The Role of Reusable KYC in Fraud Prevention

This is the third article in a series of five.

Fraud in the financial sector is a growing international concern, with billions of dollars lost annually to criminal activities.

According to the Association of Certified Fraud Examiners (ACFE), financial institutions globally lose an estimated 5% of their revenue to fraud each year.

In this context, reusable Know Your Customer (KYC) processes have become increasingly relevant.

Reusable KYC offers a robust solution to enhance fraud prevention while streamlining the verification process in financial services.

The Relationship Between Reusable KYC and Fraud Prevention

Reusable KYC can prevent fraud at multiple stages, such as initial verification, data storage, and encryption, ensuring the integrity of customer identity and compliance with regulatory requirements.

1. Initial Verification

Identity verification: During the initial KYC process, the customer’s identity is thoroughly verified using various documents and biometric checks, establishing a trusted identity baseline.

Identity verification includes checking government-issued IDs and proof of identity like utility bills and documents.

Conducting video liveness checks has also become an integral part of the verification process.

These checks ensure the individual is present and physically interacting with the verification system, thereby preventing identity fraud through the use of stolen or counterfeit documents.

Fraud prevention: Ensuring that the customer is who they claim to be is critical for preventing fraudsters from using false identities to gain access to services.

This step is fundamental to fraud prevention methods and helps reduce fraud rates in financial institutions.

By incorporating these measures, financial institutions can mitigate the risks of identity theft and fraud at the outset.

2. Data Storage and Encryption

Secure Storage: The verified KYC data is securely stored in the format of a Verifiable Credentials, using advanced security mechanisms that ensure cryptographic integrity and tamper detection.

The KYC data is tied to an individual using “holder-binding” techniques like biometric authentication and/or digital signatures to ensure that the same individual who was issued the credential is also presenting the credential.

By implementing these robust security measures, financial institutions can maintain the integrity and confidentiality of customer information, significantly reducing the risk of data breaches and unauthorised access.

Fraud Prevention: Secure storage protects sensitive customer information from unauthorised access and breaches, reducing the risk of data theft, which could be used for fraudulent activities.

Encryption adds an additional layer of security, ensuring that even if data is intercepted, it cannot be easily deciphered or misused.

Implementing these robust techniques as part of a comprehensive fraud protection strategy helps financial institutions safeguard customer identities and uphold trust.

3. Reusability and Sharing

Data Reuse: Verified KYC data can be reused by the same organisation or shared with other institutions with customer consent.

This facilitates a smoother customer journey and reduces redundant verification processes.

By reusing verified data, financial institutions can enhance operational efficiency and provide a seamless experience for customers.

Additionally, sharing verified KYC information between institutions — while adhering to privacy and regulatory standards — can significantly curb the time and resources spent on repetitive identity verification.

Fraud Prevention: By reducing the need for repeated verifications, the chances for fraudsters to introduce false information at any point in the process are minimised.

Reusability ensures consistent document verification across multiple financial services.

This consistency is crucial in identifying and rejecting fraudulent attempts, thereby enhancing the overall security framework within the financial sector.

4. Periodic Updates and Monitoring

Regular Updates: KYC data is periodically updated to reflect any changes in the customer’s information, maintaining the accuracy and relevance of the data.

These updates ensure that any new risks associated with changes in personal circumstances are promptly identified and addressed.

By consistently refreshing KYC information, financial institutions can better detect anomalies and adjust their fraud prevention strategies accordingly.

Fraud Prevention: Keeping customer data current and accurate makes it difficult for fraudsters to exploit outdated or incorrect information.

Continuous updates are a part of a dynamic fraud prevention solution, allowing financial institutions to respond swiftly to emerging threats.

Institutions can maintain a proactive stance against fraudulent activities by integrating real-time monitoring and alert systems.

*This is a shared benefit between traditional and reusable KYC. As shown, reusable KYC solutions often lean on traditional KYC systems to ensure the integrity and reliability of customer data.

5. Real-Time Alerts and Red Flags

Monitoring Systems: Continuous monitoring systems can flag suspicious activities or inconsistencies in customer behavior.

Behavioural biometrics and artificial intelligence play a significant role in analysing patterns to detect anomalies that deviate from established norms.

By leveraging advanced algorithms and real-time data analytics, financial institutions can quickly identify and respond to potential fraud threats, significantly enhancing their overall security posture.

Fraud Prevention: Real-time detection and response to potential fraud prevent fraudulent transactions before they occur.

These systems are integral to modern fraud detection systems, enabling financial institutions to act swiftly and decisively against suspicious activities.

These advanced measures play a critical role in proactively identifying and intercepting threats, minimising fraud’s impact, and protecting the institution and its customers.

*Note that if the data is stored in a decentralised way, it wouldn’t necessarily possess these capabilities. They apply only when the data is plugged into traditional KYC systems.

6. Customer Consent Management

Consent Tracking: These systems are in place to track and manage customer consent for data sharing and reuse, ensuring that data is only shared with legitimate parties with customer approval.

Robust consent management frameworks comply with regulatory requirements like the GDPR and build customer trust by providing transparency and control over their personal information.

By integrating automated consent tracking systems, financial institutions can ensure customer preferences are respected, thus enhancing data privacy and security.

Fraud Prevention: This reduces the risk of unauthorised access and use, ensuring that data handling complies with Anti-Money Laundering (AML) laws and other regulatory requirements.

By adhering to stringent regulatory standards, financial institutions can significantly decrease the likelihood of fraudulent activities and enhance the overall security framework.

Comprehensive fraud prevention measures are essential for regulatory compliance, maintaining customer trust, and safeguarding the financial ecosystem.

7. Audit and Compliance

Regular Audits: Conducting regular audits of KYC processes and data usage ensures compliance with regulations and identifies any weaknesses in the system that could be exploited for fraud.

Routine audits provide financial institutions with a thorough review of their security measures, highlighting areas that need improvement. 

By maintaining a schedule of regular audits, institutions can proactively address vulnerabilities, thereby safeguarding customer information.

Fraud Prevention: Regular audits are essential for maintaining a strong fraud strategy and meeting all regulatory requirements.

These audits provide a systematic approach to reviewing and enhancing security measures, preventing potential breaches before they occur.

Regular audits contribute to a robust and resilient fraud prevention framework and ensure the ongoing protection of customer data and organisational integrity.

8. Integration with Fraud Detection Tools

Tool Integration: Reusable KYC data can be integrated with advanced fraud detection tools and machine learning algorithms to enhance the ability to detect complex and sophisticated fraud patterns.

By leveraging machine learning, institutions can analyse vast amounts of data and identify subtle anomalies that traditional methods might overlook.

Such integration bolsters the accuracy and efficiency of fraud detection and enables financial institutions to stay ahead of emerging threats in an ever-evolving fraud landscape.

Fraud Prevention: Leveraging comprehensive and accurate customer data improves the detection of criminal activities and reduces fraud losses in the financial industry.

Enhanced data accuracy enables financial institutions to spot inconsistencies and suspicious activities more precisely.

By implementing robust data analytics and monitoring systems, institutions can preemptively identify and mitigate potential risks, safeguarding their assets and customers.

*Another shared benefit between traditional and reusable KYC. As illustrated, reusable KYC solutions often lean on traditional KYC systems to establish a strong compliance foundation while optimising operational efficiencies and reducing costs.

Examples of How Traditional and Reusable KYC Work In Tandem

Verifying Customer Identities in the Banking Sector

Customer Onboarding Streamlined:

In the banking industry, traditional KYC processes are used during the initial customer onboarding phase to thoroughly verify identities using government-issued IDs, utility bills, and biometric checks. 

Once verified, this KYC data becomes reusable. For example, when a bank customer, John, opens a new savings account, his identity is initially verified using traditional methods. 

Later, when John applies for a mortgage, the bank can quickly access his verified KYC data, speeding up the process and reducing the need for repetitive checks.

Enhancing Fraud Detection in Financial Services

Continuous Monitoring and Real-Time Alerts:

Financial institutions benefit from the combination of traditional and reusable KYC by integrating verified customer data with advanced fraud detection tools. 

For instance, John, a customer at a financial services firm, undergoes an initial traditional KYC process when opening an investment account. 

His verified data is then reused for additional services, such as opening a retirement account. 

Throughout this period, if unusual activity, like a large unexpected transfer, is detected, an alert is triggered immediately, allowing the institution to act swiftly and prevent potential fraud.

Ensuring Compliance and Security in the Finance Sector

Regular Audits and Secure Data Sharing:

In the finance sector, maintaining compliance with regulatory requirements is crucial. 

Financial institutions use traditional KYC to establish a robust verification baseline and then employ reusable KYC for ongoing operations. 

For example, a financial advisory firm conducts regular audits of its KYC processes to ensure compliance with AML laws. 

Verified KYC data is securely stored and shared with customer consent directly from their personal digital wallet, allowing for efficient and secure data reuse across various financial products and services. 

This practice enhances security by preventing unauthorized access and breaches, and ensures that customer data is always current and accurate.

*This reliance on traditional KYC frameworks provides a robust foundation for reusable KYC solutions, ensuring they inherit the strengths of well-established KYC processes while offering enhanced flexibility and efficiency.

Reusable KYC — End-to-End Fraud Prevention

Reusable KYC enhances fraud prevention at various stages, from initial identity verification to ongoing monitoring and compliance.

It offers secure storage, reduces redundant verifications, ensures real-time fraud detection, and integrates with advanced fraud detection tools.

The overall impact of reusable KYC on fraud prevention in the financial industry is significant.

By improving data accuracy, enhancing security, and enabling seamless data sharing, reusable KYC strengthens the defenses against financial fraud.

Consider adopting this cutting-edge technology to harness the benefits of reusable KYC for your institution.

Contact us at [email protected] to learn more about our solutions and how we can help you enhance your fraud prevention capabilities.

Use Cases of Verifiable AI Across Industries

This is the fourth article in a series of five.

In an era where Artificial Intelligence (AI) is increasingly embedded in our daily lives, ensuring the authenticity and trustworthiness of data is crucial. The implementation of Verifiable Credentials (VCs) across various industries offers significant potential, from protecting intellectual property to ensuring data integrity and enabling secure AI interactions. As AI continues to transform how we live and work, the adoption of Verifiable AI (vAI) strategies becomes essential. This article explores the diverse applications of vAI across multiple sectors, providing detailed use cases and examining broader industry impacts.

Recap: What is Verifiable AI?

Introduced in our first article in this series, Verifiable Credentials are portable packets of data that are held by the user (which could be a person, an AI agent or simply a dataset) in a digital wallet and ‘signed’ or attested to by a ‘Trust Anchor’ – a trusted institution, company or individual that vouches for the information held in that Verifiable Credential. This means users are able to carry attested data in their digital wallet, just as we can carry attested identifications in our wallets, such as government-signed driver’s licences. The use of verifiable credentials in creating trust in AI-related interactions can also be known as ‘Verifiable AI’ (vAI), which could be used across many industries, from labelling the huge amounts of data needed to train AI models, to improving IP holder rights, to allowing for trusted interactions between autonomous AI agents.

Although this may seem like navel-gazing, the truth is that all this technology already exists and countless companies are working on improving its application to our public and private lives. As much as every forward-looking company needs an AI strategy, they also need a Verifiable AI strategy to ensure that they avoid many of the pitfalls in terms of data quality, deep fakes and IP protection that they may run into in our changing world.

vAI Use Cases Across Industries

In this article, we will be looking at multiple use cases for Verifiable AI across various industries, including Media & Entertainment, Social Media, Search Engine Optimisation, Healthcare, Customer Service, Manufacturing & Supply Chain, Cybersecurity, and the Financial & Legal industries. However, this list is by no means exhaustive. Any industry which is affected by AI will be affected by Verifiable AI, which presents multiple opportunities as well as necessary features for any AI use case.

If you think your industry or organisation presents Verifiable AI opportunities, please contact us at [email protected].

1. New Revenue Models

One area in which Verifiable AI is lacking, is a way to monetise the act of verification. Complying with Anti-Money Laundering regulations, creating Decentralised Identifiers and transacting on any SSI identity system is a costly endeavour for those using it. One reason why we have not yet begun using verifiable credentials ubiquitously is that there has been little economic incentive to make the switch. Contemporary online identity models make money for the current trust anchors in the system, and therefore they are unlikely to change models until they are incentivised financially to do so. 

Using services such as cheqd’s Credential Payments enable those participating in the trust triangle to commercialise these relationships. By making it possible for microtransactions for Verifiable Credential verifications to occur on chain, yet abstracted away so users can still pay and be paid in fiat, cheqd enables any network participant to develop a new revenue model. This is a thread that runs through the rest of the document. Using cheqd’s credentials services for payments turns many situations in which verifiable credentials can be used into a potential economic transaction – as AI grows in use, the value of trust also rises. 

How might Verifiable AI and cheqd help create new revenue models for the media and entertainment industry?

Content credentials enable trusted organisations to create a new commercial role for themselves as ‘Trust Anchors’, who verify the content of material, and can then get paid for offering this service. A video is more trustworthy, and therefore more valuable, if fact-checked by the BBC and/or Fox News, than if that video has no content credentials confirming its genuinity. This could create a new business model for trusted news organisations, which can then earn through using their reputation to verify as well as publish.

How might Verifiable AI and cheqd help create new revenue models for Social Media platforms?

Social Media platforms have some of the largest, most valuable datasets available for the training of AI about countless different individuals and their browsing patterns. Selling this as verified data (with the correct verifiable credentials regarding data privacy permissions etc.) could become a huge money-maker for these organisations. 

Additionally, having better information regarding the status of content (e.g. is it synthetic data or not) will increase the value of their data to others. Features already built on some social media platforms, such as the Community Notes section on X could also be disrupted and commercialised using content credentials to help create a more reputable system for combating misinformation. 

How might Verifiable AI and cheqd help create new revenue models for verifying the provenance of data?

Verifying the provenance of data is going to grow in importance as the value of quality datasets rises to meet the training needs of AI models. Being able to prove that a dataset is of high quality, with the correct licensing, certificates, lack of synthetic data and a high level of accuracy offers value to those looking to create models, and therefore it makes sense that those offering these verification services get paid. 

How can Verifiable AI technology help create new business opportunities for the Legal industry?

As contracts are increasingly signed digitally, it will create new opportunities for notaries to build their businesses in a more digital fashion, enabling digitised versions of notarised contracts to begin appearing. Given the scale possible in a digital setting, this could be a huge opportunity to grow the number of notarised, digital contracts – allowing them to work with new business models.

In Summary

Once microtransactions are possible for verifiable credential transactions, many commercial opportunities for creators, trust anchors and fact-checkers to be paid via microtransactions for their services can be unlocked, such as:

  • Micropayments to content creators who are officially recognised as the creator of an image
  • Payments for trusted organisations such as academic institutions or healthcare companies confirming the accuracy of data
  • Payments to an auditing firm who can confirm that an AI Agent is working on someone’s behalf

2. Content Credentials

Content credentials were the focus of the third piece in this series, but are worth bringing up here due to the multiple important industries that they impact. 

As AI-produced content becomes more ubiquitous, knowing where an image comes from becomes important, not only to avoid the spread of misinformation, but also because training AI models on synthetic data (AI-produced data) can lead to total model collapse. 

Championed by the C2PA and other new standards organisations, they aim to create a ‘chain of custody’ from the moment a picture is taken, to the moment it is viewed and checked by someone online. 

This has huge implications for combating misinformation, protecting Intellectual Property and creating new value creation opportunities for potential Trust Anchors, be them organisations or specific individuals. 

In our below use case, we will show how content credentials, which are already being used in many places, can create new methods of IP protection and reward for content creators.

How do content credentials work in practice?

  1. A content creator takes a career-ruining image of a British politician eating a bacon sandwich. His camera has tamper-proof hardware on it that records important metadata about the video, such as the location, time, and specific camera used (this helps prove that the picture was not AI generated). The hardware ‘signs’ a content credential attesting to the recorded metadata.
  2. As the picture is uploaded to editing software such as Adobe Photoshop, any changes made to it including AI generation or the removal of metadata, are recorded as additional credentials.
  3. Publishers looking to use an image for a news article check the content credentials to ensure the image is genuine before purchasing the rights to use the image
  4. Images or videos published on websites and social media would be able to check the content credentials of the image
  5. Republishers looking to use this content then make payments to the publisher, and others with IP rights, such as the photographer.

How might content credentials benefit the media & entertainment industries?

The world of media and entertainment will hugely benefit from the introduction of Verifiable Credentials. By providing explanations for how media content was generated and manipulated, organisations can combat misinformation and ensure their brand is not brought into disrepute by nefarious actors impersonating them to spread misinformation. The fact that creators and different entities can ‘Digitally Sign’ their intellectual property will help many organisations in protecting their IP rights.

How might content credentials benefit content creators?

Smaller, independent creatives, be they artists, freelance journalists or even TikTok influencers can also benefit from content credentials through enabling the protection of their intellectual property. It may also help freelance journalists to more independently build their own self-publishing networks whilst still enabling them to receive attestations from respected trust anchors for their work.

How might content credentials benefit the social media industry?

Social Media companies also have a vested interest in ensuring their platforms are not used to spread misinformation, as doing so can cause huge reputational damage and even political problems, as seen in the scrutiny Mark Zuckerberg faced in the wake of the 2016 election and TikTok’s recent woes in the US. By arming users on the ‘frontline’ of the misinformation war with the ability to check an image’s content credentials, it could help to reduce reliance on moderation teams to ‘fight misinformation’ and improve brand reputation.

How might content credentials benefit Search Engines?

Synthetic data is a huge danger to AI models, consuming too much of it can lead to total model collapse due to the AI ‘mutating’ as it copies more and more of itself. Using content credentials to determine whether content is AI generated or not will help organisations avoid the collection of synthetic data for their datasets, whether they sell this on or just use it for their own proprietary models.

In Summary

Content credentials unlock a huge amount of value for multiple industries. They can:

  1. Enable clearer knowledge of content’s provenance, creating a fingerprint on an image or video which can help showcase ownership. 
  2. Reduce the spread of misinformation and help protect brands
  3. Create new commercial opportunities for multiple industries involved in the production and consumption of content
  4. Improve the quality of data being collected by search engines and other crawlers

3. Data Provenance

“You are what you eat” applies to machines as well as humans – data is the most important input there is for AI models. 

A model trained on IP-protected data may risk the user getting sued by the rights-holders; a model trained on a small sample size of inaccurate health data could lead to incorrect diagnoses and patient deaths; a model trained on synthetic data may completely collapse as it begins replicating AI-generated data, leading to a ‘Habsburg Chin’ in the data. It is therefore crucial to any AI model that there is clear labelling for all of the datasets used to train their models. 

Verifiable Credentials are highly useful tools in this case, enabling datasets to carry verified information about themselves along with them. Rather than an AI Model developer having to cross reference reviews on different websites and consider if the reviews or ratings have been gamed, they can check that the Verifiable Credentials of a given dataset, verify that the accuracy of the data has been attested by an organisation they trust and decide if the data is of good enough quality for their model.

How does data provenance verification work in practice?

  1. Healthcare provider publishes anonymised data records on a data marketplace, most likely to have obtained consents from patients or their doctors
  2. They sign a Verifiable Credential with a DID from their organisation attesting that the data is accurate, whilst the patients sign a Verifiable Credential confirming they consent to the sharing of their data. They also receive a Verifiable Credential from standards bodies showing that their data complies with Data Sharing and Privacy laws
  3. Before purchasing a dataset, an AI model developer requests verification that the dataset is compliant with local data privacy laws and that the data is accurate and from a trusted source. These verifications can be supplied by the marketplace in the form of attached verifiable credentials.
  4. Once the verifiable credentials have verified that everything is in order, the model developer purchases the dataset
  5. A payment is made to the organisations which have verified the data’s accuracy and compliant
  6. The originally issued Verifiable Credentials can be reused repeatedly with attestors continually receiving payments

How might verifiable credentials enable better provenance of data in the healthcare industry?

Data provenance is critically important in the healthcare industry, where the integrity and reliability of data can have significant legal, ethical, and reputational consequences. Ensuring data accuracy and avoiding biases are essential to prevent incorrect diagnoses, ineffective treatments, and potential harm to patients. Given the sensitivity of healthcare data, compliance is paramount, and the use of data in models must always rely on sources verified as compliant with health and data protection regulations. 

Through the use of Verifiable Credentials, any dataset could come with a set of attestations attached, enabling anyone planning to train a compliant model to quickly confirm if a dataset is from a trusted source with high-quality data, without having to directly confirm that the dataset has the correct credentials.

How might verifiable credentials enable better provenance of data in the financial and legal industries?

In the financial and legal industries, accurate data provenance is critical due to their significant impact on our economies. It is essential that datasets used for training are correctly labelled, as bankers and lawyers handle other people’s money and business contracts. Any inaccuracies or biases in these datasets can lead to severe financial losses, legal liabilities, and reputational damage. These organisations, often holding substantial social capital, can also serve as ‘verifiers’ of data, ensuring that specific datasets are legally compliant.

How might verifiable credentials enable better provenance of data in the cybersecurity industry?

As with any AI model, the quality of data used to train it is key, meaning those buying data for their own models will want the utmost certainty that the data they are using is genuine and of high quality, especially given the sensitivity of cybersecurity to the functioning of any business. It may be that in the future, ensuring that cybersecurity departments at companies may demand that only certain verified datasets can be granted, to ensure bad quality data does not infect their models.

In Summary

The use of dataset labelling has many benefits for countless industries as they develop their AI capabilities: 

  • Ensure high quality data, non-synthetic data for many industries with high compliance requirements.
  • Create new ways to monetise data and creates new revenue models for Trust Anchors (e.g. universities, auditing companies)
  • Help create safer, more trustworthy datasets

4. Know Your AI Agent

OpenAI’s most recent offering, ChatGPT-4o showcases a newer kind of AI model – one capable of not just being able to break down tasks into smaller more manageable ones, but also going out and performing actions. 

The fact that this is now something even the most widely used LLM is becoming capable of means that any business able to implement this technology should be thinking about it. Very soon, AI agents will be able to automate many complex tasks previously performed by humans – an agent could make a restaurant reservation, implement a trading plan, or design and run a social media advertising campaign.

Despite the exponential growth in capabilities, AI agents will remain hugely limited until the amount of permissions available to them increases and the process of ‘trusting’ the AI on each side of the interaction is possible. Verifiable Credentials can be signed by humans, giving AI agents permissions to act on their behalf, and can also be used to give these agents ‘reviews’ and attestations about their effectiveness (on top of important information regarding how an agent was trained). 

How would AI agent verification work in practice?

  1. A customer wishes to book the time of a business dinner at an uptown restaurant. He signs verifiable credentials (VCs) giving his AI agent permission to book calendar events for him and to represent him in certain interactions, as well as giving the agent permission to share his dietary restrictions with other agents.
  2. His AI agent exchanges VCs with his colleagues’ agents to verify they are representing them, then all agree together on the most ideal time and date, they also confirm the dietary restrictions of their users, so this can be communicated with the restaurant.
  3. His AI agent then interacts with the AI customer service agent for the restaurant. Both share their representation credentials and the preferred time is agreed on, dietary restrictions are shared to ensure that the restaurant is able to accommodate them.. 
  4. Both agents use their Edit permission VCs to edit the calendars

How might ‘Know Your AI’ improve the customer service industry?

The customer service industry is poised for significant disruption by AI and AI agents. Ensuring that these AI agents have verifiable credentials is crucial. Users can assign these credentials to agents, granting them the correct permissions to perform tasks on their behalf.

Verifiable credentials are essential for maintaining high standards and ensuring that only specific, high-quality verified agents represent individuals or companies. Additionally, as AI agents become more prevalent and competitive, having verified reviews of different agent models will be invaluable. This will contribute to a more seamless user experience for potential customers, reducing waiting times and improving overall customer satisfaction.

On the other hand, AI agents should verify their answers with verifiable credentials to assure people of the quality of customer service and the accuracy of the information provided. This added layer of verification builds trust and confidence in the AI agents’ capabilities, leading to more effective and reliable customer interactions.

In short, ‘Know Your AI’ can enhance the customer service industry by ensuring AI agents are properly credentialed, providing high-quality service, and verifying their responses to assure customers of their reliability and accuracy.

How might ‘Know your AI’ improve the Manufacturing and Supply Chain industries?

For many years, people have predicted that logistics and manufacturing would be revolutionised through the use of blockchain, but the no-edit features of traditional public blockchains limited their use. Verifiable credentials however can have a strong impact, allowing for each node in a logistics network to give its stamp of approval along each step in an item’s journey. As all of this becomes more automated, ensuring the actors at each step – be they human or bot, have their own identity, will make ensuring an improved record of custody from beginning to end much more possible. 

How might ‘Know Your AI’ improve the healthcare industry?

There are already huge moves in healthcare to use artificial intelligence for diagnoses – and this trend will likely continue as AI becomes more powerful and effective than current human healthcare workers. Therefore, having ongoing recording of diagnoses made and medicines prescribed, by whom and by what will be incredibly important to ensure decision-making processes are documented.

How might ‘Know Your AI’ improve the cybersecurity industry?

Being able to recognise approved agents and what access and permissions they have will also likely be an important industry update in the future, enabling another method for avoiding sybil attacks or sophisticated bot attacks. Additionally, the imprints that approved agents would make, using their own DIDs, would enable better understanding of how security decisions were made within a company in which decision-making may be done by AI agents.

How might ‘Know your AI’ improve the Finance and Legal industries?

As AI agents begin taking on more responsibility for decision-making, the ways in which decisions were made will need to be better understood. Agents with their own Decentralised Identifier (DID) will be able to sign off on decisions they made so people and organisations have a better understanding of why certain decisions have been made by which agents. Having a record of what decisions were made, by which programmes and how will be very important, especially when it comes to important financial transactions. 

In Summary

Verifiable Credentials save a lot of friction when used in AI agent interactions. They enable us to:

  • Verify that agents have the correct permissions to complete their task on behalf of someone
  • Review and attest to the effectiveness of different agents , and different agent models
  • Create a record for the decisions made by AI agents

5. Proof of Personhood

To be able to verify the AI agents you are using is important, but to be able to verify the humans you are working with is of equal importance. Millions have been lost to fraud in recent years due to deepfake impersonations, and sybil attacks and Distributed Denial-Of-Service attacks can waste thousands every hour if successfully targeting the right organisation’s applications. Although captchas are somewhat effective, they are time consuming and annoying for users and no longer filter out every bot which attempts it, meaning new methods to prove proof of personhood will soon be needed.

It is therefore likely that just as proving one is a good bot will be important, so too will proving that one is a real person.

How can proof-of-personhood credentials help improve outcomes for cybersecurity departments and companies?

As AI makes cybersecurity attacks more sophisticated, the need for tighter proof of personhood protocols are clearly needed more than ever. Proving that you are truly talking to the correct person through the use of verifiable credentials could become hugely valuable as deep fakes become easier and cheaper to produce, as could replacement for low-security bot checks such as CAPTCHA with other proof-of-personhood credentials a person would pick up in their identity wallets.

How can proof-of-personhood credentials help prevent Customer Services being overrun by bots?

Distributed Denial-Of-Service and other types of sybil attacks can completely jam up customer service queues. Now that captchas can now be solved by AI, the use of Proof of Personhood credentials can be used by a person to prove that they are in fact a person and not a bot. This could range from supplying several social media accounts to sharing a reusable KYC credential, with the level of certainty up to the parties involved.

In Summary

Verifiable credentials offer a simple way to prove that one is interacting with a real person and not a bot. This can:

  • Prevent DDOS and sybil attacks
  • Mitigate fraudulent deepfakes aiming to socially hack companies 

6. Additional Opportunities Enabled by Verifiable AI

How can Verifiable AI help keep patient’s records available for diagnoses and training but still remain secure?

As healthcare diagnostics are increasingly performed by disjointed AI services rather than coordinated by a single professional (e.g. your family GP), more and more details about a person’s medical history are likely to get recorded purely in the need to make better diagnoses. Storing this centrally creates a huge honeytrap for data. Enabling users to hold their data as verifiable credentials themselves (and decide for themselves if they wish to sell or share it) would make the reviewing and recording of a patient’s medical records more decentralised, privacy-preserving and secure.

How can Verifiable AI create new business niches for the legal industry?

The ability to see Verifiable Credentials and Content Credentials will open up new legal avenues for litigation and compliance – especially around issues related to intellectual property. It is likely that verifiable credentials will become important parts of legal law cases in this area in the future as they create strong proofs around Intellectual Property and truthfulness that are hard to refute.

How can Verifiable AI create new business niches for the cybersecurity industry?

As the use of verifiable credentials as a safer, more convenient and privacy-preserving method of digital identity grows, likely fuelled by regulations such as the EU’s eIDAS2 coming into force, it is likely that there will be a growing niche of cybersecurity experts needing to focus on verifiable credentials.

In Summary

Verifiable credentials as a technology are likely to create new ways of working and new roles for the workforce. Using verifiable credentials will in all likelihood:

  • Make personal, sensitive data such as health records more secure and self-sovereign
  • Create new legal niches for IP-protection
  • Create or scale new identity management systems within cybersecurity

Final Words

Verifiable AI (vAI) presents transformative opportunities across various industries, enhancing transparency, efficiency, and trust. From improving content creation and media integrity to streamlining customer service interactions and securing financial transactions, the implementation of verifiable credentials is set to revolutionise the digital landscape. As AI continues to integrate into everyday operations, the need for verifiable credentials will become increasingly critical, ensuring data authenticity, protecting intellectual property, and fostering trust in AI-driven processes. Forward-looking companies must adopt vAI strategies to navigate the complexities of our evolving technological world effectively.

How can the cheqd network help?

The cheqd network has been working for three years on Decentralised Identity infrastructure. As well as being thought leaders in the SSI space, we have been increasingly involved in the steering committees of the DIF. Much of the work we have done in the past few years is to make the world of verifiable credentials much more interoperable. Recently, we joined the Content Authenticity Alliance and the C2PA, the major bodies setting standards for content credentials. Our major innovation in the last few years has been the introduction of Credential Payments, which enable network users to charge for verifying credentials, enabling new business models which have been keeping back the SSI community for years.

Are you interested in having your organisation or industry to capitalise on the benefits of Verifiable AI? Please don’t hesitate to contact us at [email protected]!