Safeguarding the Future: Protect AI Secures $35 Million to Defend Machine Learning and AI Assets

Artificial Intelligence Security Firm, Protect AI, Raises $35 Million in Series A Funding

Introduction

Seattle-based startup Protect AI, a machine learning and artificial intelligence (AI) security firm, has recently raised $35 million in Series A funding. The funding round was led by Evolution Equity Partners and included Salesforce Ventures and existing investors. This brings the total funding raised by Protect AI to $48.5 million. Protect AI was founded in 2022 by Ian Swanson, former worldwide leader for AI and machine learning at AWS, and Badar Ahmed, former director of engineering at Oracle. Richard Seewald, founder and managing partner at Evolution, will join the board of directors at Protect AI.

The Growing Need for AI Security

The rapid growth and adoption of machine learning and AI technologies have introduced new security risks. Both machine learning systems and artificial intelligence algorithms are vulnerable to adversarial attacks, such as poisoning training data and manipulating algorithms. Compromised AI systems can lead to bad decisions, reputational damage, compliance failures, and regulatory fines. Despite the magnitude of the AI and ML security challenge, the industry’s largest cybersecurity vendors do not currently provide a comprehensive solution to address these risks.

Protect AI‘s Solution: AI Radar

Protect AI offers a platform called AI Radar, which provides real-time visibility into the assets and inventory used in ML/AI systems. The platform addresses various security risks, including regulatory compliance, data manipulation, model poisoning, infrastructure protection, and brand damage. AI Radar’s key pillars include:

1. Real-time Visibility

AI Radar provides real-time insights into the attack surface of machine learning models. It enables organizations to monitor and detect security vulnerabilities and threats in their AI systems.

2. Immutable Bill of Materials (MLBOM)

The MLBOM is an automatically created document that tracks all components and dependencies within a machine learning system. It ensures visibility and auditability of the supply chain and helps identify potential security risks.

3. Pipeline and Model Security

AI Radar continuously scans ML models and other inference workloads using Protect AI‘s scanning tools. It automatically detects security policy violations, model vulnerabilities, and malicious code injection attacks. Additionally, it integrates with third-party application security and CI/CD orchestration tools.

Editorial: Filling the Gap in AI Security

Protect AI‘s Series A funding round highlights the growing recognition of the need for specialized AI security solutions. The traditional approach to cybersecurity is inadequate to protect machine learning and artificial intelligence systems. The unique risks posed by adversarial attacks on ML models require tailored solutions, like AI Radar, offered by Protect AI. This funding will enable Protect AI to further develop its platform and expand its reach.

Philosophical Discussion: The Ethical Implications of AI Security

While AI security is essential for protecting sensitive data and preventing malicious attacks, it also raises ethical concerns. As AI systems become more sophisticated and autonomous, the potential for misuse and unintended consequences increases. Protecting AI from adversarial attacks is crucial, but we must also consider the potential for AI systems to be used maliciously. Striking the right balance between security and ethical considerations is crucial for the responsible development and deployment of AI technologies.

Advice: Prioritizing AI Security

As the use of machine learning and AI continues to grow, organizations must prioritize AI security. Investing in specialized solutions, like Protect AI‘s AI Radar, can help mitigate the risks associated with adversarial attacks and protect valuable AI assets. It is also important for AI developers and researchers to integrate security measures into their systems from the early stages of development. Regular security assessments, vulnerability testing, and the implementation of best practices should also be part of an organization’s AI security strategy. By taking a proactive approach to AI security, organizations can build, deploy, and manage safer AI systems.