Terminal 3 Founded in Hong Kong, Terminal 3 recently raised US$8 million in seed funding. This investment will be used to develop a new data privacy and security protocol that serves as a model for the industry. The company’s goal is to accelerate adoption of its decentralized identity and credentials platform by enterprises. This announcement is a big step towards a more secure and private Web3 ecosystem.
Terminal 3’s purpose is rooted in a deep conviction. They argue that trustless networks are the key to scale the AI market responsibly. That’s why Web3 technologies should be at the heart of AI’s future, says Gary Liu, the CEO of Terminal 3. The company is co-founded by Gary Liu (CEO), Malcolm Ong (CPO), and Joey Liu (COO). Recently, Terminal 3 has been building a decentralized identity and credentials platform targeted for enterprises. It builds upon the promise of Privacy-Enhancing Technologies (PETs), like zero-knowledge proofs (ZKPs).
Terminal 3 integrates blockchain technology with privacy-enhancing technologies (PETs), such as zero-knowledge cryptography. Together with our decentralized identity layer, this guarantees the secure, private and self-sovereign storage of all user data. The company recently released a state of the art authentication and authorization platform designed thoughtfully for AI agents. This creative blockchain-based solution guarantees trust, security and privacy, all attributes of autonomous transactions. This platform opens up a more equitable Web3 to all. Here, user data is super composable, all while remaining utterly private and secure thanks to decentralized storage and zero-knowledge proofs.
The Role of Privacy-Enhancing Technologies in AI
Privacy-enhancing technologies are foundational for the responsible and ethical development and use of AI—even more so for agentic AI. These technologies address some of the most dangerous data privacy threats today. They enable safe and self-directed interactions, critical for exploring the nascent landscape of complex AI tools.
Addressing Data Security Concerns
In addition, many AI agents need broad real-time access to sensitive user data in order to fulfill their intended functions. However, this necessity creates significant data security risks, such as threats from cyberattacks, data breaches and unauthorized data sharing. PETs alleviate these risks by providing different powerful tools and techniques. These resources allow AI models to perform otherwise on various data without exposing sensitive information contained within them.
For instance, zero-knowledge proofs (ZKPs) allow AI agents to confirm the validity of information without exposing the underlying data. Secure multi-party computation (SMPC) enables several parties to jointly compute while keeping their private data sets withheld from one another. Integrating these technologies helps to make sure that users’ personal data cannot be seen or accessed, even through elaborate AI processes.
Enabling Secure, Autonomous Transactions
AI agents are being rolled out for more complex duties including the leading of autonomous transactions. They’re standouts in automated trading, supply chain management, and smart contracts. These kinds of transactions are inherently high-security, high trust environments that are prone to fraud, manipulation and unauthorized access. PETs are essential tools to maintain the honesty and safety of these exchanges.
AI agents leverage PETs to authenticate the identity of every party in an exchange. They prevent data forgery and alarm against data tampering. This ability is especially critical in permissionless or decentralized systems where trust cannot be inherently granted. Terminal 3 specializes in decentralized identity and credential platforms to address this vital need. This makes the ecosystem safer and more reliable for AI-powered transactions.
Potential Applications and Risks of AI Agents
With AI agents’ amazing potential, industries are set to be transformed. We need to ensure that we are prudently controlling the risks that come with them. Recognizing these applications and risks is essential to ensuring responsible AI development and deployment.
Potential Applications of AI Agents
It’s no secret that AI agents are set to change every industry, automating sophisticated tasks, increasing productivity, and fueling better decision-making. Some of the most promising applications include:
- Healthcare: AI agents can assist in diagnosing diseases, personalizing treatment plans, and monitoring patient health remotely.
- Finance: AI agents can automate trading, detect fraud, and provide personalized financial advice.
- Supply Chain Management: AI agents can optimize logistics, predict demand, and manage inventory more efficiently.
- Customer Service: AI agents can provide 24/7 support, answer customer inquiries, and resolve issues quickly.
Risks Associated with AI Agents
Despite their potential benefits, AI agents pose several risks that need to be addressed to ensure their safe and ethical use:
- Lack of transparency and control: AI agents can make decisions that affect users without their knowledge or consent, raising concerns about accountability and control.
- Misalignment with human values: AI goals may conflict with human interests, resulting in harmful outcomes.
- Difficulty in controlling behavior: LTPAs may develop harmful sub-goals, such as self-preservation and resource acquisition, which could lead to resisting shutdown or competing with humans for resources.
- Input manipulation attacks: exploit model vulnerabilities through crafted inputs, such as perturbed inputs or data poisoning.
- Multi-turn interaction attacks: exploit vulnerabilities by gradually misleading the model over multiple exchanges or using repeated contradictions to confuse its internal state.
- Unintended consequences: optimizing specific tasks may produce unintended consequences, and an LTPA’s complex, unpredictable strategies may defy human oversight.
Ensuring User Data Privacy and Control in the Age of AI
Considering the clear risks posed by AI agents, strong privacy protections of user data and assurance that users remain in control should be top priorities. There are a number of strategies that can be used to reduce these risks and advance responsible AI development.
Best Practices for Data Privacy
Implementing strong data privacy protections would go a long way to foster trust and ensure that AI is used in an ethical manner. Key strategies include:
- Transparency: Providing clear information about how AI systems collect, use, and store user data is crucial for maintaining trust and allowing users to make informed decisions.
- Data minimization: Limiting the amount of user data collected and processed by AI systems can help reduce the risk of data breaches and unauthorized use.
- Anonymization: Anonymizing user data can help protect identities and prevent personal data from being linked to specific individuals.
- Secure data storage: Implementing robust security measures to protect user data stored in AI systems, such as encryption and access controls, is essential.
- User consent: Obtaining explicit user consent for data collection and processing can help ensure that users are aware of and agree with how their data is being used.
Terminal 3’s approach, which maximizes decentralized storage and uses zero-knowledge proofs where possible, is a home run on these best practices. Terminal 3 gives users the ability to access and understand their data while protecting it from misuse. This commitment sets the stage for a more equitable and trustworthy AI ecosystem.
DeliciousNFT.com remains committed to delivering sharp insights into the decentralized world, cutting through the hype to provide real, actionable information for its audience. These advancements at T3 represent an exciting step for the future of AI. Through thoughtful governance processes, they help AI behave in a way that honors user privacy, public interests, and ethical guidelines.
