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Work-Bench Co-founder and General Partner Jonathan Lehr
I first met Jonathan Rea when he and Jessica Lynn co-founded Work-Bench almost ten years ago, helping early-stage enterprise tech startups in New York with the funding they needed to do business with corporate customers. It was right after I helped get support, connection. The venture capital firm has since invested in over 50 companies including Cockroach Labs, Socure, Dialpad and Spring Health. (This was written by my colleague, his Alex Konrad, after we raised our third round of $100 million last year.)
So, as part of our new CIO Insights series, we asked the Work-Bench General Partner to share his thoughts on 2023. In addition to his own perspective, he shared some thoughts from some of the team members and the founders he supports.
About cyber security
Security leaders face two major challenges. On the one hand, companies continue to focus more than ever on the performance of CISO organizations in light of the ever-growing impact of cybersecurity threats. Meanwhile, the board is demanding that her cybersecurity spending be cut to increase her ROI. In 2023, we will see more and more of his CISOs using automated performance management tools to streamline budgets, consolidate security program measurements, and achieve performance improvements. – Shirley Salzman – Co-founder and CEO, See your metrics
From the CISOs we spoke to, we can see that they are leveraging more automation across their security programs. This is especially true in areas such as governance, risk and compliance. Security leaders prefer security analysts to focus on high-level work rather than mundane, manual tasks. Consider third-party risk and vendor due diligence, for example. AI can process high volumes of vendor security surveys. This provides broader coverage of vendor risk and can double power for security teams with few resources. – Kelly Mack,Workbench
In the cloud and DevOps
Trust is emerging as the next DevOps frontier for Fortune 500 CIOs. Whether it’s a mobile banking app, e-commerce site, or streaming media, downtime not only hurts a company’s brand, it hurts their bottom line. In an era of cloud hosting and increasingly microservices architectures, traditional incident management tools struggle with reliability. To improve organizational credibility, new tools and approaches are emerging that go beyond simply alerting you to problems and actually help you remediate incidents, improve communication, and learn from incidents. This is an area where we will see significant enterprise spending in 2022 and is expected to remain a CIO budget priority even as much of the rest of cloud spending is curtailed. – Jonathan Rea,Workbench
About data and ML
One of the most important challenges for the data and engineering leaders we work with tends to focus on gaining visibility into critical systems and enhancing real-time risk communication around anomalies. Given the proliferation of modern data architectures and the vast amounts of data stored and processed across distributed systems, proper data management (data collection, transformation, governance, privacy, and availability) is critical for enterprises. From financial services to highly regulated industries, businesses are urgently required to respond to “bad data” in an almost realistic way to prevent incidents and outages on the customer side. In 2023, on the need to implement proper data monitoring and observability guardrails, and the need to adopt advanced solutions that enable enterprises to proactively address data issues across batch and stream processing environments is expected to increase awareness of – priyanka somra Workbench
With the recent buzz around basic models, organizations should consider how to leverage their internal data to optimize model performance for mission-critical use cases. For many organizations we spoke to, common pre-trained models are just the surface in terms of value-added benefits for enterprise companies. General-purpose models expand the reach of machine learning, but the most impactful models and business outcomes come from leveraging a company’s internal data. Unfortunately, in many organizations, this data is kept in a black box as it is distributed across multiple file stores and typically contains sensitive personal health information and her identity information. . In 2023, we expect to see technologies that enable the long tail of machine learning through new model deployments, optimized model performance, and contrastive learning. – Daniel Chesley,Workbench
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