ai course cyber reading real world examples of applied ai based threat intelligence
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Introduction
Case Study: Google and Mandiant
Case Study: Microsoft
Case Study: IBM
Summary
Resources
The implementation of AI in threat intelligence has become increasingly prevalent in real-world scenarios, with several notable companies leading the way. This lesson examines case studies of organizations, including Google, Microsoft, and IBM, to showcase how they leverage AI to enhance their threat intelligence capabilities.
By the end of this lesson, you will be able to
Explore how Google, Microsoft, and IBM use AI for threat intelligence
Google uses AI to analyze billions of security signals daily to identify potential threats. Advanced modeling allows Google’s customers to create complex workflows and reliable, repeatable response processes. By combining Google's robust data analysis and computing resources combined with decades of security knowledge gained through the acquisition of Mandiant in 2022, customers can quickly detect indicators of compromise, respond, and mitigate threats.
Mandiant, renowned for its frontline expertise and industry-leading threat intelligence, has been at the forefront of combating security breaches for the past 18 years. With Mandiant’s acquisition, Google also acquired the domain knowledge and expertise of over 900 consultants and analysts. The dynamic cyber defense solutions that Mandiant provides Google with protection from cyber threats and a highly skilled team to guide incident response management when security breaches and cyber attacks occur.
The popularity of cloud computing in recent years has introduced different concerns to the cyber security landscape. The interconnected nature of cloud environments necessitates robust cybersecurity measures to safeguard data integrity, confidentiality, and availability.
Organizations like Google Cloud must ensure the confidentiality of sensitive information, protect against unauthorized access, prevent data breaches, and maintain compliance with regulatory requirements. Additionally, they must defend against evolving threats such as malware, ransomware, phishing attacks, and insider threats that target cloud systems.
The consequences of inadequate cybersecurity in the cloud can be severe. Breaches can result in significant financial losses, reputational damage, legal implications, and the loss of customer trust. Furthermore, as cloud environments often host critical infrastructure and services, disruptions or unauthorized access can have far-reaching implications for businesses and their customers. To address these challenges, organizations must prioritize cybersecurity measures tailored to the cloud computing landscape. This includes implementing strong access controls, encryption, network security, and threat monitoring solutions. Regular security assessments, vulnerability scanning, and employee awareness training are also crucial to identify and mitigate risks effectively.
The collaboration between Google Cloud and Mandiant will enable the delivery of intelligence and expertise at scale through the Mandiant Advantage Software-as-a-Service (SaaS) platform, complementing Google Cloud's existing security portfolio. By combining forces, the two organizations aim to make a significant impact in securing the cloud, promoting the adoption of cloud computing, and fostering a safer digital environment.
Security Copilot, that will address three key issues that security analysts face:
Attack Complexity
Complex systems can be detrimental during an attack. By consolidating data from various sources and transforming it into straightforward, practical insights, analysts can respond to incidents within minutes rather than enduring extended periods under attack.
Subtle Evasion Tactics
In the face of subtle evasion tactics employed by attackers, Copilot swiftly analyzes signals at rapid speed using machine learning. It identifies threats at an early stage and obtains proactive guidance to effectively counter an attacker's future actions.
Talent Gap
Talent scarcity poses a challenge as the demand for skilled security experts far surpasses the supply. Copilot can assist teams in maximizing their effectiveness and enhancing their abilities through detailed, step-by-step instructions for mitigating risks.
Copilot endeavors to combine machine learning and human intelligence into a cohesive system that helps organizations of all sizes effectively manage threats using a software service. Microsoft uses AI to track threat actors' activities and assess the risk of an attack by monitoring and analyzing input from detection systems, customer input, and response data. Then, Microsoft Security Research Center (MSRC) analysts, can efficiently verify and evaluate the impact of independent research submissions into their bug bounty portal using AI and ML tools, reducing the security burden on individual organizations.
With Security Copilot, Microsoft enables their customers' security teams, threat hunters, and malware analysts to collaborate in real time, investigate threats, and improve response times by creating playbooks and procedures based on previous incidents and responses.
IBM is leveraging the power of Watson AI technology in their flagship security incident and event management (SIEM) platform with a solution called QRadar Advisor.
So how does it work? QRadar Advisor is an AI assistant that helps security operations centers (SOCs) keep up with a flood of information by automatically chaining together different incidents in a way that helps analysts have an eye on the big picture and not mistakenly dismiss an event.
A security operations center (SOC), also known as an information security operations center (ISOC), is a team of IT security professionals who work either internally or externally to monitor an organization's entire IT infrastructure around the clock. Their main objective is to identify cybersecurity incidents in real-time and respond to them swiftly and efficiently.
By automating key practices in their SOC, QRadar can help organizations address these common challenges:
More Threats and Not Enough Time to Spot Them - Valuable information often goes unnoticed because analysts struggle to connect the dots. This makes it challenging to derive actionable insights, leading analysts to focus only on cases they feel confident about. Unfortunately, this approach can result in missed investigations and expose the organization to risks.
Information Overload - The sheer volume, variety, and speed of insights to analyze make it difficult to prioritize work and identify the root cause of issues. This challenge affects companies of all sizes. Analysts struggle to piece together local context swiftly, leaving them overwhelmed by repetitive tasks.
Dwell Times - Dwell time, which refers to the duration between a security incident occurring and its detection and response, is a significant metric relied upon by security experts to evaluate their effectiveness in safeguarding and defending data. Specifically, two key measurements, namely MTTD (mean time to detect) and MTTR (mean time to respond), are widely utilized to assess this success. Despite the availability of more solutions and data, the average dwell time today can range from 50 to 200 days. The lack of consistent, high-quality investigations with contextual information contributes to a breakdown in existing processes, heightening the risk for organizations.
Shortage of Cybersecurity Talent and Job Fatigue - Security analysts often find themselves overworked, understaffed, and overwhelmed due to the expanding threat landscape and daily operational tasks. As the data continues to grow exponentially, the skills gap widens, and the problem will grow larger too.
The primary advantage of automating parts of your SOC is that it brings together and coordinates an organization's security tools, practices, and incident response. This integration typically leads to improved preventive measures, enhanced security policies, faster detection of threats, and quicker, more effective, and cost-efficient responses to security incidents. Moreover, a SOC can boost customer confidence and simplify compliance with industry, national, and global privacy regulations. The solution drives consistent response, prioritizing the most severe alerts and mapping an attacker's actions to the MITRE ATT&CK framework.
The MITRE ATT&CK framework, developed by MITRE Corporation, is a comprehensive knowledge base that catalogs real-world tactics, techniques, and procedures used by attackers in cyber intrusions. It offers a structured and standardized approach to analyzing different stages of attacks and covers various threat vectors like network, endpoint, cloud, and mobile platforms. ATT&CK consists of a matrix organizing attacker tactics and techniques, providing insights into goals and methods. Widely used by cybersecurity professionals, it enhances threat detection and response capabilities, helping organizations understand adversaries' tactics, develop effective defenses, and improve overall cyber resilience.
The implementation of AI in threat intelligence has gained significant traction, as demonstrated by case studies of prominent companies such as Google, Microsoft, and IBM. Other notable security vendors like Cisco, CrowdStrike, and Palo Alto, are also leveraging AI and ML technologies to improve detection and stop attacks against their customers. These organizations leverage AI to enhance their threat intelligence capabilities, enabling them to analyze vast amounts of security signals, detect potential threats, and respond swiftly.
The collaboration between Google and Mandiant brings together Google's data analysis capabilities with Mandiant's expertise, offering advanced cyber defense solutions and incident response guidance. Microsoft's AI-powered tool, Security Copilot, addresses complexities, evasion tactics, and the talent gap faced by security analysts, facilitating proactive threat management. IBM utilizes Watson AI technology in its QRadar Advisor solution, automating key practices in security operations centers (SOCs) to address challenges like information overload, dwell times, and cybersecurity talent shortages. The integration of these AI-driven approaches with the MITRE ATT&CK framework further enhances organizations' ability to detect, respond to, and defend against cyber threats, ultimately promoting a safer digital environment.
As you move through the exercises in this course, consider these case studies and how AI tools might help to address some of these challenges for your organization.
Google Cloud AI Threat Intelligence
Microsoft Security Copilot IBM Security
Palo Alto Networks- The Value of AI/ML in Security Environments: Getting Beyond the Hype
