Artificial Intelligence and Cybersecurity

Use cases for AI in cybersecurity

• User Entity Behavior Analytics (UEBA): AI monitors and analyzes user behavior to establish baselines and identify deviations that may indicate insider threats or compromised accounts. UEBA helps in detecting unauthorized access or unusual activities associated with malicious actors.
• Identity and Access Management (IAM): AI for IAM can analyze sign-in patterns and behaviors to detect and raise suspicious behavior to analyst attention. It can then automate two-factor authentication or password reset when conditions are met. It can even block a user if an account has been compromised.
• Security Content Creation: Utilize AI to analyze attack behaviors to automatically create new rules for threat hunting and detection and response.
• Extended Detection and Response (XDR): XDR solutions leverage AI to correlate and analyze data from various sources. This provides a more comprehensive and effective approach to threat detection. XDR solutions monitor endpoints, emails, identities, and cloud apps for anomalous behavior. They then surface incidents to the team or respond automatically depending on the rules defined by security operations.
• Threat Detection: AI analyzes network traffic, system logs, and user behaviors to detect unusual patterns and anomalies that may indicate a security threat. ML models can identify known malware signatures and learn to recognize new, previously unseen threats.
• Advanced Phishing Detection: AI uses natural language processing (NLP) to analyze email content, sender behavior, and communication patterns to detect phishing attempts.
• Malware Detection: AI-driven malware detection systems analyze patterns and behaviors to identify and mitigate the impact of malware in real time. This includes those with polymorphic characteristics that change over time.
• Incident Response Automation: AI automates incident response processes and triggers quick and coordinated actions in response to security incidents. Automated responses can include isolating compromised systems, blocking malicious activities, and implementing predefined security measures.
• Network Security: AI is used in intrusion detection systems (IDS) and intrusion prevention systems (IPS) to monitor network traffic for signs of malicious activity. Deep learning models can analyze complex network patterns and identify sophisticated attacks.
• Security Information and Event Management (SIEM): AI enhances SIEM solutions by correlating and analyzing real-time security events, reducing false positives, and providing actionable insights.
• Security Awareness Training: AI-powered platforms can tailor security awareness training programs based on individual user behavior. This targeted education helps mitigate risks.

Best practices for AI in cybersecurity

Data Security and Privacy

• Ensure data quality and security: Since AI models learn from data, high-quality data with proper security measures is crucial. Implement data anonymization and encryption where necessary to protect sensitive information.
• Adhere to data privacy regulations: Comply with relevant data privacy regulations like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) when collecting, storing, and utilizing data for AI models.

Model Training and Development

• Determine the appropriate AI technique: Different AI techniques are suited for different tasks. Carefully analyze the specific cybersecurity challenge and select the most appropriate AI approach. Choices include machine learning and deep learning.
• Address bias in data and models: Biased training data can lead to biased AI models. Implement techniques to identify and mitigate bias in data collection, labeling, and model development.
• Ensure model explainability and interpretability: Strive for transparency in AI models to understand their decision-making processes and better audit and identify potential issues.

Deployment and Monitoring

• Start with small-scale implementations: Begin by deploying AI for specific, well-defined tasks before scaling up to monitor and evaluate its effectiveness easily.
• Continuously monitor and evaluate: Regularly monitor the performance of AI models, identifying potential performance degradation or vulnerabilities that need to be addressed. These include overfitting, underfitting, model drift, and more.
• Establish human oversight and control: AI should be seen as a powerful tool, not a replacement for human expertise and judgment.

Challenges of AI

Challenge #1: Bias in Data and Models

AI models may inherit biases present in the training data, potentially leading to biased decisions or outcomes. Mitigating bias in data collection, labeling, and model development is critical. This is particularly concerning in cybersecurity, where biased models may disproportionately impact certain user groups or fail to detect specific types of threats.

Challenge #2: Adversarial Attacks

Malicious actors might attempt to manipulate AI models through techniques like poisoning training data to generate incorrect outputs. Or they might craft adversarial examples to exploit vulnerabilities and bypass security measures such as model inversion, model extraction, and model-based attacks. Securing the underlying AI infrastructure and algorithms is critical to preventing compromise.

Challenge #3: Complexity and integration

Integrating AI with existing security infrastructure can be complex and require significant resources. Compatibility issues, interoperability challenges, and the need for skilled personnel to manage and maintain AI systems may obstruct seamless integration.

Challenge #4: Overreliance on AI

Overreliance on AI without human oversight can lead to blind spots. Human analysts provide critical thinking, context, and intuition that AI may lack. Relying solely on AI could result in missed threats or false confidence in the system's capabilities.

The future of AI for cybersecurity

Collaborative Defense

The evolution of collaborative defense, with AI systems working together across organizations, fosters a collective and robust cyber defense ecosystem.

Proactive Defense

AI could analyze threat intelligence, user behavior, and network activity to identify potential vulnerabilities. It could then take preemptive actions like patching software, isolating compromised systems, or even deploying counter-deception measures.

Self-healing Systems

Self-healing systems can automatically detect and remediate security breaches. This includes automatically quarantining infected systems, patching vulnerabilities, and restoring compromised data.

Evolving Adversarial Landscape

While AI advances the defenders' side, the attackers are likely to leverage similar technologies to develop more sophisticated and evasive threats. This could lead to an arms race between AI-powered offense and defense, pushing the boundaries of both sides.

Trellix's approach to AI

For over a decade, Trellix has harnessed the capabilities of AI and ML to fortify our defenses, enhance detection capabilities, facilitate thorough investigations, and expedite remediation processes. With some of the largest databases globally, our extensive repository of file and certificate reputations significantly contributes to the effectiveness of our product detections, enabling the development of more resilient models.

At Trellix, our approach involves seamlessly integrating human expertise with the dynamic potential of AI. Through the strategic application of AI, our products at Trellix optimize security operations by incorporating;

• Workflow automation
• Advanced detection mechanisms
• Event correlations
• Comprehensive risk assessments
• Malware and code analysis
• Auto-generated investigative and response playbooks
• A unified understanding of product knowledge throughout the ecosystem

Trellix AI capabilities

By providing critical insights earlier in the kill chain, Trellix Threat Intelligence helps our customers mitigate more attacks faster. It utilizes AI-guided investigations to help you resolve attacks sooner.

Here's how Trellix is using AI:

• Tracking, analyzing, and disseminating over 3,000 threat campaigns through curated intelligence dossiers.
• Providing AI-guided threat modeling supported by our Advanced Research Center for data analysis.
• Operationalizing intel with proactive policy guidance within the Trellix Insights product
• Using control points to provide rich telemetry across threat vectors with highly trained models for faster, more accurate detections.
• Analyzing diverse data sources, including logs, alerts, and threat intelligence, to extract meaningful insights for faster remediations.
• Offering recommendations for enhancing defenses. These include implementing intrusion detection and prevention systems, tightening access controls, updating security policies, and conducting security awareness training.
• Using AI's language processing capabilities to enable security orchestration, automation, and response (SOAR) platforms to support multiple languages. This helps overcome language barriers in incident response.

Trellix Wise

Trellix Wise is GenAI-powered hyperautomation for threat detection and response. Wise’s capabilities leverage over 10 years of AI modeling, 25 years of analytics and machine learning, and numerous petabytes from control points like endpoints, networks, email, data security, and more. 

With Wise, teams can automatically investigate all of their data, eliminate false positives, automate remediation, and use conversational AI to perform threat hunting, no matter their current level of expertise. Additionally, Trellix has teamed up with Amazon Bedrock to enhance GenAI functionality. 

• Delivers 5x more efficiency for analysts
• Ensures alerts are quickly triaged, scoped, and assessed with automatic alert investigations
• Saves eight hours of SOC work for every 100 alerts
• Leverages 3x more third-party integrations
• Delivers real-time threat intelligence leveraging 68 billion queries a day from more than 100 million endpoints
• Delivers 50% reduction in MTTD and MTTR