Is it possible to predict data breaches?
Let’s face it, cybersecurity specialists are facing volumes of threats with a high record of attacks. In fact, according to Statista, 53 million individuals had their data breached, leaked, or exposed in the first half of 2022.
At the same time, cyber-attacks are growing more complex daily, making it harder for us to avoid the attacks.
And we now have to work harder than before to safeguard our assets and clients. This goes past automating reactive measures. We are required to work toward proactive detection to avoid attacks or thwart threats.
This is where artificial intelligence comes in. AI will help you:
• Handle more complex threats
• Avoid more complex attacks
• Reduce the cost of resources by cutting human labor
The use of artificial intelligence is widespread across cyber security.
So today, I’ll take you through 3 main applications of artificial intelligence in cybersecurity in 2022.
Let's go.
1. Predictive Analytics
Security should be at the forefront of every organization.Today, attacks are intelligent and organizations are vulnerable to modern cyberattacks and are losing sensitive data. Handling cyber threats manually is no longer a suitable option.
As a cybersecurity professional, your work involves detecting threats and thwarting them to ensure that the network system is running smoothly.
The problem is, you can’t predict future threats or loopholes which may affect your system. In fact, EnterpriseAppsToday reports that nearly 76% of enterprises experienced phishing attacks in the past year.
It goes without saying, cyber security measures need to be more complex and state-of-the-art.
So how can predictive analytics help?
Predictive analytics uses algorithms to find patterns in your network system to predict future threats using your historical data.
Additionally, using historical data combined with statistical modeling, data mining techniques, and machine learning, predictive analytics can predict the future by going through your transactional database, equipment log files, sensors, and other data sources.
You can later use your predictive analytics findings and perspective analytics to avoid any possible cyber threat.
By introducing predictive analytics in your system, you can catch potential threats and loopholes while working against the norm of only patching up holes in the system after a cyberattack.
Korean SMEs wanted to predict future aspects of cybersecurity incidents, more specifically on how enterprises can gain the ability to predict response action to future cybersecurity threats. They used a model developed using text mining methods.
Using machine learning algorithms their result was 81% accurate in predicting different types of responses and malware.
2. Threat Detection
Cyber threats are a critical issue in today’s economy.Hackers are advancing daily, following the latest trends just like everyone else. Evidently, as cyber security engineers, we are overwhelmed by the volume of threats received daily.
In fact, a report given by Criticalstart states that organizations receive 5,000 alerts daily. You should also take into consideration that in a single shift, a cybersecurity engineer can examine, on average, 18 threats only.
Obviously, you will spend more resources trying to battle the daily volume of threats your organization faces. Sadly, while you are trying to battle the alerts, cybercriminals are a step closer to attacking your enterprise.
Fortunately, machine learning and AI can help your teams manage their daily workload by detecting, preventing, and monitoring threats.
With the use of complex algorithms, AI analyzes relationships between threats, runs pattern recognition, discovers even the smallest threat, and thwarts it before it can bring any harm to your system.
AI in cybersecurity also provides information on new attacks, anomalies, and possible prevention methods.
In addition, with the use of machine learning, organizations can cut the cost of resources while reducing the time that security analysts take to make critical decisions.
Sogeti Luxembourg, a team of security professionals, was facing an increase in threats to their data since they were handling a “sea of information”.
With the help of cognitive security from IBM, they managed to reduce threat investigation from 3 hours to 3 minutes. They also improved their accuracy in detecting threats.
3. Endpoint Security
Data theft would cost you massive data loss.Considering that endpoints are frequently the weakest link in the security chain, first-generation antivirus solutions can’t handle modern sophisticated threats and attacks.
Concurrently, the growing number of endpoints has made it even harder to avoid cyber attacks. According to research carried out by Ponemon Institute, the attacks on endpoints increased to 68%. The same research shows that the attacks are also difficult to detect.
With the increase of endpoints, maintenance and updating blacklists of malicious codes demand more resources and more labor.
So what’s the solution?
Well, AI delivers endpoint detection and response which can continuously monitor networks and systems while also mitigating advanced threats.
Beyond that, machine learning is able to detect any unauthorized behavior of users and block suspicious actions in your system. Also, advanced endpoint security comes with actionable threat forensics, which allows you to isolate any infections in your system.
With tight endpoint security, you will have proactive web security that ensures safe browsing on the internet, while integrating your firewall to block hostile network attacks.
In addition, machine learning can also detect zero-day threats in real time, making it easy to thwart them.
Hitachi Consulting, a company that helps organizations manage digital transformation projects, was having problems protecting against all threats across all remote endpoints.
They incorporated the SentinelOne Endpoint Protection Platform and as a result, they gained real-time visibility into endpoints. They also gained protection against all malicious threats like malware and zero-day exploits.
Conclusion
It’s no secret that AI has made it easier to tackle data breaches.As an organization that needs to continually provide quality services, you should incorporate these 3 main applications of artificial intelligence in cyber security in 2022 in your enterprise.
These applications provide the much-needed analysis and threat prediction, which will help improve your efficiencies while increasing your data privacy. Concurrently, AI can instantly identify malware on a network and prioritize risks. This makes it easy for you to act fast on the threats.
I suggest you get an AI-powered antivirus to help you predict future threats and keep all your sensitive data safe.
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