Harnessing AI for Cybersecurity: The Power of Threat Intelligence

In today’s digital age, cybersecurity is more critical than ever. With the increasing sophistication of cyber threats, traditional security measures often fall short. Enter artificial intelligence (AI) – a game-changer in the realm of cybersecurity. This blog post explores how AI is revolutionizing cybersecurity and allows organizations and enterprises to combat increasingly sophisticated threats quickly and effectively.

The Role of AI in Cybersecurity

AI has become an indispensable tool in cybersecurity, offering unprecedented capabilities to combat increasingly sophisticated threats. By harnessing the power of AI, organizations can enhance threat detection and response capabilities, automate processes, and gain valuable insights into new attack vectors. AI’s ability to process massive amounts of data, recognize patterns and extract data-driven insights in real-time has changed the way threats are detected and mitigated. In addition, generative AI (GenAI) in particular is increasingly being used by threat actors in creating and executing increasingly sophisticated cyberattacks. GenAI is a type of AI that uses generative models to produce text, images, videos or other forms of content.

Why is AI Crucial for Cybersecurity?

The growing sophistication of cybersecurity threats, such as social engineering attacks and ransomware, is increasingly challenging traditional security measures in detecting and stopping these incidents. As organizations contend with an overwhelming influx of data that must be analyzed for potential vulnerabilities, alongside increasingly sophisticated attackers and attack techniques, strengthening cybersecurity has become essential. Leveraging innovative technologies is now vital to stay ahead of these evolving risks.

Key reasons why AI plays a critical role in cybersecurity include:

• Cost Efficiency: AI-powered automation brings significant cost savings to cybersecurity operations. By automating repetitive tasks like log monitoring, vulnerability scanning and patch management, AI reduces the need for extensive manual work, allowing human analysts to use their time more effectively. Additionally, AI enhances the accuracy of threat detection, reducing the likelihood of false alarms that can waste time and resources. Traditional cybersecurity systems can sometimes misidentify threats, causing teams to investigate non-issues or miss actual risks, leading to inefficiencies. AI helps avoid these pitfalls, contributing to overall cost reduction.

• Enhanced Scalability and Real-Time Analysis: Traditional cybersecurity methods often struggle to handle the sheer volume of data generated by today’s complex, interconnected systems. AI capabilities, however, can efficiently process and analyze vast datasets, including network traffic, system logs, user activities and threat intelligence feeds, from multiple sources in real time. AI can often spot subtle signs of potential threats that might otherwise go unnoticed by human analysts, maintaining a proactive defense strategy.

Real-World Applications of AI in Cybersecurity

Rather than replacing security professionals, AI enhances their ability to perform tasks more effectively and efficiently. Key AI use cases in cybersecurity include:

• Vulnerability Intelligence: AI is used to continuously scan for and identify system vulnerabilities, both known and unknown. By analyzing historical data, threat feeds and past incidents, AI can prioritize which vulnerabilities are most likely to be exploited, helping teams to patch high-risk issues before they’re targeted by cybercriminals.
• External Attack Surface Management: AI helps organizations manage their external security posture by continuously scanning for threats across the internet-facing attack surface, including websites, applications and external APIs. It identifies potential vulnerabilities that could be exploited by attackers and provides recommendations for mitigation, ensuring the organization’s public-facing assets remain secure.
• Dark Web Monitoring: AI-powered tools monitor the dark web for stolen data, leaked credentials or discussions targeting the organization. By analyzing patterns and activities on dark web forums and marketplaces, AI can flag potential threats, giving security teams early warning signs and enabling them to respond quickly before data is used maliciously.
• Digital Risk Protection: AI-driven Digital Risk Protection (DRP) helps organizations defend against a wide array of digital risks, including brand impersonation, phishing attacks, and threats arising from third-party vulnerabilities. By scanning the web, social media, and dark web, AI identifies digital threats that may harm the organization’s reputation or lead to data breaches. With real-time monitoring, AI can detect and neutralize emerging risks before they escalate, allowing businesses to protect their online presence and sensitive information from potential exploitation.
• Identity and Access Management (IAM): AI is used in IAM to analyze user sign-in behavior patterns and identify anomalies that could indicate suspicious activity. It can trigger actions like enforcing two-factor authentication or prompting a password reset under certain conditions. In more severe cases, AI can block access to accounts that are suspected of being compromised, helping to prevent unauthorized access.
• Endpoint Security and Management: AI plays a crucial role in identifying all endpoints in use within an organization, ensuring that each is updated with the latest operating systems and security patches. It also helps detect malware and other signs of cyberattacks on organizational devices, providing an additional layer of protection.
• Cloud Security: As organizations increasingly rely on cloud infrastructure and apps, AI helps provide visibility into risks and vulnerabilities across multicloud environments. It helps teams track and manage security across multiple cloud platforms, ensuring that potential threats are identified and mitigated before they become serious issues.
• Cyberthreat Detection: AI is central to both Extended Detection and Response (XDR) and Security Information and Event Management (SIEM) solutions. XDR uses AI to monitor endpoints, email, identities and cloud apps for anomalies, either surfacing incidents for further review or responding automatically based on predefined rules. SIEM platforms leverage AI to aggregate and analyze signals from across the enterprise, offering enhanced visibility and enabling quicker threat identification.
• Information Protection: AI assists security teams by identifying and labeling sensitive data within the organization, whether stored on-premises or in the cloud. It can also detect suspicious data movement, such as attempts to transfer sensitive information outside the organization, and either block these actions or alert the security team for follow-up.
• Incident Investigation and Response: In the aftermath of a cyberattack, security professionals need to sift through massive amounts of data to identify threats. AI streamlines this process by correlating relevant events across multiple data sources, saving time and improving response efforts. Generative AI goes a step further, translating complex analysis into natural language and providing answers to investigators’ questions, making the process more intuitive and efficient.

These use cases illustrate how AI is transforming cybersecurity, empowering security teams to manage, detect and respond to threats with greater precision and speed, all while reducing manual workload and improving proactive defense strategies.

How AI is Being Exploited by Threat Actors

Just like cybersecurity teams, threat actors also increasingly leverage AI to automate, accelerate, or enhance their attacks. This adaptation has further significant implications, including lowering the barrier of entry for new cybercriminals, without needing specialized skills or extensive training. Moreover, it significantly increases the scale and sophistication of various types of cyberattacks, including:

• Ransomware – AI can be used by ransomware groups in many ways, from researching targets, identifying system vulnerabilities, encrypting data, adapting and modifying ransomware to evade endpoint detection. For instance, the recently emerged FunkSec ransomware group, which rapidly gained notoriety, has reportedly used AI-assisted malware development.
• Social Engineering Attacks – AI can assist in the research phase and execution of a social engineering attack (a cyberattack that aims to manipulate human behavior to fulfill malicious purposes).
• Phishing Scams – GenAI can create highly personalized and realistic text to be used in emails, SMS messages, phone communication, or social media to achieve a desired result. Moreover, AI can be used to automate real-time communication, allowing cybercriminals and other bad actors to steal personal information and account credentials, reset account passwords, etc.
• Deepfakes – AI driven deepfakes allows threat actors to impersonate individuals’ appearances with deceptive accuracy, bypassing verification systems and gaining access to sensitive resources. Furthermore, they can also be used as part of disinformation campaigns and other types of cyberattacks.
• Malicious LLMs – usually an altered version of a large language model (LLM) that produces harmful outputs. Such GPTs can generate attack vectors (such as malware) or supporting attack materials (such as fraudulent emails or fake online content) to advance an attack. For example, recent research unveiled a new malicious GenAI chatbot called GhostGPT, that is traded on dark web forums and group messaging platforms. It is marketed for various malicious activities, including malware creation, exploit development, business email compromise (BEC) scams and phishing.
• Exploit Vulnerabilities – AI can effectively find vulnerabilities in software, and attackers can use it to find those vulnerabilities before they are patched, while also automating the creation of code exploits for one-day and zero-day vulnerabilities.
• Social Media Botnets – threat actors, including hacktivists and nation-state actors, use AI-generated botnets to flood social media with misinformation, manipulating public opinion. The bots generate convincing content, including deep fake videos and multilingual posts, significantly increasing the scale and sophistication of influence campaigns.

Understanding AI-Powered External Threat Intelligence

One critical area of cybersecurity where AI has been revolutionary is external threat intelligence. External threat intelligence involves the collection, analysis and dissemination of information about potential or current attacks that threaten an organization. AI enhances this process by automating data collection from various sources, identifying patterns, and predicting potential risks with speed and accuracy. This proactive approach allows organizations to stay ahead of cyber threats and protect their critical assets.

Key Capabilities of AI-Driven Threat Intelligence

1. Data Aggregation and Analysis: AI-powered threat intelligence platforms analyze and correlate data from multiple sources, including the dark web, group messaging platforms and other online forums. Comprehensive data aggregation provides a broad perspective on current and emerging threats relevant to the organization’s industry. Another aspect of analysis is automatic risk scoring and categorization. Gen AI is used to filter findings in the data lake, and each finding is then assigned a threat level and categorized (e.g., ransomware, phishing), providing more focused and actionable insights for analysts.
2. Anomaly Detection: AI systems continuously monitor network traffic and user behaviors to detect subtle signs of intrusion or abnormal activities that might elude traditional security measures. By establishing a baseline of normal behavior, these tools can detect potentially malicious deviations.
3. Predictive Capabilities: AI can identify potentially malicious activities and threat actors, allowing organizations to predict and prevent cyberattacks before they occur. This predictive capability is crucial for proactive cybersecurity measures.
4. Automated Response: AI can automate the response to detected threats, isolating affected assets and mitigating risks in real-time. This reduces the time it takes to respond to incidents and minimizes potential damage.
5. Comprehensive Reporting: AI-driven threat intelligence services provide in-depth reports and documentation, summarizing completed work, highlighting threats specific to the organization and providing automatic alerts based on potential threats detected. These reports empower executives to make informed, strategic decisions to strengthen cybersecurity resilience.

AI Features of LUMINAR External Cyber Threat Intelligence

LUMINAR, Cognyte’s external threat intelligence solution is designed to help organizations stay ahead of advanced cyber threats, including those driven by AI-powered adversaries. As cybercriminals increasingly employ artificial intelligence to enhance their tactics, techniques and procedures (TTPs), LUMINAR leverages GenAI capabilities not only to detect and respond to these evolving threats but also to streamline threat classification and risk prioritization.

• LUMINAR’s GenAI risk scoring assistant is a crucial tool in combating complex, AI-enhanced threats. It automatically filters through vast amounts of collected data, efficiently identifying false positives and focusing on relevant risks. The assistant assigns risk scores based on threat relevance and urgency, while also classifying threats across different vectors. The GenAI assistant dynamically adapts to emerging AI-driven tactics, providing timely and precise threat detection. This automated process enables security teams to prioritize high-risk incidents and allocate resources effectively.

• LUMINAR delivers a continuous stream of threat intelligence feeds, powered by GenAI, offering comprehensive global coverage of cyber threats. The LUMINAR AI cyber feed tracks a wide range of critical information, including exploited vulnerabilities, newly identified malware, evolving TTPs of threat actors, and significant cyber incidents impacting organizations worldwide. By correlating open-source data on cyber threats and attacks, the feed enhances LUMINAR’s analysis, providing organizations with actionable intelligence. This enables security teams to extract valuable insights and develop proactive strategies for mitigating security risks effectively.

• LUMINAR features a GenAI-powered global threat intelligence dashboard that delivers actionable insights into the constantly evolving cyber threat landscape. This interactive platform allows security teams and threat analysts to seamlessly review and analyze critical data on emerging security threats, highlighting key details such as the most active attack groups, prevalent malware, and vulnerabilities most frequently exploited by cyber adversaries. Moreover, the dashboard enables focused analysis on specific areas of concern, such as industries under attack or geographical regions being targeted, ensuring that security teams are always aware of the most relevant threats. The platform also provides immediate access to GenAI-detected TTPs and associated IOCs, facilitating swift, informed response and effective mitigation strategies.

Conclusion

AI-driven threat intelligence is transforming the cybersecurity landscape by providing organizations with the tools they need to detect, predict and respond to threats more effectively. As cyber threats continue to evolve, the integration of AI in cybersecurity will become increasingly vital. By leveraging AI’s capabilities, organizations can stay one step ahead of cybercriminals and protect their digital assets with confidence. Click here to learn how.