With the ever-evolving threat landscape, cyberattacks have become more sophisticated. Malicious actors are diligently researching, planning, and improving their skills to target and take down users and organizations. In cyber risk environments, where threat actors develop new modus operandi with each attack, it is imperative for the cybersecurity industry to adopt new safeguarding mechanisms and advanced defensive technologies.

In response to the increased number of breaches, the cybersecurity industry is witnessing a progressive shift from security defenses that are conventional to those that are innovative. The advent of AI and ML in cyberspace could be a new dawn for the industry.

AI and ML in the cybersecurity industry means deploying self-supporting tools that detect, stop, and prevent attacks with additional intelligence that involves less human intervention. The ML-powered tools can detect threats by undergoing a form of training on their own based upon the initial dataset supplied. This primal set of metadata, provided by the developers, acts as a reference point for these tools to help them form a user behavior baseline, and differentiate between normal and malicious user behavior. On the other hand, the AI-based tools follow more of a predictive analysis approach that takes input from ML-based systems, such as anomalies. With its resolution intelligent workflow execution capability, AI-based tools can then predict the damage or possible attack intensity, and ensure the decision-making process is streamlined and quick.

AI and ML-based tools can quickly and easily analyze thousands of incidents and detect various types of threats ranging from brute-force attacks to threats that exploit zero-day vulnerabilities. These systems become smarter and more experienced over time, and with the help of past behavior records of the users and the entities in the network, can then efficiently respond to deviations from the normal behavior.

However, can AI and ML be termed the future of the cybersecurity industry? The answer is not as easy as it may sound. Like the two sides of a coin, this emerging technology has its fair share of positive and negative influences on cyberspace. We'll discuss each of these below:

Positive influence of AI and ML in cybersecurity

  1. Effective use in SIEM: AI and ML can be used by SIEM tools for anomaly and pattern detection, and for automating time consuming tasks. They can also be used with threat intelligence for better security.

  2. Detection of hacking attempts: The tools can detect different hacking techniques used for breaching the networks like Aircrack-ng, and brute-force attacks.

  3. Combating and detection of bots: AI and ML-based approaches help take down botnets and defend against attacks which make use of botnets, like ransomware or DoS attacks.

  4. Phishing and email spam filters: These can identify and filter out malicious and spam email content.

  5. Improved incident response: The AI and ML-based tools can facilitate automatic intelligent response initiation and remediation of security alerts and incidents, which helps in better mitigation and the prevention of attacks.

  6. Prevention of fraudulent activities: The tools can collect and analyze data to help predict fraudulent and illicit activities. This enables security teams to take proactive actions to halt or minimize threats and disruptions.

Negative influence of AI and ML in cybersecurity

  1. AI vs. AI battle: Threat actors try to exploit AI and ML technology to fulfill their malicious intentions. This can lead to a battle between AI-based security tools and the adversarial AI systems of hackers.

  2. Malware: Hackers can use AI to develop malware with mutating capabilities which might allow them to stay undetected in a compromised network.

  3. Phishing and Spam: Attackers can create AI and ML algorithms to create sham messages that might look authentic and legitimate but are intended to steal user's personal and sensitive information.

  4. Unavailability of data: Without huge volumes of data and records to learn from and perform analysis, AI tools might provide inaccurate outcomes and false positives.

The introduction of AI and ML technology in the cybersecurity discipline sounds promising, but it also has some serious downfalls. The future of AI and ML in the cybersecurity space is not easy to predict, and it will take time for industry professionals to understand how efficiently the technology can scale and provide a robust and resilient security posture to users and organizations.

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