Justin Lam is a senior research analyst at S&P Global Market Intelligence, leading data security research within the Information Security channel since October 2021. At S&P Global Market Intelligence, Justin leverages his years of industry experience and his unique understanding of both how customers buy and why sellers sell to help investors, practitioners and entrepreneurs understand and contextualize industry trends. Prior to this role, Justin successfully served and advised numerous startups in information and data security in strategy, sales and partner-development roles. He has built worldwide partnership and sales programs from scratch and has been fortunate enough to earn several “Presidents Club” and “Quota Club” awards. Within these startups, Justin has held both technical and enterprise customer-facing roles, with assignments in engineering, product management, customer success, consulting, prospecting and closing. Justin has been part of five exits, including two IPOs. He has also seen the process evolution of data security adoption. Justin holds a Bachelor of Science degree from the Tepper School of Business at Carnegie Mellon University.
While Proofpoint has traditionally focused on large enterprises and select public-sector organizations as its customer base, the acquisition of Hornetsecurity for a reported $1 billion will expand its reach to small and medium-sized businesses. Opportunities for integration abound.
As sensitive data volumes expand, the use cases for enterprises to leverage that data have heightened risks. With the fastest-growing volumes based in the cloud, cloud-native data security approaches must accurately and effectively scale. With these challenges in mind, we examine Bedrock's outlook.
The cybersecurity industry's largest annual event surpassed its own attendance records this year. The conference is also at the threshold of new economic, political and technological uncertainties. However, it could sharpen its appeal as a nexus of learning, innovation and networking.
Critical to modern app development, incorporating the AI model layer requires understanding and mitigating the weaknesses and risks for underlying services and models. The combination of new risks and new opportunities for vendors and their enterprise customers will require additional approaches.
Real-world data collection has often been a competitive moat and a regulatory challenge for incumbent SaaS and cloud offerings. New GenAI use cases wish to bypass incumbent SaaS competitive moats by relying less on real-world data, and build with synthetic data instead.
In the second report in this series, future trends for SaaS security posture management are considered. New challenges in trust, safety, architecture authenticity and shifting motivations for SaaS providers must be accounted for by enterprises to understand and anticipate future risks.
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