Deepfake deceptions are fooling people in our expanding and ever-improving digital world. Imagine arriving at work one morning to discover all employees have received an important video announcement from the CEO and are scrambling to comply with the instructions it contains. Their responsiveness would be impressive if not for one thing: The CEO never recorded or sent the video, and now must somehow undo the resulting damage. 

Improvements in artificial technology (AI) and machine learning (ML) is making such flawless deepfake deceptions possible. These fake videos and audios have the potential to undermine security at every level from small businesses to global governments. 

How Deepfake Works

A deepfake is a video or audio made by employing AI and ML to create an exact likeness of a person saying or doing things he or she never actually said or did. The deception plays on the human tendency to believe what is seen and can be very effective in making it appear as though the content of a video is genuine. 

These videos aren’t simply fakes created by hackers skilled in forgery. Deepfakes rely on a form of machine learning in which two networks are fed the same data sets and pitted against each other in a back-and-forth battle of generation and detection. Known as generative adversarial networks (GANs), these systems consist of one network creating fakes and another evaluating the fakes for flaws. The data set consists of hundreds or thousands of images and videos of the person to be imitated, and a forgery is considered good enough when the detection network no longer rejects the results. 

Deepfake Deceptions

Deepfake audio and video involve using AI algorithms to manipulate or synthesize speech or audio to create realistic yet false content. The risks associated with deepfake deceptions include:

1. Misinformation and disinformation: Deepfake can be used to spread false information and manipulate public opinion by making it appear as if someone said something they didn’t.
2. Reputational damage: Deepfake can be used to defame or damage the reputation of individuals by making them appear to say something controversial or damaging.
3. Privacy invasion: Deepfake can be used to invade the privacy of individuals by synthesizing audio content that appears to be of them, but is not.
4. Psychological harm: Deepfake can cause psychological harm to individuals who are portrayed in false or misleading content.

Deepfake has the potential to cause harm and undermine trust in information and media, so it’s important to approach all content with a healthy dose of skepticism.

Artificial Intelligence Factor

Artificial Intelligence is a key component in the creation of deepfakes. AI algorithms are used to analyze and manipulate audio and video content to create realistic yet false depictions of individuals. The following are some ways in which AI contributes to deepfakes:

1. Image and speech synthesis: AI algorithms, such as Generative Adversarial Networks (GANs), are used to generate synthetic images and speech that are almost indistinguishable from the real thing.
2. Face and voice recognition: AI algorithms are used to analyze and manipulate face and voice recognition data to swap the faces or voices of individuals in audio and video content.
3. Machine learning: AI algorithms are trained on large amounts of data to learn patterns in facial movements, speech patterns, and other features that can be used to manipulate audio and video content.

AI plays a critical role in the creation of deepfakes by enabling the creation of realistic and highly convincing false audio and video content. As AI technology continues to advance, the quality and realism of deepfakes is likely to improve, making it even more important to be aware of their potential risks.

Hackers and Malicious AI

When deepfakes first appeared, people mostly used the technology to goof off and create fake pornographic videos. However, the software to produce such videos is readily available to everyday users, making it simple for hackers to employ deepfake tactics and use realistic false content to manipulate their targets. 

Deepfakes are prime candidates for viral status and can spread rapidly across social media. Because fake rumors can take as long as 14 hours to be recognized and debunked, a well-produced deepfake could become entrenched in the public mind as truth long before the deception was detected. Hackers can take advantage of the popularity of viral fakes to spread videos containing malware or record messages designed to entice users to click on links as part of a phishing attack. 

Deepfakes may also be used to draw people to websites in which malicious code has been embedded, turning their computers into tools for mining cryptocurrency. Known as cryptojacking, this kind of attack can also be launched on mobile devices and run undetected in the background as users go about their daily tasks. 

Deepfake Deceptions and Access Control

Deepfake technology is progressing to the point of perfection, and rapid advances in AI and ML mean scenarios like the one described above can no longer be relegated to the realm of science fiction. Using deepfakes, hackers could trick employees into giving away a great deal of information, including access credentials, financial records, tax documents, customer profiles and proprietary company data. 

Because GANs require a significant number of images to create realistic deepfakes, this kind of attack isn’t likely to become the norm overnight. However, the internet in general and social media in particular provides a wealth of pictures and videos posted by users and could theoretically be mined for the data sets necessary to train GANs to produce convincing results. 

Employees tricked by deepfakes or those who indulge in viral videos on company time could easily open the door for hackers to access business networks and fly under the radar or launch large-scale attacks. Such a prevalent threat to access control and compliance requires an updated approach to security. 

How to Identify Deepfakes

Identifying deepfakes can be challenging, as they are designed to look and sound realistic. However, there are some tell-tale signs to look for that can help you determine if an audio or video is a deepfake:

1. Audio-visual inconsistencies: Look for discrepancies between what you hear and what you see in the audio or video. For example, the lips might not match the words being spoken, or the facial expressions might not match the emotions being expressed.
2. Unnatural movements: Look for unnatural movements in the video, such as stiff or jerky movements, or movements that don’t match the audio.
3. Artificial artifacts: Look for artifacts, such as blurring or pixelation, that suggest the audio or video has been artificially manipulated.
4. Background inconsistencies: Check for inconsistencies in the background of the video, such as objects appearing or disappearing, or changes in lighting that don’t match the audio.
5. Metadata analysis: Analyze the metadata of the audio or video file to determine if it was edited or manipulated.
6. Use of specialized software: There are specialized software programs that can analyze audio and video files to detect deepfakes.

Keep in mind that deepfakes are constantly improving and new techniques are being developed, so it’s important to approach all audio and video content with a healthy dose of skepticism and to be aware of the latest methods for identifying deepfakes.

Preparing for Deepfake Security Threats

To get your network and your employees ready to stand up against the potential risks posed by deepfake videos: 

• Develop and deploy ongoing security training 
• Monitor employee activities on company devices 
• Update your BYOD policy to prevent infected devices from spreading malware to your network 
• Invest in security software with deep learning capabilities to predictively detect malware threats 

Combining employee training with machine learning software minimizes the likelihood of human error and leverages the power of artificial neural networks to protect your company from sophisticated threats and deepfake deceptions. 

The rise of deepfake in a world where fake news is already a concern signals a future in which it could be nearly impossible to trust anything you read, hear or see. Detecting falsehoods requires an updated approach to security, including employing the same technologies used to create deepfakes. The future of security may boil down to beating hackers at their own games, and learning to identify and outsmart threats launched using fake video content could be just the start of a new wave of necessary security upgrades.

This article provides a NIST digital identity summary and update related to NIST special publication 800-63 for digital identity guidelines. The National Institute of Standards and Technology (NIST) digital identity guidelines, known as Special Publication (SP) 800-63, provide recommendations for creating and maintaining secure digital identities. The guidelines are intended to help organizations, government agencies, and other entities establish a secure and trustworthy digital identity ecosystem.

The guidelines offer topics related to digital identity, including:

• Authentication: guidelines for verifying the identity of a user through various methods such as multi-factor authentication, knowledge-based authentication, and biometric authentication.
• Authorization: guidelines for granting access to resources and information based on the user’s verified identity.
• Identity proofing: guidelines for verifying the identity of an individual, including identity proofing remotely and in-person.
• Identity assurance: guidelines for determining the level of trust that can be placed in an individual’s identity claim.
• Risk-based authentication: guidelines for assessing the risk level with a given transaction and adjusting authentication methods accordingly.
• Out-of-band authentication: guidelines for using a separate method to verify an identity.

The NIST digital identity guidelines are voluntary but widely adopted by many companies and governments globally. They are designed to provide a comprehensive and flexible framework for creating and maintaining secure digital identities, with a focus on providing robust remote identity proofing and device-based authentication options, while balancing security and usability.

Purpose of NIST Digital Identity Guidelines

The purpose of NIST digital identity guidelines is to provide a set of best practices for creating and maintaining secure digital identities, to help organizations and government agencies to establish a secure and trustworthy digital identity ecosystem, and to provide a comprehensive and flexible framework for creating and maintaining secure digital identities.

NIST Digital Identity Updates

The National Institute of Standards and Technology (NIST) regularly updates its digital identity guidelines, known as Special Publication (SP) 800-63. These guidelines provide recommendations for creating and maintaining secure digital identities, including guidelines for authentication and authorization. Some of the changes in recent updates include:

• SP 800-63-3 (2017): This update introduced new guidelines for multi-factor authentication and introduced the concept of “verifiers” (organizations or entities that verify identities) and “subscribers” (individuals who are seeking to prove their identity).
• SP 800-63B (2018): This update provided additional guidelines for multi-factor authentication, including guidelines for using knowledge-based authentication (KBA) and risk-based authentication.
• SP 800-63C (2020): This update was a major revision of the previous version, which added new guidelines for remote identity proofing, device-based authentication, and more. The guidelines are focused on the new concept of “identity assurance level (IAL)” and “authentication assurance level (AAL)”

It is important to note that NIST guidelines are voluntary, but are widely adopted by many organizations and government agencies in the United States and around the world.

SP 800-63C Update 2020

SP 800-63C, which was released in 2020, is a major update to the previous version of NIST’s digital identity guidelines. Some of the key updates include:

• Remote Identity Proofing: This update introduces new guidelines for verifying identities remotely, such as through online or video-based methods. This is important in light of the increased use of remote work and online services.
• Device-based Authentication: The update also includes new guidelines for device-based authentication methods, such as using a device’s biometric data or other unique characteristics to authenticate a user.
• Identity Assurance Level (IAL) and Authentication Assurance Level (AAL): In previous versions of the guidelines, NIST recommended different levels of authentication based on the sensitivity of the information being accessed. In this update, NIST introduced the concept of “identity assurance level (IAL)” and “authentication assurance level (AAL)” to provide a more comprehensive framework for assessing the level of assurance required for different types of transactions.
• Biometric Authentication: This update also provides specific guidelines for biometric authentication, which is becoming more widely adopted as a means of verifying identity.
• Risk-based Authentication: The guidelines also introduce the concept of risk-based authentication, which allows organizations to assess the level of risk associated with a given transaction and adjust their authentication methods accordingly.
• Out-of-band Authentication: The guidelines also provide recommendations for out-of-band authentication, which is a method of authentication that uses a separate communication channel (e.g. SMS, phone call) to verify a user’s identity.

Overall, the SP 800-63C update provides a more comprehensive and flexible framework for creating and maintaining secure digital identities, with a focus on providing more robust remote identity proofing and device-based authentication options.

What is Remote Identity Proofing?

Remote identity proofing, also known as remote identity verification, is the process of verifying a person’s identity remotely, typically through an online or video-based process. This can be accomplished through various methods, including:

• Document verification: This involves verifying a person’s identity by comparing information on a government-issued ID (e.g. passport, driver’s license) to information provided by the individual.
• Knowledge-based authentication (KBA): This involves verifying a person’s identity by asking them to answer personal questions, such as their mother’s maiden name or the name of their first pet.
• Biometric verification: This involves using a person’s unique physical or behavioral characteristics (e.g. fingerprints, facial recognition) to verify their identity.
• Video-based verification: This involves using video conferencing technology to conduct a live interview with the individual to verify their identity.

Remote identity proofing is becoming increasingly important as more and more transactions and interactions are conducted online, and in light of the increased use of remote work and online services. The NIST SP 800-63C guidelines provide recommendations for remote identity proofing, including the use of multiple methods to verify identity in order to increase the overall level of assurance.

It’s important to note that remote identity proofing is not a replacement of in-person identity verification, but rather it’s an additional means of identity verification that can be used in certain circumstances where in-person verification is not possible or feasible.

What is Risk-based Authentication?

Risk-based authentication (RBA) is a method of assessing the level of risk associated with a given transaction and adjusting authentication methods accordingly. This approach allows organizations to balance security and usability by only requiring stronger authentication methods when the risk of fraud or unauthorized access is higher.

Risk-based authentication typically involves evaluating a number of different factors to determine the level of risk associated with a given transaction. These factors can include:

• The type of transaction being conducted
• The sensitivity of the information being accessed
• The location of the user
• The device being used
• The behavior of the user (e.g. whether they have a history of suspicious activity)

Based on the level of risk determined, the organization can then choose an appropriate level of authentication to use. For example, if the risk is low, a simple username and password may be sufficient, while a higher risk transaction may require multi-factor authentication (MFA) or other stronger methods.

Risk-based authentication is becoming an increasingly popular approach to identity and access management, as it allows organizations to provide a more seamless user experience while still maintaining a high level of security. The NIST SP 800-63C guidelines provide recommendations for risk-based authentication, including how to evaluate risk and how to implement risk-based authentication in a way that is both effective and secure.

What is Out-of-band Authentication?

Out-of-band authentication (OOB) is a method of authentication that uses a separate communication channel to verify a user’s identity. This separate channel is typically used as a secondary means of authentication, in addition to something the user knows (like a password), something the user has (like a security token), or something the user is (like a biometric).

There are different ways OOB authentication can be implemented, but some common examples include:

• Sending a one-time passcode (OTP) to a user’s mobile phone or email address and asking the user to enter it on the login page
• Making a phone call or sending a text message to a user’s phone number, and asking the user to confirm their identity by responding to the message
• Using an application such as Google Authenticator, that generates a time-based OTP that the user must enter in addition to their password.

The idea behind OOB authentication is that it can be more secure, as it ensures that the person attempting to log in has access to a device or communication channel that is only available to the true user. It can also serve as an additional layer of security in case the primary means of authentication is compromised.

OOB authentication can be used in various scenarios, including high-security environments, such as financial institutions, government agencies, and healthcare organizations, as well as for online transactions that involve sensitive information or large amounts of money. The NIST SP 800-63C guidelines provide recommendations for OOB authentication, including how to implement it in a way that is both effective and secure.

NIST Digital Identity Update Video

Zero trust cybersecurity model assumes that all network activities cannot be trusted and that every access request should be validated before permission is granted to access resources. This model is designed to mitigate the risk of unauthorized access to sensitive data or systems and to prevent the spread of malware or other malicious activity within a network.

The Zero Trust security concept advocates for always verifying the identity of users and devices before granting them access to network resources, regardless of their location or whether they were authenticated for past activities. This approach is based on the premise that organizations should not automatically trust any user or device within their network, and that all network traffic should be treated as potentially malicious until it has been properly authenticated and authorized.

In a zero-trust environment, all users, devices, and traffic are treated as potential threats, and every access request is verified and authenticated using multiple layers of security controls. This includes using strong, multi-factor authentication methods, such as passwords and security tokens, to verify the identity of users, as well as using network segmentation and micro-segmentation to limit access to only those resources that are necessary for a user to perform their job.

The goal of a Zero Trust security model is to protect against cyber threats by implementing strict access controls and continuously monitoring and verifying the identity of users and devices. This is often achieved through the use of multi-factor authentication, network segmentation, and secure remote access solutions.

Adopting a Zero Trust cybersecurity model can help organizations improve cybersecurity, and reduce data breach risks or other security incidents. However, implementing a zero-trust model can also require significant changes to an organization’s network infrastructure and security protocols, and may require the use of specialized security tools and technologies.

One important aspect of a Zero Trust cybersecurity model is the concept of “least privilege,” which means that users and devices are only granted the minimum access necessary to perform their job duties, and that access is continually monitored and reviewed. This helps to minimize the risk of unauthorized access and can help to prevent data breaches and other cyber-attacks.

How Zero Trust Cybersecurity Model Prevents Data Breach

Zero trust cybersecurity model assumes that any user, device, or system within an organization’s network may be compromised and should not be trusted automatically. Instead, each access request should be validated before permission is granted.

The goal of zero trust is to prevent data breaches by creating multiple layers of defense and continuously verifying the trustworthiness of users, devices, and systems. This approach helps to reduce the risk of a data breach by minimizing the number of potential entry points for attackers and continuously monitoring and verifying the identity and trustworthiness of those who are granted access to sensitive data.

Some specific ways in which zero trust can help prevent data breaches include:

Multi-factor authentication: Requiring multiple forms of authentication, such as a password and a security token, can help ensure that only authorized users are granted access to sensitive data.

Access controls: Zero trust systems typically use granular access controls to limit what users can see and do based on their role and needs. This helps to reduce the risk of unauthorized access to sensitive data.

Network segmentation: Zero trust systems often use network segmentation to create isolated networks for different groups or types of data. This helps to limit the spread of any potential compromise.

Continuous monitoring: Zero trust systems continuously monitor for unusual activity and can automatically block or alert on suspicious activity. This helps to catch any potential breaches before they can do significant damage.

Overall, zero trust is a proactive approach to security that helps to prevent data breaches by continuously verifying the trustworthiness of users, devices, and systems and limiting access to sensitive data to only those who are authorized.

How Zero Trust Cybersecurity Model Works

A Zero Trust security model typically involves the implementation of several key strategies and technologies:

1. Identity and access management: This involves verifying the identity of users and devices before granting them access to network resources. This may involve the use of multi-factor authentication, single sign-on solutions, and access controls based on user roles and permissions.
2. Network segmentation: This involves dividing the network into smaller segments or “micro-perimeters,” each of which is secured and isolated from the others. This helps to prevent unauthorized access and can contain the impact of a cyber-attack.
3. Secure remote access: This involves implementing secure solutions for remote workers and devices to access network resources from outside the physical network perimeter. This may involve the use of virtual private networks (VPNs) and other secure remote access technologies.
4. Continuous monitoring: In a Zero Trust model, the identity and activity of users and devices are continuously monitored and reviewed. This helps to detect and prevent unauthorized access or activity and can also help to identify potential cyber threats.
5. Least privilege: This involves granting users and devices the minimum access necessary to perform their tasks, and continually reviewing and revoking access as needed. This helps to minimize the risk of unauthorized access and can help to prevent data breaches and other cyber-attacks.

By implementing these strategies and technologies, organizations can create a security model that is designed to continuously verify the identity and activity of users and devices, and grant access only to those that have been properly authenticated and authorized. This helps to protect against cyber threats and can help to prevent data breaches and other security incidents.

A Sybil attack is a cybersecurity threat to an online system where one person creates multiple fake identities or Sybil identities to influence and take over a P2P network. This article offers some details about Sybil attack risks and solutions.

Sybil Attack Risks to Companies

Online services have been known to have problems with Sybil attacks as the scam can sway public opinion, often leading to financial gain for the perpetrator. For example, in a Sybil attack, the perpetrator can send identical messages or post similar content in different forums.

In a Sybil attack, there is the risk of reputation and identity damage and a significant loss of customer trust. The attack can have many purposes. For example, a Sybil attack can be used to spread damaging rumors about a company. This type of attack is also very common in the crypto space. For example, in a blockchain Sybil attack, the perpetrator may create multiple fake accounts posing as real persons which will make it difficult to determine the actual number of users in a blockchain network. A Sybil attack can also be inflicted on a blockchain to make transactions using multiple accounts. The objective of a blockchain Sybil attack is to take advantage of an account with a high reputation score to pretend having a significant number of followers or amount of money. This type of attack would not be possible if the actual user account is not compromised because after fake accounts are created, the scammer must access the actual user’s account by stealing their email or password which makes it very important to maintain account security at all times with strong passwords and multi-factor authentication.

How To Detect Sybil Attacks

Companies should look for red flags in new accounts. The information in an email address, IP address, phone number, physical postal address, etc., can be noted and validated to identify a pattern of abuse. This type of monitoring for abuse is more likely to be successful when the company performs the monitoring proactively rather than after the abuse has been identified. Some services require the validation of a phone number or physical address before allowing the creation of a new account, which can further protect them from a Sybil attack.

Sybil Attack Risks and Solutions

Below is a list of Sybil attack risks and solutions that may be considered to prevent these attacks:

1. Whitelist Users

If a company allows comments to be posted on its website, the whitelist approach is beneficial for users to prevent a Sybil attack. It is an effective filtering method that prevents an attack from occurring through the identification of the IP address of each user as they log into the website.

2. Canvas Fingerprinting

It is a supplementary method that works with user-agent and IP address data by adding information about other sources outside the computer. It is used to detect the most active Sybil attackers. It needs to be foolproof to avoid false positives.

3. Use of CAPTCHAs

CAPTCHAs require that a user correctly answer a set of challenges, such as how to spell a word or what number is the favorite number of choices. They are often used to prevent spamming, but they are also among effective methods used to detect Sybil attacks.

4. Machine Learning

Machine learning and artificial intelligence is a great tool to detect and prevent Sybil attacks efficiently and effectively. As Sybil attacks commonly occur on social media websites that allow comments and postings, machine learning can help companies identify and block potential attackers in real time before posting any comments on their social media accounts or websites.

5. Banters

Banters are a form of attack detection method that tries to identify users who are likely to be creating malicious discussions on the forum or chatroom by monitoring the frequency of posts, user IDs, and IP addresses over time. It is not a foolproof method, as it can provide false positives. This detection method is often used in conjunction with other methods to reduce the number of false positives.

6. De-anonymization

Deanonymization is a relatively new solution that involves identifying an anonymous user by analyzing the network packet data between the client and server while interacting. It is not a foolproof method, and it comes with the risk of having false positives. Though rare, this can have adverse effects on a company’s reputation.

Conclusion

Sybil attacks significantly impact the company’s reputation and customers since they will give potential attackers access to their accounts, reputation, financial information and other sensitive data. Companies that are proactive in protecting themselves against these attacks can avoid the negative impact and consequences of such attacks. Companies can leverage the allowlist method to prevent any potential attackers from using the company’s platforms.

With the rising popularity of digital assets such as crypto and decentralized identity, users take advantage of digital wallets to store their identity data and digital assets, however, there are some crypto wallet security risks that we need to address.

In a blockchain based digital age, individuals no longer need to rely on traditional ways to identify themselves, access their accounts, or store digital assets. Instead, personal identifiers and digital assets like crypto and NFTs can be stored in crypto wallets.

What Exactly is a Crypto Wallet?

In the simplest terms, a crypto wallet is where an individual stores digital assets and private keys or passwords used to access cryptocurrency. These wallets are designed to protect, store, send, and receive digital assets and currency like Ethereum or Bitcoin. They come in different forms, from mobile applications to physical hardware that resembles a USB flash drive which are used for authentication or shopping online using cryptocurrency, which is as straightforward as a traditional credit card.

It’s important to remember that these crypto wallets don’t store actual crypto currency. The cryptocurrency is instead on the blockchain or digital ledger. The crypto wallet holds the private keys to access the digital currency on the blockchain and is an important security consideration. So essentially, a crypto wallet is the key to the vault and critical to accessing crypto assets.

What are Crypto Wallet Security Risks?

There are always savvy thieves working to find ways around security measures. Therefore, it’s essential to consider the various crypto wallet security risks. Wallet applications that are available on mobile devices and personal desktop computers connected to the internet are always accessible which are also called hot wallets. While convenient, some apps include functionality that may increase the risks of theft. For example, some wallet apps feature the ability to export keys. Remember, those are the same keys that grant access to digital assets on the blockchain.

Another issue with a mobile wallet is that private keys are stored within the application. So, software bugs or vulnerabilities within the app itself can become problematic. In fact, this isn’t just a hypothetical situation, as thieves have exploited or hacked into mobile wallet applications in the past.

Another form of the crypto wallet is web-based. Coinbase is a popular choice for this type of wallet, where you must navigate a secure login process to access private keys. However, the account can be tied to a mobile phone number, leaving users vulnerable if they misplace or lose their smartphone. Unfortunately, it would only take a skilled thief a few moments to transfer all crypto assets to another wallet.

Desktop wallets allow users to access their private keys right from the desktop. The thinking behind this crypto wallet is that the information remains on a personal computer. However, the data remains unsecured and susceptible to knowledgeable hackers unless the PC is encrypted.

How to Counteract the Risks

With all these risks, how do digital wallet users keep their digital assets safe? Fortunately, several ways exist to mitigate the risks and help ensure private keys remain private.

First, before choosing a crypto wallet, it’s crucial to verify whether or not it stores private keys in an encrypted form. This encryption is an extra layer of security that prevents private keys from falling into the wrong hands.

Secondly, users should add extra security measures to their smartphones. While a PIN seems like a solid security measure, someone could still lean over and view the code being entered. Instead, utilize a fingerprint authenticator that’s much more difficult to circumnavigate.

Another great theft deterrent is a strong password for web-based wallets. Resist the urge to reuse passwords or make them too simple for convenience sake. While it may be cumbersome to enter, a complex and strong password will protect users from hackers looking for easy targets.

Many crypto wallet options offer more robust security features like 2-factor authentication. It may be tempting to turn the feature off for easier login, but it adds another layer of security. Every hurdle put in front of a hacker makes you the least desirable target.

It’s also vital that should an individual’s account get compromised, a plan is in place to respond. Any unusual crypto wallet activity discovered should be immediately taken seriously a robust response. Once an account is breached, it could be minutes before a robust account balance is reduced to zero.

Lastly, another type of crypto wallet avoids many of the security concerns outlined above, and that’s a hardware wallet. These small physical devices look similar to USB memory drives and allow users to log in once plugged into a PC. A small screen also confirms transactions and requires a PIN to access hardware wallets. These wallets when not connected to the internet are called cold wallets.

Security Measure Considerations

• Private key encryption

• Smartphone fingerprint authentication

• Two-factor authentication

• Response plan in case of data breach

Keeping Private Keys Secure

While there are many options for crypto wallets to consider, it’s important to take extra security steps. The risks associated with digital wallets aren’t too significant to overcome with a bit of planning. Consider crypto wallets such as mobile, desktop, web-based, and hardware to determine the right option. Add extra layers of security where possible, and hackers will move on to easier prey. Mitigating the risks of using crypto wallets is simple but shouldn’t be taken lightly.