The basic tenets of AI-enabled programs are that they can collect data, analyze it, make decisions with an understanding of the results, and learn from the results. This is why the application of AI to cybersecurity brings new defensive promises and offensive cybersecurity challenges.

Cybersecurity  is central because the exponential growth of data (consumers and businesses) has made data breaches more prevalent. Some of the most common causes of data leaks are:

  • Weak or stolen credentials such as passwords
  • Malicious programs in the form of viruses, ransomware, phishing.
  • Social engineering
  • Insider threats
  • Incorrect IT system configuration and user error
  • Back doors through vulnerable applications and
  • Improper management of permissions

A growing number of attacks are driving the adoption of artificial intelligence in cybersecurity to ensure effective and accurate data protection. As expected, AI has given new opportunities to bad actors.

AI facilitates the creation of smart defenses and threats.

In the past, hackers were highly skilled programmers who could program their malware and navigate complex security protocols. This is no longer the case today; Malware can now be marketed as an intelligent plug-and-play solution. This leads hackers who are not computer experts to fight and ultimately increase the number of hackers.

Defending against such easy-to-use intelligent threats requires an intelligent solution. For example, using an AI-powered network monitoring tool, security vulnerabilities can be quickly identified by analyzing user behavior, recognizing patterns and detecting network violations and responding accordingly. It can detect, track and close more vectors of cyberattacks than is possible for humans.

Here’s how it works: AI models  will use large amounts of data from every application in the organization across all endpoints to develop a profile. This helps establish a baseline for behavior, so if there is a statistically significant deviation from the norm, the algorithm flags it for further study.

AI can also boost biometric authentication.

One of the main challenges for digital users is creating, remembering, and regularly changing strong passwords. This pain point has been used by hackers to penetrate and break into protected data. This loophole can be closed by biometric logins that use either fingerprint, retinal, or handprint scans. Biometric logins can be used alone or with a password to control and control access.

Automation now applies to malware. Instead of a direct attack by hackers, they can now use automated malware that works with minimal human intervention. Malware automation makes malware more frequent, complex and relentless.

Automated malware is a threat to IoT devices , and security breaches are expected to increase exponentially with increased use. IoT devices are of particular concern because manufacturers do not prioritize security when creating a product, and consumers rarely think about security when connecting devices. This has made IoT devices a prime target for Internet attack traffic.

Automation can save cybersecurity teams time and money. Cybersecurity teams perform many routine tasks that need to be automated. IT administrators are constantly inundated with recurring incidents, internal threats, and device management responsibilities that waste time on more important tasks. Automating these day-to-day tasks will not only free up human capital, but will also allow you to achieve results in less time and with greater accuracy.

Malware is usually a program with a tough purpose or protocol. Hackers can use AI in their programs to adapt and learn from every attack. AI-enabled malware can also mimic human or trusted elements of an IT system in order to gain access. This makes it easier to create polymorphic malware with obfuscation functions.

A key asset in malware detection is virus definitions or databases that contain malware identifiers and patterns to help identify threats. Attackers can use machine learning to evade detection, but IT pros can also use it to quickly identify risks.

Cybercriminals usually tweak their malicious code to gain access to security software. It is difficult to identify every type of deliberately disguised malware. A machine learning malware database can detect malware, whether existing or modified malware, and the system can block them based on previous events that were thought to be malware.

Identifying ever-evolving threats is easier with AI. AI systems can be trained to detect ransomware and malware attacks before they enter the system Once detected, they can be isolated from the system. AI predictive capabilities outperform traditional approaches.

The use of machine learning in cybersecurity can bring benefits such as:

  • Monitoring and analysis of multiple endpoints for cyber threats
  • Detecting malicious activity before it manifests itself in a full-fledged attack
  • Automation of routine security tasks
  • Eliminate zero-day vulnerabilities

AI-enabled cybersecurity is essential.

The Capgemini Research Institute found that two-thirds (69%) of organizations admit that they cannot respond to critical threats without AI. More than half (56%) of CEOs say their cybersecurity analysts are overwhelmed by the sheer volume of data points they need to track to detect and prevent intrusions. In addition, there has been a significant increase in the type of cyber attacks that require immediate intervention or that cannot be quickly eliminated by cyber analysts, including:

  • Cyberattacks affect time-sensitive applications (42% say they have grown by an average of 16%).
  • Automated attacks at the speed of a machine that mutate at a rate that cannot be neutralized with traditional response systems (43% reported an increase, on average 15%).

AI is already being applied to cybersecurity. Some of the AI ​​cybersecurity applications in use today include:

  • Application spam filter
  • Network Intrusion Detection and Prevention
  • Fraud detection
  • Botnet detection 
  • Secure user authentication
  • Hacking Incident Prediction

While an organization’s security system can be secure because it interacts with third parties (customers, regulators, suppliers, etc.), it is vulnerable through these paths. According to Accenture, 40% of security breaches are indirect, as threat actors target weak links in the supply chain or business ecosystem. This is why organizations need an automated, intelligent solution that can predict attacks and respond quickly.