The battle between data defenders and data hackers can be described as a never-ending game of chase. As soon as a pattern or malicious behaviour is detected from the white hats, a new, malevolent form of hack shows up. Hackers become increasingly innovative with the technologies they use to access sensitive data. In many cases, new technologies that enter the market become a great potential for hackers, who use people’s lack of understanding the platforms and their privacy preferences. They’ve perfectioned the concept of finding undiscovered holes in new security systems. They’re also leveraging certain technologies that are being exploited for harmful purposes.
How can the battlefield be titled in favour of the good guys? Here are several emerging technologies that might be able to do that.
Hardware authentication
We are aware of the inadequacies of usernames and passwords. Companies and people also feel the need for a more secure form of authentication. Hardware authentication is an approach to user identification that relies on a dedicated physical device (tokens) held by an authorised user. The device generates a unique and temporary cryptographic code that has to be typed by the user in addition to the password. This way you will gain access to a computer resource. This is not a new approach to the market but certainly is effective and evolving in its expertise. Compared to software-based or SMS based two-factor authentication are more effective and represent less hassle. You can these keys in a variety of formats from USB-A and USB-C and even keys that use Bluetooth.
User behaviour analytics
When someone’s username and password are compromised, whoever has them can connect and involve in different malicious behaviour. This type of behaviour can trigger a red flag to system defenders if they’re familiar with user behaviour analytics. This technology uses big data analytics to identify suspicious behaviour. That said, user activity is the number one concern of data security professionals. A close up in the activity that doesn’t fit in the norm of the user can close a blind spot in the middle of the attack chain.
The technology behind user behaviour analytics is successfully implemented in the gaming industry. Providers can easily identify what type of games their audience prefers, their playing time and retention according to their user journey. For example, if a player shows an increased activity on playing themed online slots like Beetlejuice Megaways free play game, the platform can translate players’ preferences to numbers and ratios readable for the Customer Management department. This way, they’ll know whether their campaigns are successful or not, so they can further improve or change them.
Comparing the user’s present to past behaviour is not the only way UBA can identify an attack, as peer analyses are also one of the best practices. These analyses show how someone is behaving compared to accounts that have the same traits. This can indicate that a person is doing something unusual or that someone has taken over their account. In addition, this technology can identify gaps between employees and company policies. This way it helps to identify people and mitigate risks by proper employee training for the policies they violated.
Data loss prevention
The key to data loss prevention is deploying technology like encryption. This can further protect data to multi-layered levels where companies will benefit in a number of ways:
- Cyber-attackers can’t monetise data when a breach has happened.
- Data can be moved and further used across the company
- The company can benefit from compliance and security regulations for protection of payment cards, personally identifiable information and protected health information
We mustn’t forget that there can’t be a strong encryption process without key management and vice versa. The relationship between the data controllers and the system itself is crucial for a successful prevention process.
Deep learning
Deep learning is accompanied by a number of technologies such as AI and Machine Learning. Regardless of the name behind the process, there is a great interest for it in terms of security purposes. Similar to user behaviour analytics, deep learning, in this case, puts focus on malicious behaviour and the background behind it. Instead of looking at users, this system looks at entities. Recent developments in machine learning can dive deep into various entities that exist across a department on micro and macro levels. With its ability to differentiate good from bad at line speed, industries have big plans for this technology. However, many industries enter the market with the same approach for this challenge. In the future, it would be more difficult to differentiate online behaviour and recognise unique, safe patterns between clients and accounts.
Conclusion
While these technologies represent a shield from frequent data breaches and attacks, they are also a challenge. Predictions are that data thieves will strive forward and continuously try to mimic and camouflage their presence using the same tools as the data controllers. The best approach to this matter is to keep an open eye and be alert on the latest trends. Being up to date, compliant and aware that data breaches can happen to anyone might prevent possible malicious entries.
