Data’s value is something of a double-edged sword. On one hand, digital data lays the groundwork for powerful AI applications, many of which could change the world for the better. Conversely, storing so many details on people creates huge privacy risks. Synthetic data provides a possible solution.
What Is Synthetic Data?
Synthetic data is a subset of anonymized data – data that doesn’t reveal any real-world details. More specifically, it refers to information that looks and acts like real-world data but has no ties to actual people, places or events. In short, it’s fake data that can produce real results.
In many cases, synthetic data is the product of machine learning. Intelligent models analyze a real-world data set to learn what real data looks like and how it behaves. They then produce new data sets that serve the same purpose but don’t reflect anything in the real world.
5 Uses for Synthetic Data in Cybersecurity
Synthetic data has gained popularity in finance and medical fields, but it has extensive applications in cybersecurity, too. Here are five of the most promising security use cases for this anonymized data.
1. Machine Learning
The most common application of synthetic data lies in training AI models. Machine learning plays many roles in cybersecurity, from behavioral biometrics to phishing prevention, but training these models on real data can expose personally identifiable information (PII) to breaches. Using synthetic data instead eliminates that concern.
In some cases, machine learning models trained on synthetic data are even more accurate than those using real-world information. That’s partly because synthetic data has fewer consistency- and error-related problems and partly because it’s easy to generate more of it for a larger sample size.
These benefits make AI-enabled security tools more accessible and reliable without sacrificing people’s privacy. It won’t matter if a hacker breaches these training data sets because they won’t gain any PII from them.
2. Security Testing and Training
Synthetic data is also a useful tool for vulnerability testing and employee security training. These tests are an important part of preventing the millions of dollars in losses phishing attacks cause, but conventional methods are risky. Businesses may accidentally expose real PII to attackers when testing for holes or running phishing simulations.
Swapping PII for synthetic data means security researchers can run these tests without risking breaches of privacy. They may replicate their company network using dummy data for safer penetration testing. Alternatively, they could test a phishing prevention system with fake profiles instead of real employee details. Whatever the specifics, synthetic data has the same benefits without the same hazards.
3. Intrusion Detection
Similarly, cybersecurity professionals can use synthetic data for perimeter security. One way to do so is to craft honeypots to lure cybercriminals away from real, sensitive data and systems. Hackers may target these distractions because they resemble real-world data, but as soon as they do, security workers will recognize the breach.
This approach helps preserve IT resources by driving attackers to a few continuously monitored points instead of having to watch the entire network. This resource efficiency is important because tight budgets and staffing problems are two of the three most-cited challenges to thorough cybersecurity.
Luring criminals to a specific area makes it easier to spot and contain breaches before they cause much damage. While that’s possible with real-world data, it would put sensitive information at risk. Synthetic data is a much safer alternative.
4. Password Protection
Synthetic data can also play a critical role in protecting passwords. Many businesses use password managers to defend against the brute force attacks behind 89% of hacking incidents today. However, even these systems are imperfect, as hackers can crack the encrypted passwords in these databases through further brute force attacks.
One solution is to use both hashing and salting. Hashing refers to the encryption of passwords in storage. Salting is the practice of adding random synthetic data to the hashing process. These extra figures make it extremely difficult to crack a hashed password, as much of the information doesn’t correlate to real credentials.
5. Biometric Authentication
Passwords aren’t the only authentication measure to benefit from synthetic data. These dummy data sets can also make biometric authentication algorithms more reliable.
While more secure than passwords, biometric authentication – especially facial recognition – has a bias problem. Several studies have found that they’re less accurate for people of color, largely because these models are mostly trained on white male faces. Training them on a more diverse data set could address that issue, but it could also introduce significant privacy concerns.
Deep learning models can create synthetic deepfake images that look like real people but aren’t. Training biometric algorithms on these fakes would make them more reliable for more people without potentially exposing anyone’s biometric data.
Synthetic Data Is an Important Security Tool
Synthetic data may not be a perfect solution for every problem, but its potential is impressive. These five use cases highlight how it can make the cybersecurity industry safer and more accurate.
As the models that generate synthetic data improve, so will these applications. Pursuing this technology now could ensure a safer tomorrow.
The post The Role of Synthetic Data in Cybersecurity appeared first on Datafloq.
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