How AI Enhances Data Encryption How AI Enhances Data Encryption
Data encryption is an essential part of modern cybersecurity. It renders data unreadable to unauthorized users, ensuring sensitive information stays private,... How AI Enhances Data Encryption

Data encryption is an essential part of modern cybersecurity. It renders data unreadable to unauthorized users, ensuring sensitive information stays private, even if breached. Still, most encryption practices are imperfect, but artificial intelligence is improving things.

As AI becomes more reliable and accessible, security professionals use it to improve their encryption. AI could reshape encryption practices as a whole as this trend continues.

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Benefits of AI Data Encryption

AI technology is already widespread in the cybersecurity industry. While threat and fraud detection remain the most common uses for AI in security, it also has many advantages for data encryption.

Automated Encryption

The most straightforward benefit of AI-powered data encryption is it saves time and prevents human error. Conventional practices require choosing from dozens of encryption algorithms to find the best fit for specific data and applying it manually.  

Repetitive processes and complex decision-making are AI’s strong suit. Machine learning models can determine the best encryption method by classifying data in minimal time and applying it automatically.

This automation gives security professionals more time to focus on urgent tasks like responding to possible threats. It also minimizes errors like misconfiguration or using a non-compliant encryption standard in heavily regulated industries. As cybersecurity skills gaps and regulations increase, those advantages become increasingly valuable.

Adaptive Encryption

AI also enables adaptive encryption to stay safe and adapt. One of the most persistent challenges in cybersecurity is it is continually evolving. Data breaches have increased by 15% worldwide — amounting to $4.45 million per event — as cybercriminals develop new techniques to get around existing defenses.  

Machine learning models can analyze threat trends to determine when encryption methods are no longer safe. They then apply different standards or develop novel strategies to adapt to new threats.

Conventional, static encryption algorithms have a limited life span. Eventually, criminals will find a way to break them. Without AI, businesses must accept the risk or over-apply advanced methods, which can be time-consuming. Adaptive, AI-driven encryption addresses both sides of the issue.


Quantum Cryptography

A newer but equally promising use case for AI in data encryption is developing quantum-resistant cryptography standards. Quantum computers instantly make calculations — which would take conventional machines 47 years — posing a massive threat to current encryption methods.

Conventional encryption methods do not work against quantum computers because the time it takes to decode text is no longer a significant barrier. Businesses will need new ways to encode data without relying on predictable patterns or mathematical equations. Since AI is excellent at complex problem-solving, it can highlight potential alternatives.

AI could even employ these new, quantum-resistant standards automatically. Pairing this technology with adaptive encryption would unlock further benefits, letting businesses frequently shift their cryptography to stay safe against evolving threats.

AI Risks to Encryption 

As impressive as these benefits are, it is essential to recognize cybercriminals can also use AI. In fact, 85% of security professionals believe recent cybersecurity spikes stem from generative AI. Just as AI can improve encryption, it can help the bad guys find ways around it.

Cybercriminals use AI to analyze encrypted data and recognize patterns to determine how to decrypt it. In this case, AI-driven encryption would only be reliable if the security provider’s encryption AI is more advanced than what the attackers use. This could lead to a constant back-and-forth of developing and tweaking AI encryption models.

Alternatively, cybercriminals could limit AI encryption’s reliability through data poisoning attacks. Researchers have found attackers can manipulate data sets for as little as $60 to affect the accuracy or behavior of the end model. These attacks could cause AI to fail to encrypt data effectively, leaving companies vulnerable if they do not recognize these weaknesses first.

AI Will Shape the Future of Data Encryption

While AI is not a cure-all for current encryption problems, it is a significant step forward. As both cybersecurity professionals and criminals take advantage of this technology, AI will change encryption one way or another.

Threats are growing too quickly not to take advantage of the latest technologies. While no tool is perfect, AI enables a much higher level of security. Applying it to encryption practices today can help businesses avoid tomorrow’s threats.

Zac Amos

Zac is the Features Editor at ReHack, where he covers data science, cybersecurity, and machine learning. Follow him on Twitter or LinkedIn for more of his work.