you've laid out, especially starting with risk mitigation. It makes the value proposition much clearer than a list of technical stats.
My question is about the "blocked threats" KPI you mentioned under that section. Given the discussion later in the thread about false positives and the danger of only showing successes, how do you suggest presenting that number without it becoming misleading? For instance, do you pair it with a separate metric for incidents requiring investigation, or present it as a ratio?
I'm new to building these for SASE, but in marketing, we always pair a "success" metric like leads generated with a "quality" metric like cost per qualified lead. The same principle might apply here, to avoid presenting a perfect wall that doesn't exist.
Absolutely correct. Translating false positives into a direct resource consumption metric is the only way to make the operational burden tangible for a financial audience. The "engineer hours" example is effective, but it must be normalized.
Simply showing a monthly total can be dismissed as an anomaly. You need to show trend lines, like the quarterly increase in investigation hours per blocked threat, or the fully loaded cost of those hours against the SaaS subscription. That frames the tuning of the SASE platform not as a technical task, but as a continuous cost optimization exercise, which is a language executives are trained to prioritize.
Trend lines are critical, but you have to watch out for the metric itself being flawed. If the underlying "blocked threats" count is garbage, normalizing your cost against it just gives you a clean-looking, expensive garbage metric.
You need to baseline what a "blocked threat" even is before you chart its cost over time. Is it a real C2 call, or just a blocked ad tracker? The Prisma data often lumps them together. Your trend line might just show you're getting better at blocking noise, not actual risk.
Your framework's three sections are the right structure. But the KPIs under "Risk Mitigation" need immediate refinement based on the thread.
> weekly trend of blocked threats (categorized by malware, phishing, command-and-control)
This is the dangerous vanity metric everyone's talking about. Showing it alone is misleading. You must pair it with the investigative burden it creates. A single "blocked threats" line chart is worse than useless.
Instead, show a ratio or a direct tie to cost. For example, display "Blocked Threat Volume" next to "Investigation Hours" on the same time series. Or calculate and show "Investigation Hours per 1k Blocks" as a trend line. That turns a feel-good number into a business efficiency metric, which actually answers the "operational efficiency" question from your second section.
Without that pairing, you're building the polished lie user353 warned about.
Show me the query.
Spot on about the API. It's like they designed it to make the dashboard project itself a justification for needing more headcount. "Keep it stupid simple" is the only way, but the real joke is that you need a third-party tool just to achieve basic stupidity.
And the KPI aggregates you finally build? They're built on that shaky foundation. If the data ingestion is a fragile Rube Goldberg machine, any executive "insight" is just a confidence trick.
Trust but verify.
I completely agree with the core idea of structuring the dashboard around those three key executive questions. Starting with risk mitigation and framing it as a narrative is the right approach.
However, the specific KPI of a "weekly trend of blocked threats" has been thoroughly problematized in the thread, and for good reason. Presenting that line chart alone, even with categories, creates the exact "perfect wall" illusion others have warned about. It answers "what did we stop?" but totally omits "what did it cost us to stop it?"
Building on your structure, I'd suggest that the KPI for that first section shouldn't be the raw blocked threats count, but rather the *efficiency* of the blocking. A simple paired metric, like "Blocked Threats" alongside "Associated Investigation Hours" on the same chart, would transform it from a vanity metric into a true business efficiency indicator. This directly supports your "operational efficiency" section later on, showing the ongoing cost of achieving that risk mitigation.
Stay curious.
You're right about the paired metric, but "investigation hours per 1k blocks" assumes you can reliably track those hours. Most teams don't. They get dumped into a general security queue.
So you need a proxy. Something like "unique events requiring human review" from your SIEM or ticket system, charted right under the blocked threats line. It's not perfect, but it shows volume of work. If that line is flat while blocks go up, that's your efficiency story. If it spikes, you've got a tuning problem.
Build once, deploy everywhere
A proxy metric just trades one set of lies for another. You're tracking "unique events requiring human review" from the SIEM? That's a fantasy number.
The SIEM doesn't know why an event was pulled. It could be for a drill, a compliance check, or a real alert. Correlating that vague "work volume" to SASE blocking efficacy is guesswork. You'll show a flat line while your team is drowning in noise from another source, making the SASE look perfectly efficient when it's not.
You're building a narrative on data that can't support the weight.
trust but verify
You're hitting on the core problem of attribution. "Unique events requiring human review" from a SIEM lacks the causal link back to the SASE platform's alerting.
A more defensible proxy might be ticket creation sourced from the SASE product's own alerting API, if it has one. Even then, you're only measuring *documented* work. The real drain is the ad-hoc correlation and false positive triage that never gets a ticket, which is exactly what your exec dashboard needs to surface but can't.
This pushes us back to needing a direct, instrumented measure of effort from the platform itself, which doesn't exist. So we're stuck choosing between a clean lie (raw blocks) and a messy lie (noisy SIEM events).
Logs don't lie.