Today, the act of keeping information and knowledge safe has become incredibly difficult. The level of cybersecurity threats has grown and is far superior to what they were a couple of years ago. As a result, cybersecurity experts need to combat such threats on a daily basis. They need to assess new risks, establish potential prevention measures and find a solution for residual threats.

The new age of cybersecurity threats needs agile and effective initiatives that are able to adapt and modify to new and evolving attacks. Cybersecurity experts believe that AI and machine learning’s potential to tackle this problem is the key to solving these issues.

Within cybersecurity, AI programs are usually used for three types of tasks:

1 – Discover cyber threats (quantity)

2 – Change the run of the mill character of those threats (high quality)

3 – Current, new and obscure risks (quantity and high quality)

AI can be used to highlight the data sets which are most at risk from a cyber attack and quickly act to set up preventive measures to keep them secure.

Here are a few benefits of predictive AI in cybersecurity:

Predictive Protection
Using a mixture of cyber risk detection and defensive strategies, AI will implement predictive techniques to identify Intrusion Detection Systems (IDS) which target illegal activity on a network or computer or detect spam or phishing attempts using two-step authentication systems. This approach of guarded strategic AI can also be utilized for automated vulnerability testing, sometimes referred to as fuzzing.

Another emerging application of AI in cybersecurity is within communication and social media. It can enhance bots and social media bots by creating safeguards against anything related to manipulated digital content and manufactured or fake media which consists of video, audio, images or hyper-realistic texts are hard to classify as fake, through conventional forensic techniques.

Network Detection and Response(NDR)
To safeguard global networks, security teams detect anomalies in data flow using NDR. Viral code introduced into vulnerable systems gives cybercriminals the ability to transfer massive data sets. As cybersecurity advances, so do the efforts of cybercriminals. To constantly stay ahead and avoid new and innovative hacks and breaches, cybersecurity teams and their investigation methods need to be constantly adapting to new threats and researching new ways of being secure.

Risk Identification
Identifying risk is one of the fundamental elements of implementing predictive AI in cybersecurity. AI’s capacity to process vast amounts of data can detect threats from multiple channels like malevolent programming, suspicious IP addresses or virus files.

Cyber attacks can be predicted by detecting threats using security analytics which uses data to run predictive analyses of when and how the cyber attack will occur. Simultaneously, network action can be analyzed by comparing data samples using predictive analytic algorithms. Essentially, AI frameworks in cybersecurity have the ability to predict and assess a threat before it even occurs.

Cybercrime Prevention
The best way to keep a company safe is to caution clients before attacks occur. Hackers exploit obscure vulnerabilities in real-time.

One of the best methods of keeping a company safe is to warn clients regarding any potential attacks. Hackers initiate zero-day attacks that exploit obscure vulnerabilities in real-time. Usually, first wave and second wave network security tolls are defenseless against such attacks.

On the third wave, however, unsupervised AI can detect and deal with zero-day attacks in real-time without letting it cause any harm. It also allows the company to retaliate by:

• Artificial intelligence-driven alarms on known vulnerabilities

• Top tier threat chasing tooling

• IP addresses of programmers before they attack.