The Internet of Things and artificial intelligence have brought myriad benefits to businesses in almost every industry. From streamlined supply chains to manufacturing processes, AI and IoT offer solutions for nearly any organizational need. 

However, these technologies also present new cybersecurity risks that weren’t present before because so many devices are connected to the internet now. The risk of cyber theft is one of the organizations’ primary concerns about implementing AI and IoT solutions.

 These technologies introduce more devices that hackers can access remotely and software that can be manipulated to steal information or money, either directly or indirectly. 

We’ll explore some of the threats posed by AI and machine learning, along with five strategies you can use to mitigate your organization’s risk of cyber theft using these technologies. So let’s get started!

1. Forecast future attacks

When you can forecast future attacks, you can also prevent them. When you can predict the subsequent potential attacks, you can arm your security team with enough information to defend against them. There are three ways you can forecast potential attacks using AI and ML: 

• Network traffic analysis: This type of analysis examines the communication between devices on a network. It can help you detect malicious communication between devices that may indicate an impending attack. 
• User and behavioral analysis: This type of analysis looks at what users are doing and how they interact with systems. It can help you detect abnormal user behavior that may indicate an impending attack. 
• Network behavior analysis: This type of analysis monitors the devices and traffic on a network and the state of the network devices and software. Network behavior analysis can help you detect impending attacks by monitoring the state of devices on a network.

2. Implement biometric authentication

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Biometric authentication is a type of identification that uses a person’s physical traits, such as their fingerprint, face, or voice, for identification and verification purposes. In most cases, biometric authenticating uses sensors to collect data, like fingerprints or retinal scans, and then uses software to create a unique identifier. 

Biometric authentication is a great way to prevent cyber theft as it doesn’t require using a token or PIN, which can be forgotten or stolen. However, biometric authentication isn’t foolproof and can be breached by an attacker using a fake fingerprint or voice impersonation.

3. AI-powered real-time detection and response

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Real-time detection and response can detect an attack in real-time and respond with automatic remediation. You can use real-time detection and response to prevent cyber theft in three ways. 

•  Early anomaly detection: An early anomaly detection is an analytics-driven approach to identifying anomalous events before they become security threats. Early anomaly detection can help you prevent cyber theft by identifying network behavior that indicates a potential attack. 
• Real-time threat analysis: This approach relies on security information and event management (SIEM) platforms to identify threats. Real-time threat analysis can help you prevent cyber theft by identifying threats on the network. 
• Real-time data discovery: This approach relies on data mining, machine learning, and graph analytics to help you discover patterns in data that may indicate a cyber attack. 

4. AI Massive data scanning

Massive data scanning is a method of analyzing huge volumes of data to identify potential threats. To prevent cyber theft, you can use AI massive data scanning to look for malicious files on your systems, such as viruses or ransomware.

 You can use data scanning to look for suspicious code, such as a piece of code that might be part of a botnet or for systems communicating with a suspicious domain. Data scanning can also look for sensitive data on your systems, such as unencrypted credit card numbers.

 If you find malicious or sensitive data, you can quarantine it or remove it from your systems to prevent further damage.

5. Secure your data using blockchain-based platforms

A blockchain is a distributed ledger that records transactions and assets, such as contracts, immutable and auditable. Blockchain technology is often associated with cryptocurrencies, such as Bitcoin and Ethereum, that use the technology to track and secure their transactions. 

It is also used in other industries, such as healthcare and aviation. Blockchain technology provides a robust and secure way to store and share data. This has led to the development of AI-driven machine learning platforms that use blockchain technology to store and share data between users securely.

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 These AI-driven platforms use a decentralized network to distribute data across multiple nodes to ensure that the data is secure and not stored on one central server. The data is also encrypted to protect against malicious attacks.

6. Machine learning-based vulnerability scanner

A machine learning-based vulnerability scanner is an AI-driven security tool that analyzes your systems for vulnerabilities, such as unsecured network ports or a weak password policy, and recommends corrective actions.

 A vulnerability scanner is critical to any security strategy as it helps you identify and remediate issues before they become a security threat. A vulnerability scanner can prevent cyber theft by identifying vulnerabilities in your systems and providing recommendations on how to remediate them. 

It can also be used to identify if your data is at risk, and if it is, it can recommend ways to secure it. Vulnerability scanners can also identify threats in your data, such as malicious URLs. If a vulnerability scanner finds threats in your data, it can provide recommendations for remediating them. For example, it can provide a rule to block malicious URLs from entering your network.

Conclusion

There’s no denying that cybercrime is on the rise. The number of attacks and the scale of the damage they cause continue escalating as more businesses adopt the technology.  However, AI and machine learning are helping combat this threat by detecting anomalous behavior patterns before it becomes a security threat.