As cyber crimes become increasingly more sophisticated, it is important for businesses to adopt new ways to tackle them and defend themselves effectively. According to research, there have already been over 35 million cyber attacks in 2022, a more than 3 million increase from last year! That is why it is so important to leave behind a legacy, rules-based defense — and adopt cutting-edge tactics like Artificial Intelligence for cybersecurity.

What is Artificial Intelligence in Cybersecurity?

Artificial intelligence in cybersecurity refers to the use of cognitive technologies to detect and protect against attacks on digital systems, usually through advanced data models that can consume billions of data artifacts to find anomalies or signs of a compromise.

In recent years, the use of AI for this purpose has become increasingly more common, with an IBM study noting that 64% of businesses have already implemented AI-based security. Just 7% of respondents said that they are not considering the use of AI! The popularity of this technology can be attributed to its ability to:

• Triage tier 1 threat (67% of AI adopters use it for this reason)
• Detect zero-day attacks (66%)
• Predict future threats and reduce false positives (65% for both)
• Correlate user behavior with that signals (61%)

AI cybersecurity tools may also use machine learning (ML) to become increasingly more accurate over time, and deep learning to operate in an unsupervised manner.

Why Do We Need Artificial Intelligence in Cybersecurity?

There are several cybersecurity challenges that companies face today, which make AI adoption so important. They are:

1. Geographically distant IT systems

Large organizations always faced the cybersecurity challenges of geographically dispersed IT teams, and this has become all the more prevalent due to the COVID-19 pandemic. Today, SIEM managers, network administrators, cybersecurity analysts, threat-hunting consultants, penetration testers, and other security stakeholders might be sitting in different locations around the world. This makes collaboration difficult and can slow down the analysis of threats.

2. Manual threat hunting

Some companies continue to depend on manual threat hunting, despite the availability of automation and artificial intelligence in cybersecurity. This could be due to limited internal knowledge, which causes a company to hire third-party manual resources, or, existing security data may not be suitable for automated analysis. Either way, relying solely on manual efforts slows down threat hunting and also limits its coverage.

3. The reactive nature of cybersecurity

Reactive security measures kick into action only after a compromise has happened. For example, a company may conduct root cause analysis after a data breach to identify the insider threat who caused it — but that will not undo the damage. Instead, artificial intelligence in cybersecurity enables “always on” detection that can find and flag the smallest anomaly. This prevents vulnerabilities from snowballing into severe threats.

4. Hackers changing/hiding their IP address

Skilled cybercriminals can disguise themselves by masking their IP address, which makes it very difficult to pinpoint the exact location of the attack. They could use a proxy, a rerouted network system, a VPN, or even a different Mac address when exploiting enterprise systems. That is why it is necessary to use tools like artificial intelligence in cybersecurity that considers behavioral patterns and not just machine data.

8 Ways AI Improves Cybersecurity

To address these challenges and stay a step ahead of cybercriminals, businesses need proactive, powerful, and “all-seeing” cybersecurity systems that do not rely only on human intervention. This can be achieved by AI-driven security tools, which improve cyber defenses in the following ways:

1. Artificial intelligence can identify unknown threats

Traditional, rules-based security must be told what to look for and can operate only based on these fixed models. AI, on the other hand, forms a baseline of usual/accepted behavior by regularly monitoring digital systems and comparing suspicious activity against them. It can also ingest thousands of external data feeds, to protect against unknown or zero-day threats.

2. AI for vulnerability management

Security vulnerabilities are weaknesses that exist in IT systems, due to flawed software, incorrect network configuration, human negligence, or simply due to the natural aging of systems. AI can help conduct automated vulnerability assessments and even prioritize them based on their risk levels (through predictive analytics) for intelligent remediation.

3. AI can manage more data

Artificial intelligence in cybersecurity can ingest much more information than possible by a human being. “In our own experience, adopting AI algorithms to enhance the protection of tens of millions of homes was a resounding success,” said Santeri Kangas, CTO of Cujo AI, a company that provides highly scalable security solutions to consumers and businesses. “AI helps us prevent around 10,000 threats every minute,” he added, a feat that is impossible for human analysts.

4. AI for network security

AI is very effective at addressing the cybersecurity challenges around enterprise networks. It can assess the entire network perimeter, and look for anomalous signals. As the network scales, the data models grow along with it, and AI solutions can even be implemented right at the router level — to protect networked devices that cannot run antivirus software (e.g., IoT).

5. AI learns more over time

Another benefit of artificial intelligence in cybersecurity is that it is self-learning in nature. ML technology allows AI data models to learn from exceptions — for example, if an anomaly is found to be a false positive more than once, it will factor this lesson in when making future analyses. Also, AI can study highly specific data sets and not rely on generic threat feeds.

6. The reduction of duplicate processes

Artificial intelligence also helps to minimize process duplication and bring in a degree of consolidation to security ops. This means that enterprises can spend less time, effort, and resources on performing the same tasks, such as running a routine cybersecurity check across the company’s networked devices. Instead, they can focus on strategic analysis and research.

7. AI accelerates detection and response times

AI can monitor almost 100% of network communications and nearly every endpoint device for malicious activity. Then, cybersecurity automation can step in to act on the findings of the AI model and quickly deploy a response. IBM estimates that this help detects threats 30% faster on average, as human involvement is minimized.

8. AI-based security authentication

Traditional authentication processes can be cumbersome, which sometimes results in users bypassing authentication mechanisms altogether. Artificial intelligence provides a smarter alternative and secures authentication, to protect against password-related risks and social engineering attacks. For example, AI can detect unusual login patterns and block access.

Considerations to Remember when Using Artificial Intelligence in Cybersecurity

Globally, AI in the cybersecurity market is expected to cross $28 billion by 2030, indicating strong demand. However, companies should keep the following factors in mind before deploying it:

• AI can prove resource-intensive, both in terms of costs and computational power.
• Unethical use of AI — such as monitoring private user behavior — has to be avoided.
• AI models must be pre-trained on the right datasets, and errors in data can lead to incorrect security insights.
• While AI can vastly minimize false positives, it still remains a concern.
• The resources freed up by using automation and AI must be rerouted to other tasks to prevent unemployment

Artificial intelligence has now found widespread use across IT systems, from AIOps to help desk management. In 2022-23, businesses must explore viable ways to incorporate this technology into their cybersecurity models.

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