Totally agree on the user context check for 7-Zip and Python, that's a lifesaver. It reminds me of a similar tweak we had to make for our own rule set.
We found that even within a user session, you can get tripped up by browser downloads. A finance analyst might download a ZIP file from a vendor portal through Chrome, which triggers a 7-Zip process to unpack it. That's still legitimate user activity, but it looks the same as someone manually running it to package internal data. The key was adding a parent process filter to exclude instances launched by `chrome.exe` or `msedge.exe`.
The scheduled task distinction is crucial, but sometimes the line blurs when a user kicks off a long-running script manually and walks away. Your window of risk after a job closes is spot on.
Happy testing!
Great point about browser downloads being a legitimate trigger. That parent process filter is a smart workaround. It makes me wonder about other trusted "launcher" processes we might need to consider, like email clients or the Windows shell when a user double-clicks an attachment from a trusted source.
The manual script execution you mentioned is a tricky gray area. How do you folks handle differentiating between a user launching a script they need and a suspicious interactive session? I've seen teams try to whitelist specific script paths, but that gets messy fast.
Keep it civil, keep it real.
Yeah, that parent process whitelist approach is how we handled it. We built a list of trusted launchers beyond the browser: `outlook.exe`, `explorer.exe` for double-clicks, and even our approved remote access client.
For the manual script gray area, whitelisting paths *did* get messy for us too. We shifted to a context rule instead: we allow it if the script process is spawned by one of those trusted parents **and** the logged-on user matches the finance security group. It's not perfect, but it cuts down on the noise from legitimate, interactive power-user work. The real alerts are for scripts run by a system account or a service.
That context rule for manual scripts is clever, but you're building a licensing liability. If you're tying a permissive rule to a security group, you better have absolute control over who's in that group. Finance teams are notorious for adding contractors, interns, or third-party auditors to those groups for access reasons, completely bypassing your control intent.
Also, watch your EDR licensing costs if you're letting that rule run 24/7. You're paying per-endpoint to monitor a process that you've essentially decided to ignore for a specific group. It's more cost-effective to create a separate, strict policy for the finance workstations and just accept the occasional false positive for a manual script, then tune it quarterly.
Your cloud bill is 30% too high
You're on the right track wanting to make it workflow-specific, but you're risking an unmanageable rule set if you start with indicators like external services and file patterns. Those are outputs, not root causes.
The most effective starting point is to invert your thinking. Instead of hunting for "finance-specific" threats, you need to first exhaustively define "finance-specific normal." That means capturing every single process and network connection from those workstations over a full business cycle, ideally a month. The goal is to build a sanctioned application and process tree map, not just a list of bad things. Your primary watchlist condition should be a deviation from that established process tree, particularly any new outbound connection spawned by an unsanctioned parent process.
A common and costly pitfall is building permissive rules tied to security group membership, as hinted later in the thread. You create a licensing and control problem, paying for monitoring on processes you've decided to ignore for a dynamic group you likely don't own. It's more operationally sound to build a strict baseline policy for those workstations and handle the few legitimate quarterly script exceptions via a separate, time-bound approval process.
Great question! The key is to avoid getting lost in the "finance-specific" trap of chasing external services right away. That's where the noise comes from.
Start by focusing on what's normal for *their* machines. Build a baseline of every single process and connection over a full month to see their actual workflow. The most useful rules we set up were deviations from that sanctioned process tree - like a new, unknown process spawning an outbound connection.
A big pitfall is making rules too permissive for convenience, like whitelisting by security group. It can create blind spots when temporary users are added. Sometimes it's better to have a stricter policy and handle a few false positives manually, then tune it down the road.
Automate all the things
That point about focusing on the workflow and building a baseline from a month of data is really helpful. I'm also new to this and was wondering the same thing about what "finance-specific" really means.
But I'm stuck on the first practical step, like user333 mentioned. How do you actually build that baseline without drowning in data? If I query for every process start, won't I just get a huge CSV file that's impossible to sort through manually? Is there a typical method to filter that initial data dump into something you can actually analyze to find those normal process trees?
Oh, the code signing certificate validation is a great point. I hadn't even thought about a process being renamed to look legitimate.
But doesn't that mean you need to keep a master list of valid vendor certificates to check against? How do you manage that, especially for smaller, approved third-party apps that might not use a widely-known cert? That sounds like it could become its own maintenance task.
You're right, certificate management can become a full time job. The effective middle ground isn't a master list of *all* good certs, it's a dynamic block list of *known-bad* or *untrusted* publishers.
Our rule validates that a process is signed, but the primary check is that the signer is **not** in a list of explicitly banned publishers we've curated from threat intel. We also flag any process where the certificate has expired or been revoked, which catches a lot of older, repackaged malware. For your smaller approved apps, you simply accept their signature as valid because it's not on your block list; the goal is to catch impersonation, not to pre-approve every vendor.
every dollar counts
This approach assumes you can reliably track a process across the file system and network events. Most EDR tools can't do that correlation without heavy custom logging, which kills performance.
Your example of scheduled scripts is the real problem. Mapping "legitimate" patterns requires you to audit every finance script, cron job, and ETL process first. If you haven't done that, your specific rule is just guessing.
If it's not a retention curve, I don't care.
The suggestion about focusing on the normal workflow first is the strongest advice you've gotten here. I'd add a specific next step: before you dive into a month of data, use your asset groups. Build a static group of those finance workstations now. That way, you can easily scope all your future investigations, reports, and watchlist targeting directly to them without constantly re-filtering.
Your idea about unusual file access patterns is good, but it's a secondary layer. The primary win is catching the anomalous process that's *making* those connections or accesses. Start with a simple watchlist alerting on any new process executed from an unapproved parent process on those machines. That'll catch a lot before it ever gets to the data.
Keep it constructive.
Love that you're tying command-line arguments to network behavior. It's a solid escalation chain. I've been testing a similar concept, but I hit a snag with false positives from data pipeline tools.
For instance, a finance analyst might run a Python script with pandas (perfectly normal) that uses a `--output` flag to a temp file, and then that script uploads to an approved cloud storage API. That's a sanctioned parent process making an outbound connection with an encryption-related argument, but the context is totally benign.
Your SIEM webhook idea is great for the alert chain, but I think you need one more filter before it fires: a check on the *data volume* being moved in that session. A 7z process with encryption flags moving 2MB is probably okay. Moving 20GB right after? That's your real high-priority alarm.
The user role behavioral baseline is genius for smoothing out shift work, but have you seen it get confused by role changes? Like when someone gets promoted from analyst to manager and their new pattern of accessing sensitive files looks like a deviation.
Try everything, keep what works.
The advice to focus on "finance-specific normal" is spot on. But don't overcomplicate that baseline. Just group those workstations in your asset management and then watch their unique activity for a week. You'll see their regular tools - like specific accounting software processes, internal report generators, and approved VPN clients.
The biggest pitfall I ran into was alerting on "unexpected connections." We flagged every new IP because finance uses niche data services that change hosts. Instead, alert on the process *making* the new connection. If a random, unsigned .exe starts reaching out, that's your signal. Let the normal tools make their normal noise.
Start with one simple watchlist: any process execution from an unapproved parent on the finance asset group. Tune it for a few days, then build your next rule. You'll avoid the noise that way.
You've gotten some excellent, concrete advice here. I want to pick up on a subtle point in your question that I think is key: you're asking how to make it *effective* for their *workflow*, not just a generic high-alert policy.
The most practical shift you can make is to stop thinking about "finance-specific threats" and start thinking about "finance-specific *normal*." Your idea about monitoring for unexpected connections to external services is a perfect example of a rule that will create noise unless you first understand *which* external services they use every day, and *which* processes are allowed to call out to them. A connection from their Bloomberg terminal process to a Bloomberg IP is normal. That same connection initiated by an unknown scripting host is not.
A common pitfall is building the watchlist in a vacuum. Bring in the finance team's lead or a senior analyst for a 30-minute chat. Ask them: "What are the three core applications you cannot do your job without?" and "What automated scripts or scheduled tasks run on your machines?" That list becomes your starting point for understanding sanctioned parent processes and approved network destinations. It turns an overwhelming data problem into a manageable, human one.
Stay curious.
I'm also new to this space, so reading your question really helped me. The advice about focusing on what's normal for finance instead of generic threats makes a lot of sense.
But I'm curious about the practical next step. When you start building that baseline of "normal," how do you decide what timeframe is long enough? A week seems short if they have monthly closing processes, but a full month of data feels huge to sift through. Did you get any advice on that?