How AI is preventing cyber attacks [including e-book]

My recent experience working at a cybersecurity firm has been enormously inspiring. I witnessed firsthand the dedication and innovation within the field, trying to embed AI into their security offerings. I had the distinct privilege of contributing to the development of one of those AI-based cybersecurity platforms, and this project ignited a passion to share the transformative potential of artificial intelligence in securing our digital landscape with you all.

The book(let) AI and Cybersecurity - threats and opportunities, is the result of that experience: DOWNLOAD IT HERE

This aim of the booklet is to bridge the gap between my personal experience and the broader industry trends shaping the future of cybersecurity, because the cybersecurity landscape is undergoing a radical transformation. This transformation is driven by the evolving nature of cyber threats - amongst others - due to AI.

AI has a brilliant arsenal of techniques capable of analyzing lots of data in real-time, identifying previously unseen patterns, and automating essential security tasks.

Within the context of my work at the cybersecurity firm, we used AI's capabilities to create a platform that could:

• Revolutionize threat detection: By analyzing network traffic, user behavior, and system logs, the platform could identify anomalies that might escape traditional methods. Machine learning algorithms were trained on historical data to recognize suspicious patterns, and incident prevention.
• Augment security analysts: The platform wasn't designed to replace human expertise but rather to augment security analysts. The AI handled the heavy work of data analysis. That freed up analysts' time to focus on strategic decision-making, and incident response.
• Automate repetitive tasks: AI made security operations more efficient by automating routine tasks such as vulnerability scanning, and patching. This not only saved valuable time for security teams but also improved their overall efficiency.

Major industry reports, such as the one by MarketsandMarkets, forecast a significant rise in the global AI cybersecurity market, with estimates a growth from $8.8 billion in 2019 to $38.2 billion by 2026. This exponential growth reflects the increasing value that AI has to offer in securing our digital infrastructure.

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Furthermore, a Gartner survey mentioned that cybersecurity has become a top priority for board directors, with a staggering 69% acknowledging its critical importance. Gartner further predicts that organizations that use AI and ML for cybersecurity will experience a significant boost in operational efficiencies (15%) and a reduction in cybersecurity costs (10%) by 2025.

Prevention methods against AI-powered attacks

Fight fire with fire. In other words, use defensive AI to mitigate offensive AI. Security practitioners must embrace the next generation of security tools, including:

• Network anomaly detection: AI-driven anomaly detection systems can identify unusual patterns or behaviors in network traffic, user activity, or system processes.
• AI-focused threat detection: Use AI detection to identify AI vulnerabilities.
• Security audits: Harnessing AI can make audits potentially faster, cheaper, and more thorough than manual efforts.
• Behavioral-AI email security: Ensure targeted phishing and malware attacks aren’t landing in employee inboxes.
• Posture management: Instantly uncover misconfigurations and unnecessary permissions across your environment.
• Automated security operations: With the growth in cyberattacks and the workforce gap, using AI to automate cumbersome-but-crucial manual tasks becomes a necessity.

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Cybersecurity companies

Companies are increasingly turning to AI platforms to fortify their defenses. Major cybersecurity providers already have developed an offering around it:

1. Darktrace is a pioneering company in the field of AI-powered cybersecurity. Their Enterprise Immune System leverages unsupervised machine learning to establish a “pattern of life” for every user and device on a network. By continuously monitoring and analyzing this baseline, the system can detect anomalies and potential threats in real-time, enabling proactive threat detection and response.
2. CrowdStrike’s Falcon platform is a prime example of how AI and ML are being integrated into cybersecurity solutions. The platform utilizes AI models for next-generation antivirus, endpoint detection and response (EDR), and threat intelligence. These models are trained on vast amounts of data, enabling them to detect and prevent advanced malware, ransomware, and targeted attacks with high accuracy.
3. Cylance (BlackBerry) Acquired by BlackBerry in 2019, Cylance is a leading provider of AI-driven cybersecurity solutions. Their AI models are trained to identify and block malware and malicious scripts before execution, providing a proactive defense against threats. This predictive analysis approach, powered by machine learning, has proven effective in protecting against advanced threats.
4. FireEye is a well-known cybersecurity company that has embraced AI and ML across its product portfolio. Their AI models of FireEye[i`are employed in endpoint security, network security, and email security solutions, enabling the detection and response to advanced persistent threats (APTs), zero-day attacks, and other sophisticated threats that might evade traditional security measures.
5. Palo Alto Networks has integrated AI and ML into their next-generation firewalls, cloud security, and automated threat prevention solutions. These AI-powered capabilities assist in identifying anomalies, unknown malware, and targeted attacks by analyzing network traffic patterns, user behavior, and other data sources.
6. Juniper Networks’ Advanced Threat Prevention solution leverages AI and ML for malware analysis, command and control detection, and identifying malicious encrypted traffic patterns. By automating these tasks, the solution enhances threat detection and response capabilities, reducing the burden on security teams.
7. Microsoft has incorporated AI and ML across its security product portfolio, including Windows Defender, Office 365 Advanced Threat Protection, and Azure Security Center. These solutions utilize AI for threat detection, investigation, and response, leveraging the vast amount of data and threat intelligence available to Microsoft.
8. IBM’s AI security solutions, such as QRadar Advisor with Watson and Resilient Incident Response Platform, utilize AI and ML for security analytics, user behavior analytics, and automated incident response. By leveraging the power of Watson, IBM aims to provide organizations with enhanced threat detection and response capabilities.
9. Cisco has embraced AI and ML across its security product line, including Stealthwatch, Umbrella, and Cognitive Threat Analytics. These solutions use AI to detect threats, identify malicious domains, and analyze abnormal network behavior, providing comprehensive security monitoring and protection.
10. Deep Instinct is a cybersecurity company that specializes in deep learning for malware detection and prevention. Their AI models are trained to predict and prevent unknown malware and zero-day threats at the pre-execution phase, providing a proactive defense against emerging threats.
11. According to Google: new “AI for Cybersecurity” cohort of 17 startups from the UK, US and EU under the Google for Startups Growth Academy’s AI for Cybersecurity Program. This will help strengthen the transatlantic cybersecurity ecosystem with internationalization strategies, AI tools, and the skills to use them. Also Google is open-sourcing Magika, a new, AI-powered tool to aid defenders through file type identification, an essential part of detecting malware. Magika is already used to help protect products including Gmail, Drive and Safe Browsing, as well as by our VirusTotal team to foster a safer digital environment. Magika outperforms conventional file identification methods providing an overall 30% accuracy boost and up to 95% higher precision on traditionally hard to identify, but potentially problematic content such as VBA, JavaScript and Powershell.

These examples illustrate how AI and ML are being integrated into various cybersecurity solutions, from endpoint protection and network security to threat intelligence and incident response. By leveraging the power of machine learning and data analysis, these companies aim to enhance threat detection, prevention, and response capabilities, ultimately strengthening the overall security posture of organizations.

AI-Powered threat detection and prevention

For instance, companies like Darktrace and CrowdStrike use unsupervised machine learning to establish baselines of normal behavior for users, devices, and network traffic. Any deviations from these baselines are flagged as potential threats, enabling proactive detection and response. Additionally, AI models can be trained to recognize known malware signatures and identify new variants, providing an added layer of protection against evolving threats.

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AI-Driven threat hunting and incident response

Threat hunting and incident response are critical aspects of cybersecurity, often requiring skilled analysts to sift through vast amounts of data and logs to identify potential threats and respond effectively. AI and ML technologies enhance these processes by automating data analysis, and providing actionable insights to security teams.

Companies like FireEye and IBM use AI and ML in their threat hunting and incident response solutions. They use the technology to enable efficient analysis of security events, identification of root causes, and rapid response to incidents. By automating repetitive tasks and providing intelligent recommendations, AI augments the capabilities of a security analysts.

User and entity behavior analytics

User and entity behavior analytics focuses on identifying anomalous behavior that may indicate potential threats or insider attacks. UEBA solutions use AI and ML to establish baselines of normal behavior for users, or devices, or applications within an organization’s network. A deviation from these baselines are flagged as potential threats, and enable proactive detection and response.

Companies like Splunk, LogRhythm, and Exabeam are offering these UEBA solutions, that are using AI algorithms to analyze user behavior, and network traffic patterns. By identifying anomalies and suspicious activities, these solutions can help organizations detect insider threats or compromised accounts.

AI-Powered Cyberattacks

While AI can strengthen cybersecurity defenses, it can also be used to launch sophisticated and targeted attacks. AI-powered cyberattacks can automate exploit identification for instance, and attack execution, making them more efficient and harder to detect.

AI can also be used to generate highly convincing social engineering attacks, such as spear-phishing emails or deepfake videos, increasing the risk of successful breaches through human manipulation

For a full overview of the AI threat landscape, download the ENISA report.

Examples of AI-Powered Cyberattacks

1. Using a deepfake voice technology in a CEO scam. In this scenario, a threat actor used AI-powered software to mimic the voice of a company's CEO and request urgent wire transfers from employees.
2. Creating convincing phishing emails with generative AI. A sophisticated phishing scam used to require research and effort, but now attackers can make them in seconds.
3. Discovering software vulnerabilities and evading intrusion detection with AI algorithms.
4. Chatbot phishing scams, where chatbots engage in seemingly harmless conversations with potential victims, subtly gathering information about personal details or login credentials.

The company Abnormal surveyed 300 cybersecurity leaders and nearly 50% confirmed the presence of AI-generated attacks in their email environments. This number is willincrease as cyberattacks become more common and costly than ever before.

Here’s why:

1. IoT devices, remote work, and a general worldwide reliance on virtual connectivity has opened endless doors for scams, attacks, and vulnerabilities.
2. Generative AI tools make it easier than ever for attackers to craft convincing messages and sophisticated malware.
3. There is a vast shortage of qualified cybersecurity personnel.
4. State-sponsored cyberattacks are a popular method to destabilize geopolitical foes.

These combined factors make AI-enabled attacks both more attractive and effective for threat actors.

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The human factor

Amidst all this, human analysts remain fundamental. AI may process data and identify patterns, but human intuition and expertise are necessary for contextual understanding and decision-making. Collaboration between AI and analysts creates a symbiotic relationship where AI accelerates data processing and threat identification while humans provide critical thinking and strategic insights. This partnership ensures a holistic approach, addressing cybersecurity threats’ technical and contextual aspects.

The potential of AI in cybersecurity is immense, but adversaries think the same way. As we continue on this path, the arms race will only intensify.

Signing off - Marco

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