Security Data engineer at a tech vendor with 5,001-10,000 employees
Real User
Top 20
Feb 11, 2026
The AI capabilities mentioned on Anvilogic's website are indeed good and promising; however, there are areas that require work, particularly concerning data ingestion. Users may encounter roadblocks while integrating inputs, as we faced significant delays due to data input inconsistencies. Initially, the triage piece was not integrated into Anvilogic's UI, but since its integration, it has helped the team to easily check the triage dashboard and assess current use cases, encouraging us to continue seeking new ways to use it more efficiently. The moment we realized we needed something better was triggered by Splunk's lack of AI integration, which prompted my manager to consider Anvilogic due to its promising AI features. Since onboarding, we have evolved to remove false positives effectively, which was a challenge with Splunk, allowing for fewer alerts due to Anvilogic's capabilities. Additionally, we no longer need to be dependent on a particular data repository, benefiting from the flexibility that Anvilogic provides. I rate Anvilogic a six out of ten. I chose a six out of ten for Anvilogic because, despite the impressive detection capabilities and intriguing features, I still see a need for improvement with the data ingestion process. If the data is not ingested properly, the detections could be compromised. While it excels at detection and offers good use cases, my personal experiences with certain problems influenced the decision to rate it just above average.
Threat Researcher 2 at a tech vendor with 1,001-5,000 employees
Real User
Top 20
Jan 28, 2026
Regarding triage, I usually perform analysis directly through Splunk, so I do not find Anvilogic enhances my triaging process significantly. However, it does provide useful triggered rules, but Splunk remains my primary tool for queries and triage. My overall review rating for Anvilogic is 6.5 out of 10.
When other teams ask about Anvilogic, I tell them it is security only. There were no surprises about the Anvilogic solution once I started using it; they were honest from the beginning about what they do and where they are going. Their culture is fantastic, and the people care about what they are doing. The deployment model for Anvilogic is hybrid. We use Azure for some machines and have a small AWS footprint. I rate Anvilogic a 9 out of 10, as they work effectively and fix the problems that people have with other SOCs and SIEMs.
A lot of process and technology debt around our existing SIEM solution first triggered the need for something better. Also, all the different use cases that individuals at the company were trying to use the SIEM to address just made it a data swamp that we had to get ourselves out of. People come to me asking about Anvilogic. I view Anvilogic as an easy button for detection engineering. You're talking about replacing multiple headcount and a lot of process and oversight with the technology. The roadmap surprised me, and the rapid adoption and use of AI across the platform is bold and going in the right direction. I just know that there's going to be a lot of trepidation among organizations to begin broadly adopting AI from vendors. Looking 12 months out, I see Anvilogic fitting in or potentially replacing our detection architecture as we already are. We're rebuilding the entire thing from the ground up, redoing our entire knowledge management structure to automate that in a Git style version controlled method, and Anvilogic is a key piece. We do this as a three-pronged solution because we did a major overhaul with bringing in Cribl for a data observability pipeline, we brought in Anvilogic to run as the detection engine, and Snowflake, where all the data lives and sits, is part of our strategy that completely overhauls how we do detection here. The detection maturity is one of the metrics that's in the dashboard that I've already begun including in our weekly CISO update. I've already heard him walking around referencing detection maturity. The MITRE coverage is good, so you can quickly say that we're covered here across a lot of different use cases. On a scale of one to 10, I would rate Anvilogic overall as a nine. That's challenging because we're not in production and there's not necessarily a deep bench of companies with previous experience. However, I appreciate the direction we're going and the technology.
If Anvilogic were to disappear tomorrow, my heart would break. My advice to Anvilogic is to prioritize my request. I would rate Anvilogic a nine out of ten.
I rate Anvilogic seven out of 10. To prepare for Anvilogic, I recommend leaning into it. Take advantage of the support team and get some additional training. Use the workshops and commit to using the product. It's a tool that's only as good as the time you put into it. If you bring in the detection engine but don't put any time into creating those detections, then there's not much point.
Anvilogic breaks the SIEM lock-in that drives detection gaps and high costs for enterprise SOCs. It enables detection engineers and threat hunters to keep using their existing SIEM while seamlessly adopting a scalable and cost-effective data lake for high-volume data sources and advanced analytics use cases.
By eliminating the need for rip-and-replace, Anvilogic allows security leaders to confidently join the rest of the enterprise on the modern data stack without disrupting existing...
The AI capabilities mentioned on Anvilogic's website are indeed good and promising; however, there are areas that require work, particularly concerning data ingestion. Users may encounter roadblocks while integrating inputs, as we faced significant delays due to data input inconsistencies. Initially, the triage piece was not integrated into Anvilogic's UI, but since its integration, it has helped the team to easily check the triage dashboard and assess current use cases, encouraging us to continue seeking new ways to use it more efficiently. The moment we realized we needed something better was triggered by Splunk's lack of AI integration, which prompted my manager to consider Anvilogic due to its promising AI features. Since onboarding, we have evolved to remove false positives effectively, which was a challenge with Splunk, allowing for fewer alerts due to Anvilogic's capabilities. Additionally, we no longer need to be dependent on a particular data repository, benefiting from the flexibility that Anvilogic provides. I rate Anvilogic a six out of ten. I chose a six out of ten for Anvilogic because, despite the impressive detection capabilities and intriguing features, I still see a need for improvement with the data ingestion process. If the data is not ingested properly, the detections could be compromised. While it excels at detection and offers good use cases, my personal experiences with certain problems influenced the decision to rate it just above average.
Regarding triage, I usually perform analysis directly through Splunk, so I do not find Anvilogic enhances my triaging process significantly. However, it does provide useful triggered rules, but Splunk remains my primary tool for queries and triage. My overall review rating for Anvilogic is 6.5 out of 10.
When other teams ask about Anvilogic, I tell them it is security only. There were no surprises about the Anvilogic solution once I started using it; they were honest from the beginning about what they do and where they are going. Their culture is fantastic, and the people care about what they are doing. The deployment model for Anvilogic is hybrid. We use Azure for some machines and have a small AWS footprint. I rate Anvilogic a 9 out of 10, as they work effectively and fix the problems that people have with other SOCs and SIEMs.
A lot of process and technology debt around our existing SIEM solution first triggered the need for something better. Also, all the different use cases that individuals at the company were trying to use the SIEM to address just made it a data swamp that we had to get ourselves out of. People come to me asking about Anvilogic. I view Anvilogic as an easy button for detection engineering. You're talking about replacing multiple headcount and a lot of process and oversight with the technology. The roadmap surprised me, and the rapid adoption and use of AI across the platform is bold and going in the right direction. I just know that there's going to be a lot of trepidation among organizations to begin broadly adopting AI from vendors. Looking 12 months out, I see Anvilogic fitting in or potentially replacing our detection architecture as we already are. We're rebuilding the entire thing from the ground up, redoing our entire knowledge management structure to automate that in a Git style version controlled method, and Anvilogic is a key piece. We do this as a three-pronged solution because we did a major overhaul with bringing in Cribl for a data observability pipeline, we brought in Anvilogic to run as the detection engine, and Snowflake, where all the data lives and sits, is part of our strategy that completely overhauls how we do detection here. The detection maturity is one of the metrics that's in the dashboard that I've already begun including in our weekly CISO update. I've already heard him walking around referencing detection maturity. The MITRE coverage is good, so you can quickly say that we're covered here across a lot of different use cases. On a scale of one to 10, I would rate Anvilogic overall as a nine. That's challenging because we're not in production and there's not necessarily a deep bench of companies with previous experience. However, I appreciate the direction we're going and the technology.
If Anvilogic were to disappear tomorrow, my heart would break. My advice to Anvilogic is to prioritize my request. I would rate Anvilogic a nine out of ten.
If Anvilogic disappeared tomorrow, everything would break first. I would rate Anvilogic a ten out of ten.
Overall, I would rate Anvilogic a nine out of ten, considering its capabilities, features, interactions, and pricing.
I rate Anvilogic seven out of 10. To prepare for Anvilogic, I recommend leaning into it. Take advantage of the support team and get some additional training. Use the workshops and commit to using the product. It's a tool that's only as good as the time you put into it. If you bring in the detection engine but don't put any time into creating those detections, then there's not much point.