Internet Security: How to prepare for when the world runs on AI

Artificial intelligence (AI) and machine learning (ML) researchers have made incredible strides in the last decade. While advanced generalized AI applications like ChatGPT may garner the headlines, AI and ML technology are being embedded in many specialized software systems. Cybersecurity is one area where these technologies are increasingly being used in innovative advanced implementations.

Cybersecurity defenses and threat detection tools have leveraged machine learning techniques for years. Harnessing the power of AI and ML has improved the ability of software solutions to identify and respond to emerging threats to a computing environment. Unfortunately, attackers are increasingly using these technologies in their attempts to subvert cybersecurity measures.

The coming years will see Internet security informed by an AI and ML-powered arms race between defenders and attackers. Organizations that fail to implement cutting-edge AI solutions will find themselves at a distinct disadvantage.

Focus areas for AI implementation

The current level of AI technology has been implemented in many diverse use cases. According to McKinsey, the percentage of companies adopting AI in at least one business area has grown from 20% in 2017 to 50% in 2022. Along with this increased adoption, the number of AI capabilities used in these implementations has doubled from an average of 1.9 to 3.8.

The AI capabilities most commonly embedded in products or processes include:

• Robotic process automation;
• Computer vision;
• Natural language text understanding;
• Virtual agents;
• Deep learning;
• Recommender systems.

The most popular use of AI is in service operations optimization. Risk modeling and analytics, which are essential for cybersecurity applications, are currently being adopted by less than 20% of organizations.

Near future advances in AI

It’s impossible to precisely predict future advances in AI, but there is some consensus regarding the general direction research and innovation will take over the next several years. Following are some of the top trends in AI that will impact Internet security.

AI for cybersecurity

This trend speaks directly to our main area of interest. AI techniques are increasingly being used to strengthen cybersecurity and surveillance applications. AI and ML are essential for the advanced detection of criminal activity and addressing it before the threat impacts business systems.

Other uses of AI in security include enhanced facial and voice recognition, video analysis, and biometric authorization. While these trends promise to improve security, they may also be used to attack enterprise infrastructure.

AI for communications

Next-generation AI is becoming more adept at natural language processing (NLP) to automatically generate content that can be indistinguishable from artifacts created by humans. AI chatbots can answer questions and assist humans with organizational activities such as scheduling appointments.

AI and the Internet of Things (IoT)

The convergence of AI and IoT devices enables companies to obtain actionable insights for increased efficiency of IoT implementations. Infusing devices with AI capabilities allow them to function autonomously and carry out a wide variety of activities without human intervention.

How AI will impact internet security

AI will impact Internet Security both positively and negatively. In many cases, the same functionality or innovations that drive cybersecurity defensive applications will also be used by malicious actors and cybercriminals. Let’s look at how defenders are using AI to protect computing environments and how attackers are employing the technology to subvert security.

Use of AI for improved cybersecurity

Security teams can implement AI in several ways that improve cybersecurity and protect enterprise computing resources.

• Threat detection - A solution powered by AI and ML technology can recognize malware based on minute behavioral characteristics that would escape traditional detection methods.
• Breach risk prediction - AI systems can predict how and where it is most likely that an environment will be compromised, allowing organizations to assign additional resources to address these vulnerabilities.
• Enhanced endpoint protection - AI-based endpoint protection establishes a baseline of behavior that enables it to identify anomalies and take appropriate action. This approach provides proactive rather than reactive threat protection.
• Battling malicious bots - AI and ML can learn to distinguish between good and bad bots so appropriate action can be taken against malicious intruders.

Use of AI by hackers and cybercriminals

Hackers and cybercriminals can also make use of AI techniques in a variety of ways.

• AI-powered malware - Malware created by AI can think for itself, update its actions to address the current situation, and target specific victims and systems. The malicious software can take evasive action to remain hidden until the time it launches the attack.
• Malicious use of ChatGPT - ChatGPT is already being used by cybercriminals to build hacking tools. They are building bots that impersonate girls to gain the trust of potential victims and engage in extended conversations. The tool is also being used to create authentic-seeming spear-phishing emails or to develop intelligent malware to compromise user data.
• Social engineering - The creation of deep fakes with the assistance of AI technology can be used to increase the probability of fooling users through social engineering attacks.

Why cybersecurity must adopt AI to address emerging threats

As cyber criminals make more extensive use of AI capabilities to launch innovative and sophisticated attacks, it will become impossible to address these threats without deploying powerful AI-based defensive measures. In the arms race between cybersecurity professionals and cybercriminals, it’s essential to make the most effective use of AI and ML technology.

The use of AI by cybercriminals will make traditional methods of threat detection and malware protection obsolete. Malware signatures can be changed dynamically to defeat even the most recently updated traditional antivirus tools. The advanced detection capabilities of XDR rely on machine learning to perform behavior analysis and behavior modeling. Combined with traditional methods, this capacity provides more effective threat detection and enhances cybersecurity. Organizations that do not adapt and deploy advanced AI and ML-powered approaches like extended detection and response (XDR) will find themselves under increasing pressure from cyber criminals.

Samurai XDR employs cutting-edge AI and ML technology to identify and respond to potential threats to an environment before they have a chance to cause damage. Machine learning drives advanced analytics that includes behavior modeling to detect threats with near-zero false positives. A user-friendly dashboard gives a security team a wealth of information that uses unique indicators of compromise identified from combined data sources to uncover previously hidden threats.

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