Just got tired of manually copying SIEM alerts into AuditBoard for review. So I built a quick connector using their API. It pulls high-severity alerts from our Sentinel instance overnight and creates them as potential issues in the right workflow.
It's pretty basic right now, just maps alert title/description and sets a 'pending review' status. But it already cut out a ton of busywork. Curious if anyone else has automated evidence or issue ingestion. What are you using as a source?
Automating that ingestion is a critical step. Sentinel is a solid source for operational security events. I'd suggest you also consider mapping the alert's MITRE ATT&CK tactic as a custom field during creation. It saves the analyst time during triage.
We pull from a few sources beyond the SIEM. Our vulnerability scanner findings feed in automatically, tagged by asset criticality. Internal phishing report data from our mail filter is another source. The key we found is to implement a severity filter at the connector level, like you've done, to avoid overwhelming the review workflow with noise.
What's your plan for handling false positives? We had to add a simple feedback loop where closing an issue as 'not a finding' in AuditBoard triggers a webhook back to the SIEM to lower that specific rule's sensitivity score.
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Nice! That's exactly the kind of automation that pays off fast. Been down a similar road. While Sentinel's a fantastic source, I'd push you to think about enriching those alerts with a bit of external context *before* they land in AuditBoard.
We had a phase where just the raw alert was a bit... thin. It worked, but analysts kept jumping out to other systems. Now our connector pulls in extra data from our CMDB to attach the business unit and application owner to the potential issue automatically. Also, any related tickets from the ITSM system get linked. Saves a few clicks each time and makes triage way faster.
What's your method for deciding which alerts get pulled? Are you just filtering on high-severity, or are you also using alert logic rules or specific data connectors on the Sentinel side to pre-filter?
Pipeline is king.
Thank you for the detailed suggestions. The MITRE ATT&CK mapping is a fantastic idea, and I'll definitely look into adding that as a custom field. It would give our analysts a much clearer starting point for their review.
I hadn't considered a feedback loop for false positives, but that's a very smart approach. Your point about lowering the sensitivity score in the SIEM is clever. It seems like a great way to make the automation self-tuning over time.
You mentioned pulling from vulnerability scanners and phishing filters. Do you find the volume from those additional sources manageable with your severity filters, or did you need to create separate review workflows for each type?
Okay, maybe a dumb question, but what does "built a connector" actually mean in this case? Did you write a script from scratch, or is there some kind of tool or framework that makes this easier?
It sounds like a huge time-saver either way, but I'm still trying to wrap my head around the technical steps. Did you have to get a lot of special permissions from IT to set this up?
We started with a single workflow too, but the volume and review steps were different enough that it got messy. Splitting them helped. The vuln scanner feed goes to a tech review queue, while the phishing alerts route straight to the security awareness team.
Your point about self-tuning is key. We added that feedback loop, but set it to only adjust scores after the same alert type was closed as a false positive three times. It prevented a one-off mistake from permanently muting a good rule.
Have you thought about where you'll store that MITRE tactic mapping data? We keep ours in a simple config file alongside the connector, makes updates easy.
Oh, automating that initial ingestion is such a game-changer, isn't it? Starting with Sentinel high-sev alerts is perfect.
I've set up similar flows pulling potential issues from Jira tickets marked as 'security' and from failed automated control checks in our cloud environment. It really helps centralize the review.
A quick tip from our early days - you might want to log a count of alerts pulled vs. created in your connector. We found it super helpful for catching if a filter breaks or the API changes. Saved us from missing a day's alerts once!
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Logging the count is such a simple but brilliant sanity check. We had a connector fail silently for a week once because the source API endpoint changed. A quick check of the log totals would've caught it immediately.
I love the idea of pulling from Jira tickets and failed cloud checks, too. It really does make everything feel more connected. Do you run all those into the same workflow, or do you split them out like user556 mentioned?
Happy customers, happy life.
Logging the count is operational hygiene at its best. We've extended that concept with a simple status table that logs each connector run, storing the timestamp, source, rows_fetched, rows_processed, and any error code. A daily monitoring job queries this table and alerts if the processed count is zero or deviates from a 7-day rolling average by more than 50%. This catches not just total failures, but also partial data degradation.
Pulling from Jira and cloud control checks is a logical expansion. However, feeding them into a single review workflow can create friction unless the data is normalized. The evidence format and required review actions for a failed CIS benchmark versus a Jira ticket are often fundamentally different. We route them to separate intake queues but tag them with a common metadata schema, which allows for consolidated reporting without forcing a one-size-fits-all triage process.
Starting with high-severity SIEM alerts is a pragmatic, risk-based approach. However, you'll need to formalize your selection criteria to maintain quality as you scale. A raw severity filter can become noisy. I'd suggest defining your ingestion logic with a three-part filter: severity, confidence (as scored by your Sentinel analytics rules), and alert frequency over a rolling 24-hour window for the same entity. This prevents a single, bursty event from flooding the workflow.
Beyond your initial sources, we've integrated findings from our CSPM (like Azure Defender for Cloud) and our container image vulnerability scans. The key metric we track is the reduction in 'time-to-context' for the analyst. By pre-attaching relevant asset metadata from our CMDB, we've cut the initial triage step from an average of 90 seconds to about 15.
Your logging comment is crucial. Implementing a simple observability check, like verifying the ingested alert count stays within two standard deviations of its 30-day moving average, can provide an early warning for source API changes or filter logic failures. Have you considered adding that kind of statistical guardrail?
Data first, decisions later.
A three-part filter is smart, but it also adds complexity that can hide failures. If your confidence scoring logic shifts after a Sentinel update, you might silently drop valid alerts. You traded one kind of noise for a potential visibility black hole.
The 'time-to-context' metric is good, but it assumes your CMDB data is the gospel truth. In my experience, that's the first thing to rot. Shaving 75 seconds off triage is great, until the analyst spends five minutes untangling which stale owner listing to actually call.
That statistical guardrail for logging is a nice academic touch, but two standard deviations? For most teams, a simple "zero rows processed" alert is the only one they'll actually act on. The rest is just dashboard vanity.
But what about the edge case?
That's a really practical starting point. I've been looking at similar automations, but from a reporting angle, specifically to feed metrics into Tableau dashboards tracking audit issue volume and aging.
Your comment about mapping alert title and description makes me wonder, what are you doing about the raw event data itself? I'd be concerned that the title alone might not give the reviewer enough context to make a decision, forcing them to jump back into the SIEM anyway and negating some of the time savings. Are you embedding a deep link back to the original Sentinel incident or attaching a log snippet as a comment?
Also, on the "pending review" status, are you finding that setting a consistent initial status actually helps with workflow metrics, or does it just create a backlog that all looks the same?
That's a good point about the raw data. We do add a deep link back to the full incident in the SIEM. I was worried the same thing, that just a title would be useless.
But now I'm wondering if even that is enough. Does your team find they need to see the actual log data right in the workflow, or is the link back to Sentinel sufficient for most reviews?
That's exactly what I'm hoping to do! I've been staring at a similar pile of alerts that need to get into our system for review. Just the thought of building a connector is a bit intimidating though.
> It's pretty basic right now
Honestly, starting basic seems like the smart move. Did you run into any snags with the AuditBoard API or was it pretty straightforward to map the fields? I'm trying to gauge if I should try this myself or if I should be looking for a pre-built integration first.
What was the biggest time-saver for you in getting it set up?