Work-Bench’s Jonathan Lehr and team focus on enterprise technology trends

About a decade ago, Jonathan Lehr and Jessica Lin co-founded Work-Bench to help New York’s early-stage enterprise technology startups get the capital they needed to do business with corporate clients , support and contacts. The venture capital firm has since invested in more than 50 companies, including Cockroach Labs, Socure, Dialpad and Spring Health. (Here’s an article my colleague Alex Conrad wrote after they raised their third fund of $100 million last year.)

So, as part of our new CIO Insights series, I asked the Workbench General Partner to tell us his vision for 2023. In addition to his own views, he shared some thoughts from some of his team members and the founders he supports.

About Internet Security

Security leaders face two major challenges. On the one hand, companies continue to pay more attention to the performance of CISO organizations given the growing impact of cybersecurity threats. On the other hand, the board is demanding better ROI by cutting cybersecurity spending. By 2023, an increasing number of CISOs will use automated performance management tools to rationalize their budgets and integrate metrics and improve performance of their security programs. – Shirley Salzman – Co-Founder and CEO, view metrics

From the CISOs we’ve spoken with, we’ve seen them leverage more automation across their security programs. This is especially true in areas such as governance, risk, and compliance, where security leaders prefer that their security analysts focus on higher-order work rather than mundane, manual, but nonetheless necessary tasks. Take third-party risk and supplier due diligence as examples. AI can process stacks of vendor security questionnaires, which can unlock greater vendor risk coverage, a force multiplier for resource-constrained security teams. – McKellyworkbench

About Cloud and DevOps

Reliability has become the next DevOps frontier for Fortune 500 CIOs. Whether it’s a mobile banking app, e-commerce site, or streaming media, downtime is not only harmful to a company’s brand, it can damage their bottom line. In the era of cloud hosting and increasing microservices architectures, traditional incident management tools hinder reliability. New tools and approaches are emerging that don’t just alert you to issues, but can actually help you remediate, communicate better, and learn from incidents, improving your organization’s reliability. This is an area where we’ll see a lot of enterprise spending in 2022, and we expect it to continue to be a priority budget line item for CIOs, even as many other parts of cloud spending are cut. – Jonathan Lyleworkbench

About Data and Machine Learning

Often one of the first challenges faced by the data and engineering leaders we work with revolves around achieving visibility into their critical systems and implementing real-time risk communication of any anomalies. Given the proliferation of modern data architectures, and the large volumes of data stored and processed across distributed systems, proper data management (data collection, transformation, governance, privacy, and availability) is critical to enterprises. From financial services to highly regulated industries, there is an urgent need for businesses to react to “bad data” in a near real way to prevent any incidents or outages at the client end. By 2023, we expect to see growing awareness of the need to implement proper data monitoring and observability guardrails, with sophisticated solutions that enable enterprises to proactively address data issues in their batch and stream processing environments— — Priyanka Somra, workbench

With the recent excitement around underlying models, organizations should consider how to leverage internal data to optimize model performance for mission-critical use cases. For many of the organizations we interviewed, general-purpose pre-trained models have only scratched the surface in terms of value-added benefits for enterprise companies. Common models will expand the scope of machine learning, but the most impactful models and business outcomes will come from leveraging a company’s internal data. Unfortunately for many organizations, this data is scattered across multiple file stores and kept in a black box because it often contains sensitive personal health and identity information. By 2023, we expect to see technologies enabling the long tail of machine learning through new model deployment, optimizing model performance, and comparative learning. – Daniel Chesleyworkbench

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