Outpost Ventures’ Post

I could not be more pleased to announce that Anvilogic has secured a $45M Series C funding led by one of the largest cybersecurity investors, Evolution Equity Partners and we welcome our newest Board Member, Karthik Subramanian to the company! Our existing investors also participated in the round - Ashu Garg & Sid Trivedi from Foundation Capital, Shirish Sathaye from Cervin, Noah Carr from Point72 Ventures, David Dubick from Outpost Ventures, Dean Mai & Chris Fisher from Myriad Venture Partners, Grayden Gaykowski & Spencer McLeod from G Squared, and Hunter Somerville from StepStone Group. This has been a fantastic journey getting to this milestone, not without challenges, of course, and I would like to thank our CUSTOMERS without whom we would not be here today, our INVESTORS who always have our back, and our TEAM who are the most passionate and talented people alongside whom I have the honor of working. This round is validation of the opportunity Anvilogic brings to the modernization of the SOC, with our multi-data platform SIEM architecture, allowing enterprises to go beyond legacy platforms and into the AI-first, cloud world, leveraging security data lakes and expanding their detection coverage, without having to rip & replace existing investments, and yet significantly reducing their costs. We look forward to the growth years ahead of us now! Read more: https://www.anvilogic.com.

I am honored that the British Insurance Awards nominated Reserv for Best Newcomer. In the summer of 2022, we made the bold (crazy?) decision as a US-based start-up (with six people total) to launch our first customer in the UK, timezone and logistics be damned. Fast forward nearly two years, and we have nearly forty team members in the UK and 200 globally. Reserv shows that industry-leading claims technology and excellent claims-adjusting talent can impact the claims experience, improve underwriting and control costs. I'm so grateful to the Reserv team and our early customers for the journey we've been on, and I am taking this as a moment to reflect on how rapidly we've scaled & all the ambitions we still have for the UK and Europe!

🚀 Reserv Raises $20M in Series A Funding for Insurance Claims Transformation 🚀 We're excited to share some fantastic news! Reserv has successfully secured $20 million in Series A funding to expand our generative AI claims data analysis tools. This funding round was co-led by Altai Ventures and Bain Capital Ventures (BCV), along with the support of several esteemed partners. Our goal is to help MGAs and insurance carriers improve claims handling, and this funding will play a pivotal role in making that a reality. Thank you for being a part of our journey toward a brighter future for the insurance industry! 🚀 #Insurtech #SeriesAFunding cc: CJ Przybyl and Martha Dreiling https://lnkd.in/e-fMjWzj

What’s your Goldilocks latency for threat detection? Some security architects prioritize speed: real-time measured in seconds. On the other side of the spectrum, I know of at least one popular security product that issues alerts once an hour. Consider these factors and share your latency preferences in the comments below. Too Fast: The infosec instinct says “I want detections as near real time as possible.” But that comes with a heavy price. Not necessarily in terms of infrastructure spend, though streaming analytics can be costly. The bigger issue with looking at live events “on the wire” is the impact to detection fidelity. The uncomfortable truth about real-time detections is that they rely too much on the content of the individual event. It’s like if a criminal investigation could only involve what the suspect carried during the arrest. In cyber terms, good context covers the user’s behavioral profile, the environment where the action took place and an up to date model of threat actor techniques. None of which can be captured well in a static rule. In response to these limitations, some organizations rely on streaming analytics platforms like Spark or Flink combined with low latency data platforms like Redis or DynamoDB. This introduces complexity, overhead and infrastructure cost while still facing significant limitations on context and flexibility. There’s also an impact on who from the team can participate in the detection engineering. Too Slow: Applying batch analytics to threat detection opens up all the history, details and models that weren’t available in the stream. But in certain situations a late detection is almost as bad as no detection. For example, guardrails around production code deployment aren’t going to be effective if they trigger an hour after vulnerable code shipped or sensitive data leaked. Just Right: If single-second detection latency is too noisy and single-hour latency is too laggy, Goldilocks might be in the single minutes. Now is a good time to start thinking about this balance. Snowflake is blurring the lines between batch and stream analytics, and while sub-second end to end latency is still out scope, we’re reaching a point where lag in the single minutes is doable. This evolution could change the math for where many organizations choose to do their detections. #securitydatalake

More on our investment in Cerby and why we're so bullish on the need for improved Identity and Access Management solutions across all indsutries, including financial services.