Evolving Cybersecurity with Artificial Intelligence

What is Cybersecurity?

Cybersecurity, otherwise called Information Technology security, is the act of safeguarding critical systems, networks, programs and confidential data from external or internal threats and attacks.

Every cybersecurity attack is coordinated to access, alter or damage confidential data or otherwise to crash a business operation, usually for economic benefit. This financial benefit to the attackers translates to significant monetary loss for the affected party. As IBM’s “Cost of a Data Breach (2021)” report puts it:

i.  2021 had the most average cost in the 17 years since the inception of the report. Data breach costs rose from USD 3.86 million to USD 4.24 million in 2021.

ii. The average cost was USD 1.07 million higher in breaches where remote work was a factor in causing the breach. With the COVID-19 accelerated rise in remote work and the apparent preference for the approach, this statistic is a worrying trend.

iii. The most occurring initial attack vector, compromised credentials, was responsible for no fewer than 20% of breaches at an average breach cost of USD 4.37 million.

These expenses were incurred from the cost of diagnosing and containing the attack, the duration of the offline time and lost revenue and the far-off implication of the attack on the brand’s image. Customers do not take kindly to their private data being stolen and available for sale to nefarious buyers. The subsequent legal steps they pursue can further contribute to the economic burden of an affected brand.

Types of Cybersecurity Threats

Attackers, just like the technology being used to repel them, are also staying innovative and discovering new ways to circumvent defense measures.

Understanding the nature of cybersecurity threats is one step toward combating them. Some of the common cyber threats include:

Distributed denial-of-service (DDoS) attacks: A DDoS attack just does as its name suggests - it denies users the service of the target in the attack by overwhelming the servers, website, or network in a bid to crash the system. Several coordinated systems usually carry it out.

Phishing: This clever form of cyber attack deceives a user into releasing their own personal data by masking as a trusted source and requesting for the user’s data.

Malware: Malware is software e.g. viruses, or Trojans that gain unpermitted access to or damage a computer.

Ransomware: As the name implies, it takes ownership of data and restricts access to it until a ransom is paid to the originators of the attack.

Insider threat: This cyber attack originates from within an organization - usually from a past or present employee with system access.

Man-in-the-middle attack: In this form of attack, the intruder intercepts communication between two parties to steal data.

Advanced Persistent Threats (APTs): This is a covert form of attack in which the cybercriminal sneaks into a system and remains in the shadows undetected. During this time, the intruder spys and collects sensitive information away from the gaze of defense systems.

Evolving Cybersecurity with AI

Based on IBM’s report, the most important approach to lessen the economic burden of cybersecurity attacks is from the full deployment of automation and Security Artificial Intelligence. This was estimated to provide up to 3.8 million dollars in savings compared to conventional cybersecurity systems.

Across all industries, AI thrives due to its capacity to process data faster and more accurately than humans. AI also has the ability to learn from its interactions with data to become better at the task it was designed to do.

The following are important use cases of AI in cybersecurity:

i. Advanced Pattern Detection: Cyberattacks thrive under the cover of networks and slip away unnoticed through the use of encrypted communication networks, stolen credentials and deleted or altered logs. Machine learning models can learn to recognize new patterns of suspicious behavior and flag these threats.

ii. Uncovering Zero-Day Vulnerabilities: Cyberattacks often exploit zero-day vulnerabilities (i.e. software weaknesses a vendor is unaware of). Because these weaknesses are on the blindside of the vendor, they are a perfect spot for perpetrators to inject malware. AI can fight this in several ways: Natural Language Processing can detect suspicious files within a source code; Generative Adversarial Networks can utilize their data distribution mimicry capabilities to locate complex vulnerabilities. Furthermore, machine learning can be used to monitor traffic on the dark web to uncover information pertaining to zero-day attacks.

iii. Email monitoring: This is a more mainstream use case of AI in cybersecurity. Communication channels can be strengthened through the use of machine learning-based vulnerability assessment & tracking software which can flag malware and phishing attempts in emails without reading the content of the mails.

iv. Machine Learning to prevent DNS Data Exfiltration: Domain Name System (DNS) is a weak point in cybersecurity that malicious agents manipulate to avoid firewalls and intrusion detection systems. This is because DNS data is usually allowed through firewalls making it an excellent piggyback for malware. However, machine learning algorithms can be used to find and prevent DNS data exfiltration as they learn on trillions of DNS queries processed daily.

v. Triaging cyberattacks: Cyberattacks are a common daily occurrence that cybersecurity analysts have to deal with. The sheer volume of attacks they have to trace and diagnose can lead to personnel fatigue. To avoid this problem, machine learning algorithms can be designed to identify attack patterns, classify based on the level of risk and then escalate to be reviewed by the human analyst.








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