Secoda, a Toronto-based company specializing in AI-powered data search, cataloging, lineage, and documentation, has recently announced a successful Series A funding round, securing $14 million in investment. This capital infusion is earmarked for the advancement of its AI solutions, with the ultimate goal of enabling any enterprise user, regardless of their technical expertise, to seamlessly access, comprehend, and utilize company data. The user experience is designed to be as effortless as conducting a Google search.

This funding round brings Secoda’s total funding to $16 million and is spearheaded by its existing investor, Craft Ventures, with notable participation from Abstract Ventures, YCombinator, and Garage Capital. Distinguished figures from the data ecosystem, including Jordan Tigani (CEO of MotherDuck), Scott Breitenother (CEO of Brooklyn Data), and Tristan Handy (CEO of dbt), have also joined in this round.

Jeff Fluhr, co-founder and partner at Craft Ventures, emphasized the growing significance of data lineage understanding and data utilization for companies. He stated, “It has become increasingly important that companies not only have a full understanding of the lineage of their data from disparate sources but also harness their data to make more efficient and informed decisions. Secoda has built a powerful AI-powered data copilot for companies to do just that.”

The Current Data Challenge:

In today’s enterprise IT landscape, numerous systems are designed for diverse functions, creating a complex web of technologies that are essential for organizational efficiency. However, this complexity often results in disconnected data sources, with applications failing to communicate effectively and data remaining isolated.

Consequently, employees face challenges when seeking answers related to data. They must navigate through convoluted applications or seek assistance from the data team, diverting attention from other tasks. Etai Mizrahi, who encountered similar issues while working at Acadium, described the frustration: “Questions that seem simple enough to answer end up feeling like a huge, frustrating game of broken telephone.”

Addressing the Knowledge Gap: To bridge this knowledge gap, Mizrahi collaborated with colleague Andrew McEwen to launch Secoda in 2021, offering an all-in-one platform for data management and search. Secoda seamlessly integrates with business intelligence and transformation tools, as well as data warehouses, effectively connecting all components of a team’s fragmented tech stack to establish a single source of truth for company data.

To simplify the process further, Secoda employs a ChatGPT-powered assistant, enabling users to create documentation that adds contextual information to metadata. Users can also search their company’s consolidated data catalog using natural language queries. Mizrahi highlighted the platform’s capabilities, stating, “Secoda does not simply give you information but gives you answers, much like Google… Our customers have been able to leverage the platform to reduce the volume of inbound data requests by over 40%, reduce onboarding times by 50%, and reduce time teams spend on documentation by 90% — huge time savings for data teams.”

Future Plans:

With the recent funding injection, Secoda plans to bolster its engineering team and intensify research and development efforts, particularly in the area of AI enhancements. Additionally, the company intends to introduce “Secoda Monitoring” to assist data teams in ensuring the quality and accuracy of the data they use. This feature will provide insights into how changes affect assets and reduce data quality errors. Monitoring will also enable companies to track the operational efficiency of their data teams and identify potential cost savings.

Over the past year, Secoda has experienced substantial growth, expanding its customer base by a factor of five and managing over 100 million metadata resources, including tables, dashboards, columns, and queries. On the integration front, the data search tool currently supports 36 popular data warehouses, business intelligence tools, and productivity platforms, including Snowflake, dbt, and Looker. The company is committed to continually adding more connectors based on popularity and user demand.

HiddenLayer, a cybersecurity startup based in Austin, Texas, emerged in response to a cyberattack that exploited machine learning code at the founders’ previous company. Today, HiddenLayer has announced a successful $50 million Series A funding round aimed at bolstering the defenses of the ever-expanding array of AI models adopted by enterprises.

This funding round was spearheaded by M12, Microsoft’s Venture Fund, and Moore Strategic Ventures, with participation from Booz Allen Ventures, IBM Ventures, Capital One Ventures, and Ten Eleven Ventures.

HiddenLayer’s CEO and Co-Founder, Chris Sestito, expressed, “The rapid adoption of AI inspires us to accelerate our mission, ensuring that every security professional possesses the necessary tools and expertise to embrace AI securely.”

HiddenLayer already plays a crucial role in safeguarding AI/ML models for numerous Fortune 100 companies across various sectors, including finance, government, defense, and cybersecurity.

What HiddenLayer Offers:

Previously covered by VentureBeat, HiddenLayer has developed a suite of tools as part of its “MLSec” Platform, designed to safeguard enterprise machine learning (ML) and AI models. These tools do not directly access the models or compromise clients’ proprietary data and technology. Instead, they actively monitor the performance and operations of enterprise ML/AI models and associated applications in real-time. They scan for overarching vulnerabilities, provide recommendations for fortifying security, and detect the injection of malicious code/malware. Furthermore, they deploy defense mechanisms to thwart attackers and isolate intrusions.

HiddenLayer’s MLSec Platform includes an intuitive yet powerful dashboard, granting security managers immediate access to essential information about the security status of their enterprise ML/AI models. It automatically lists security issues and alerts in order of priority based on severity and stores data for compliance, auditing, and reporting purposes.

Additionally, HiddenLayer offers consulting services provided by their team of Adversarial Machine Learning (AML) experts, who remain up-to-date with the latest security trends and threats. These services encompass threat assessments, training for cybersecurity and DevOps personnel, and “red team” exercises to verify the effectiveness of clients’ defenses.

Key Partnership:

Earlier this year, HiddenLayer established a partnership with the prominent enterprise data lakehouse provider, Databricks. This collaboration enables Databricks enterprise customers to directly utilize HiddenLayer’s MLSec Platform for their models running on Databricks’ lakehouses. The integration is model-agnostic and covers model scanning, detection, and response. It empowers Data Scientists and ML Engineers to enhance their models’ security without altering their code or environment. As models are loaded, HiddenLayer’s model scanner ensures integrity and security. If an attack is detected, the integration responds automatically without human intervention.

Future Goals for Enterprise AI Security:

HiddenLayer’s inception traces back to an incident at its co-founders’ prior company, Cylance, where ML models fell victim to a cyberattack. Attackers exploited Cylance’s Windows executable ML model, revealing vulnerabilities and enabling the production of evasive binary files. While this event was concerning, it prompted the realization that attacks on ML/AI would escalate as more enterprises adopted generative AI to boost efficiency and performance.

Presently, HiddenLayer is experiencing rapid growth, having quadrupled its workforce in the past year. With the Series A funding secured, the company plans to hire an additional 40 personnel by year-end and continue expanding its client base.