Using Artificial Intelligence and Machine Learning as a Powerful Cybersecurity Tool - Liwaiwai : Job Details

Using Artificial Intelligence and Machine Learning as a Powerful Cybersecurity ToolFight sophisticated cyber attacks with AI and ML

When “virtual” became the standard medium in early 2020 for business communications—from board meetings to office happy hours—companies like Zoom found themselves in high demand. They also became prime targets for cyberattacks.

In April 2020, Zoom experienced a large data breach exposing approximately 500,000 user passwords.

This breach, along with thousands of daily reported cyber attacks, highlights that cybercrime is one of the top threats facing businesses today. Factors such as increased online operations and sensitive information crossing remote networks have made opportunities for cybercrime more prevalent, and attacks more sophisticated and harder to contain.

The average cost of a data breach was $4.35 million in 2022, and it took an average of 277 days for companies to detect and contain such breaches. These statistics underscore that cybersecurity is a critical issue requiring significant attention and investment.

As more companies adopt AI and ML in their operations, concerns arise about increased vulnerabilities and the potential for attackers to use these technologies for smarter, more advanced attacks.

Can we fight fire with fire? Can AI and ML be used within cybersecurity to react and prevent attacks more effectively?

The Constant Threat of Cyberattacks

Cyberattacks come in various forms, but data breaches often make headlines. In 2020, data breaches exposed 36 billion records, mostly containing personally identifiable information (PII), according to Risk Based Security.

Not only Zoom but also Twitter, Marriott, EasyJet, and Nintendo suffered data breaches, eroding public trust and raising data privacy concerns.

Other cyber threats include phishing, malware, and infrastructure hacks that can halt business operations. Contributing factors include:

• Vulnerabilities due to remote work
• Distributed employees using insecure networks and devices
• Advanced techniques for hackers to hide their identities

The global cybersecurity market was valued at USD 184.93 billion in 2021 and is projected to grow at a CAGR of 12.0% from 2022 to 2030.

The AI Factor

Increased use of AI and ML in business operations introduces new opportunities for cybercrime. These models can be manipulated—especially through training data—to produce desired results, and they can perpetuate attacks as they learn from previous experiences.

AI and ML models are also susceptible to reverse engineering, which can expose training data and lead to breaches.

However, advancements in AI and ML are also enhancing cybersecurity, enabling faster detection, identification, and response to threats. As cyberattacks evolve, so must cybersecurity strategies.

Understanding How Artificial Intelligence in Cybersecurity Works

AI, ML, and deep learning models create powerful tools for identifying and combating cyber threats. They analyze data, recognize complex patterns, and predict future outcomes based on historical data.

These models continually learn, becoming more sophisticated over time, and can not only recognize specific attacks but also anticipate future threats.

An example is spam filters in email inboxes, which are trained to identify illegitimate messages and warn users.

Common machine learning algorithms in cybersecurity include:

• Regression: Compares datasets, predicts outcomes, and detects deviations from expected behavior.
• Clustering: Groups similar data to reveal patterns of activity.
• Classification: Uses labeled data to categorize new data, such as identifying malicious files.

Advantages of Using AI Within Cybersecurity

AI and ML techniques enable the collection and analysis of vast amounts of data, generating actionable insights. They can learn from past attacks to predict and prevent future threats, and create detailed threat profiles rapidly.

AI models can monitor threats continuously, identify potential risks early, and support swift decision-making for responses.

AI Applications in Cybersecurity in the Real World

Facial Recognition: Used for security purposes, such as identifying individuals in footage or authenticating transactions.

Fraud Detection: In banking, AI models predict and prevent fraud by building threat profiles.

Antivirus and Threat Analysis: AI enhances detection of viruses, malware, and vulnerabilities faster than traditional methods.

AI for Smart Cybersecurity

As cyber threats become more sophisticated, leveraging AI and ML is essential for effective defense. These technologies enable faster, more comprehensive responses to both prevent and analyze cyber incidents.

For inquiries, product placements, sponsorships, and collaborations, contact us at [emailprotected]. We look forward to hearing from you!#J-18808-Ljbffr