Advanced SOC Operations / CSOC,
API Security,
Next-Generation Technologies & Secure Development
Goldman Sachs-Led Round Fuels Harness’s Expansion into AI Security and Automation
Harness, a San Francisco-based AI software delivery platform, has secured $240 million in funding aimed at enhancing its capabilities in testing, securing, deploying, and maintaining code via artificial intelligence.
See Also: OnDemand | DevOps Vs SecOps – Collaborative Dynamics
The Series E financing round will enable Harness to expand its software development life cycle (SDLC) knowledge graph by incorporating third-party data, thus establishing a robust semantic layer for complex contextual understanding, according to co-founder and CEO Jyoti Bansal. This investment will allow Harness to enhance its 15-module platform to include functionalities such as load testing, mobile testing, API and code security, and cost optimization for AI workloads.
Bansal identified that only the initial phase of software engineering constitutes a small fraction of the overall process. A developer’s work transitions into a broader cycle focused on security and compliance—areas in which Harness aims to automate systematic solutions over time. “We have been building automation for these processes for many years,” he noted.
Founded in 2017, Harness currently employs 1,564 individuals. In May 2024, the company received $150 million in financing from Silicon Valley Bank. Subsequently, in February, it acquired API security startup Traceable, also co-founded by Bansal, to enhance its DevSecOps offerings. Later in September, Harness added vulnerability detection company Qwiet AI to its portfolio.
The company has plans to streamline cumbersome tasks through intelligent AI agents capable of autonomously creating CI/CD pipelines, rectifying security vulnerabilities, executing compliance checks, and managing infrastructure changes. Money from this round of funding will not only bolster these AI initiatives but also support market expansion and sustain growth, Bansal indicated.
The Role of an SDLC Knowledge Graph in Promoting Agentic AI Initiatives
Bansal explained that the SDLC knowledge graph enables AI agents to comprehend the unique context of each organization, which includes security policies, infrastructure, development practices, and testing protocols. Harness’s existing strengths in monitoring deployments, test outcomes, and changes will be further enriched using Series E funding to enhance the graph with external data sources such as threat intelligence and vulnerability scans.
For instance, a financial institution’s Knowledge Graph could encompass its security and compliance frameworks, infrastructure layout, development methodologies, and user experience standards. This contextual information feeds into AI functions that automate multiple tasks effectively.
Future modules under consideration include load, stress, and mobile testing—endorsing improvements in application performance and reliability in various operational contexts. Bansal emphasized the need to empower developers with tools that control costs and ensure the security of AI systems, including large language models, APIs, and data communication paths.
With over 100 integrations into key cloud infrastructure, security, and DevOps tools, Harness aims to channel critical external data into its SDLC Knowledge Graph while also making its insights accessible through APIs. Bansal reiterated the importance of a semantic layer within the graph to maximize the utility and actionable insights provided to AI agents.
“We aspire to continuously enrich our Knowledge Graph while also enabling our information to be utilized by other systems through integrations,” Bansal confirmed. This focus on enhancing data relationships reflects Harness’s commitment to establishing a comprehensive platform that addresses everything required to transition code into production seamlessly.
