TL;DR:
- Protect AI, an AI and ML security company, raised $35 million in a series A funding round led by Evolution Equity Partners.
- The company aims to strengthen ML systems and AI applications against security vulnerabilities and emerging threats.
- Their security platform, AI Radar, provides real-time visibility and detection capabilities for ML environments.
- Protect AI’s MLBOM ensures comprehensive visibility and auditability in the AI/ML supply chain.
- The company plans to invest in R&D and expand research efforts to bolster AI Radar’s capabilities and identify vulnerabilities in ML supply chains.
Main AI News:
In a remarkable feat, Protect AI, a pioneering AI and machine learning (ML) security company, has announced the successful completion of a series A funding round, raising a whopping $35 million. Evolution Equity Partners, a prominent venture capital firm, led the investment, with major participation from Salesforce Ventures and existing investors Acrew Capital, boldstart ventures, Knollwood Capital, and Pelion Ventures.
The brainchild of Ian Swanson, an esteemed leader who previously spearheaded Amazon Web Services’ worldwide AI and ML business, Protect AI is committed to fortifying ML systems and AI applications against security vulnerabilities, data breaches, and emerging threats.
With the AI/ML security landscape becoming increasingly intricate, companies are grappling with the challenge of maintaining comprehensive inventories of assets and elements in their ML systems. The rapid expansion of supply chain assets, encompassing foundational models and external third-party training datasets, further complicates this issue.
Such security challenges expose organizations to grave risks concerning regulatory compliance, PII leakages, data manipulation, and model poisoning.
To tackle these pressing concerns, Protect AI has harnessed cutting-edge technology to develop an exceptional security platform named AI Radar. This innovative platform offers AI developers, ML engineers, and AppSec professionals real-time visibility, detection, and management capabilities for their ML environments.
“Machine learning models and AI applications are typically built using an assortment of open-source libraries, foundational models, and third-party datasets. AI Radar creates an immutable record to track all these components used in an ML model or AI application in the form of a ‘machine learning bill of materials (MLBOM),'” explained Ian Swanson, the CEO and co-founder of Protect AI, in an exclusive interview with VentureBeat. “It then implements continuous security checks that can find and remediate vulnerabilities.“
Having now secured a total funding of $48.5 million, the company is determined to utilize these newly acquired resources to scale its sales and marketing efforts, enhance its go-to-market activities, invest further in research and development, and strengthen customer success initiatives.
An essential part of this funding deal is the inclusion of Richard Seewald, the founder and managing partner at Evolution Equity Partners, who will be joining the Protect AI board of directors.
Proactively Protecting AI/ML Models through Unparalleled Visibility
Protect AI asserts that traditional security tools are woefully inadequate when it comes to monitoring dynamic ML systems and data workflows, leaving organizations vulnerable and ill-prepared to detect threats and vulnerabilities in the ML supply chain.
In response to this pressing challenge, AI Radar incorporates continuously integrated security checks to safeguard ML environments against active data leakages, model vulnerabilities, and other AI security risks.
The platform deploys integrated model scanning tools for LLMs and other ML inference workloads, adeptly detecting security policy violations, model vulnerabilities, and malicious code injection attacks. Furthermore, AI Radar seamlessly integrates with third-party AppSec and CI/CD orchestration tools and model robustness frameworks.
Through its visualization layer, the platform offers real-time insights into an ML system’s attack surface, automatically generating and updating a secure, dynamic MLBOM that meticulously tracks all components and dependencies within the ML system.
Protect AI emphasizes that this approach ensures comprehensive visibility and auditability in the AI/ML supply chain. The system maintains immutable time-stamped records, capturing any policy violations and changes made.
“AI Radar employs a code-first approach, allowing customers to enable their ML pipeline and CI/CD system to collect metadata during every pipeline execution. As a result, it creates an MLBOM containing comprehensive details about the data, model artifacts, and code utilized in ML models and AI applications,” explained Protect AI’s Swanson. “Each time the pipeline runs, a version of the MLBOM is captured, enabling real-time querying and implementation of policies to assess vulnerabilities, PII leakages, model poisoning, infrastructure risks, and regulatory compliance.”
A Cutting-Edge MLBOM: Unraveling the Secrets of AI/ML Models
Drawing a crucial distinction between a traditional software bill of materials (SBOM) and their innovative MLBOM, Swanson highlighted that while an SBOM merely constitutes an inventory of a codebase, an MLBOM encompasses a comprehensive inventory of data, model artifacts, and code.
“The components of an MLBOM can include the data that was used in training, testing, and validating an ML model, how the model was tuned, the features in the model, model package formatting, OSS supply chain artifacts, and much more,” elaborated Swanson. “Unlike SBOM, our platform provides a list of all components and dependencies in an ML system so that users have full provenance of their AI/ML models.”
Swanson also shed light on the reality that numerous large enterprises utilize multiple ML software vendors, resulting in various configurations of their ML pipelines. However, AI Radar remains vendor-agnostic and seamlessly integrates all these diverse ML systems, creating a unified abstraction or “single pane of glass.” Through this unified view, customers can readily access crucial information about any ML model’s location, origin, and the data and components employed in its creation.
Additionally, the platform aggregates metadata on users’ machine learning usage and workloads across all organizational environments, empowering them to create policies, deliver model BoMs (bills of materials) to stakeholders, and identify and mitigate risks across their entire ML ecosystem.
“The metadata collected can be used to create policies, deliver model BoMs (bills of materials) to stakeholders, and to identify the impact and remediate risk of any component in your ML ecosystem over every platform in use,” shared Swanson. “The solution dashboards facilitate user roles/permissions that bridge the gap between ML builder teams and app security professionals.”
Future Plans for Protect AI: Unyielding Focus on Advancement
Swanson revealed that Protect AI envisions channeling its resources into three key areas of research and development: enhancing AI Radar’s capabilities, expanding research to identify and report additional critical vulnerabilities in the ML supply chain of both open-source and vendor offerings, and furthering investments in the company’s open-source projects NB Defense and Rebuff AI.
According to Swanson, a successful AI deployment can swiftly enhance company value through innovation, improved customer experience, and increased efficiency. Hence, safeguarding AI in proportion to the value it generates becomes paramount.
“We aim to educate the industry about the distinctions between typical application security and security of ML systems and AI applications. Simultaneously, we deliver easy-to-deploy solutions that ensure the security of the entire ML development lifecycle,” said Swanson with conviction. “Our focus lies in providing practical threat solutions, and we have introduced the industry’s first ML bill of materials (MLBOM) to identify and address risks in the ML supply chain.“
Conclusion:
The success of Protect AI’s recent funding round reflects the growing demand for advanced AI and ML security solutions in the market. With the continuous expansion of AI applications and the increasing complexity of ML systems, organizations are now prioritizing security to safeguard against potential risks and threats. Protect AI’s AI Radar platform and innovative MLBOM approach position them as key player in the market, providing businesses with the necessary tools to fortify their AI and ML assets. As the AI landscape continues to evolve, Protect AI’s commitment to R&D and its vendor-agnostic approach will likely solidify its position as a leader in the AI security space.
