Use of AI in Cybersecurity in 2023

Highlights:

• Research revealed that businesses that use AI as a part of strategy are emphasizing a broader view of their digital landscapes.
• The rapid growth and adoption of AI in cybersecurity market is due to the growing contextual integration of IOAs.

Machine Learning (ML) and Artificial Intelligence (AI) are becoming the preferred choice of scammers. These tools are increasingly used for various stealth purposes, such as generating personalized phishing mails and creating malicious systems to breach the protection. The most recent multiyear breach featured certain instances of AI-powered cyberattacks.

Use of AI to Skip Detection

Advanced Persistent Threat (APT) groups and cybercriminals involve ML and AI experts to create malware to escape threat detection systems. Businesses are recommended to be vigilant always because scammers might remotely hover over the organization for several months to plan an attack and disable the systems.

Another concerning factor is disclosing new susceptibilities and the rate at which these cyber hazards can harness ML and AI for stealth operations.

Hackers and scammers use AI tools to re-configure malware, customize phishing links, and restructure algorithms to breach systems and access credentials.

Experts have observed that hackers are advancing at handling AI tools such as ChatGPT for unethical purposes. Cyber pros, on the other hand, are also engaged in exploring the optimum utility of AI for security purposes. Let’s wait for time to tell who’s going to be effective.

A recent survey revealed that a considerable number of IT policymakers predict a feasible cyberattack within a year under ChatGPT’s credit.

Developer’s AI Race

Multiple cybersecurity vendors such as CrowdStrike, Google, AWS, IBM, Palo Alto Networks, Microsoft, and others are spending on the R and amp;D of ML and AI to stay vigilant against cyber threats in response to the new features required by enterprises.

In ML, it is necessary to keep the system constantly working without interruption. Besides, the data, model training, and other relevant stuff must be prioritized. Reportedly, Microsoft has immense technology in the AI space.

Certain prominent companies’ DevOps and engineering expertise have effectively transformed R and amp;D efforts into new AI products. For instance, the zero-trust development by Microsoft Azure and many cybersecurity services by AWS proved that these cloud providers have been prioritizing R and amp;D expenditure on ML and AI.

Core Areas of Enhancing Cybersecurity Using AI in the Future

APT groups and cybercriminals increasingly use AI hacker tools to create a threat, making organizations’ security teams lose in the AI race. Such troublesome factors lead to some crucial forecasts about AI and allied investments, as follows:

1) Behavioral analytics can spot and restrict malicious activities

The zero-trust frameworks assist in real-time monitoring and visibility over a network. AI-powered behavioral analytics offers real-time insights about malicious tasks by recognizing discrepancies and acting on them. It helps IT teams to distinguish between the existing and previous behavior patterns and accordingly discard the inconsistencies. Various parameters, such as log-in attempts, configuration, and device type, are evaluated to spot glitches and real-time threats. Broadcom, CyberArk, Blackberry Persona, and Ivanti are among the leading service providers.

A behavioral analytics approach to AI-powered systems’ management prevents the app from cloning and device, protects against user impersonation, and lowers the theft risk. With behavioral analysis techniques, companies can assess endpoint detection and response (EDR), endpoint protection platform (EPP), transaction frauds, and unified endpoint management (UEM).

2) Asset management and endpoint discovery:

Research revealed that businesses that use AI as a strategy emphasize a broader view of their digital landscapes. According to IBM, almost 35% of enterprises deploy automation and AI to explore endpoints and enhance asset management.

The second most well-known use case, patch management and vulnerability are estimated to increase adoption in the coming years. As per research, the large-scale adoption of AI will help enterprises achieve zero-trust initiatives.

3) Use of AI for vulnerability and patch management:

It has been observed that a large number of security and IT personnel found patching complex and time-consuming. Moreover, several other organizations opined that coordinating crucial vulnerabilities consumes most of the time.

Sometimes, even well-equipped and adequately funded IT teams find challenges in patching. Businesses should deploy a risk-managing patch management solution and use automation to recognize and address susceptibilities without additional manual efforts.

4) Threat detection using AI:

Transaction fraud detection is the common use case that delivers high business value. Besides, file-based malware detection, process behavioral analysis, and abnormal system behavior detection also come with better feasibility and increased business value.

Organizations can deploy these solutions to spot and discard potential system threats.

5) Significance of AI-based indicators of attacks (IOAs):

AI’s rapid growth and adoption in the cybersecurity market are due to the growing contextual integration of IOAs. An IOA detects and evaluates the intent of attackers, irrespective of the malware or hacking tool used for the attack. It must be regulated to provide real-time and accurate data about breaches or attacks to apprehend the scammer’s intent and prevent possible intrusion.

IOAs strengthen existing defenses with the cloud-based ML and real-time threat intelligence to assess runtime events and generate IOAs to the sensor that links AI-based IOAs with local files to check maliciousness.

Bottom line

Threat detection has been dominating AI use cases. AI is found to deliver its optimum potential when integrated into a zero-trust security framework that treats all identities as a security perimeter.

A distinct idea of what the technology and solution protect leads to the utmost reliable use cases of ML and AI in cybersecurity. AI and ML-backed technologies effectively secure the use cases, be it an access credential, device, container, or client’s system. Chief Information Security Officers (CISOs) and leading organizations are becoming cyber-resilient by adopting AI-based security strategies. Besides, the C-suite in most organizations anticipates that cyber security management must be assessed financially, for which AI-based assistance comes into the picture.