A Strategic Blueprint for Leveraging AI and Automation in Cybersecurity Incident Response

Grab your favorite cup of joe and settle in, because today, we're diving deep into the world of security automation, hyperautomation, and AI—faster than your espresso machine on a Monday morning. Co-authored with Yossi Barishev, this article is like your first caffeine hit of the day; it's eye-opening, energizing, and might just make you see the world of SecOps a little differently.

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A Sip of Our Use-Case Intro

Imagine, if you will, your Endpoint Detection and Response (EDR) system springing to life, not unlike your coffee maker at the crack of dawn. An alert pops up—oh, the digital aroma of potential trouble brewing! What follows is an intricate dance of automation, whisking together alerts, frothing up analyses, and pouring them into neat, group-sized cups. This process is so rich and full-bodied, it even sifts through the grounds of past alerts to ensure no flavor of threat is missed.

Yossi Barishev , the barista of cyber security, experimenting with a bold new blend where AI doesn't just complement the human element; it seeks to become the main brewer. With a meticulous selection of historical incident beans, this AI-powered machine is designed to serve up automated remediation steps with the precision of a skilled barista crafting the perfect latte.

So, let's not let our coffee get cold. Dive into this piece where we explore how the integration of these technologies could mean a smoother, richer, and more robust SecOps experience. Because, in the end, who doesn't love their security operations like their coffee? Strong, automated, and capable of keeping you safe—er, I mean, awake—through the longest of days.

Automating the Alert Handling

Focusing on a ransomware infection scenario, let's delve into the steps for automated alert handling, assuming a true positive alert:

Security Incident Identification Phase (Trigger and Validation):

• SIEM Alert Generation: This stage initiates with an alert from the Security Information and Event Management (SIEM) system. The alert, indicating suspicious outbound activity, results from complex correlation searches. These searches may use heuristic analysis, signature-based detection, and behavioral analytics to identify unusual activities such as abnormal data transfer rates, connections to malicious IPs, or unexpected encryption processes typical in ransomware attacks.

• Alert Transfer to Case Management Platform: The alert is then routed to a Case Management Platform. This platform serves as a central point for incident tracking and management, starting the automated response protocol for ransomware incidents.

Security Automation Initiates

• Information Enrichment: This involves enrichment for the user and device. Automated scripts or API integrations are used to gather comprehensive user information, including login history, recent activity, and network traffic patterns. This step is crucial for understanding the context of the alert and potential attack vectors.

• Yossi suggests advancing the concept by establishing an enrichment data lake. This data repository would enable automation and AI platforms to query data contextually, with the context rooted in the initial alert. For instance, if there's an indicator of event X occurring on machine Y, the context engine would query all available data pertaining to X or machine Y, subsequently integrating this information into subsequent processes.

• Process Identification: The focus here is to identify the process responsible for the suspicious connection, capturing a sample of the process or file for malware analysis. Techniques such as sandboxing may be used to safely analyze the behavior of the suspected ransomware. Here I would suggest to look at the post for Orchestrating malware analysis. [Automated Malware Analysis Framework]

• Suspicious Process/File Enrichment. : Identifying Related Alerts: The system searches for other security alerts triggered by the same user or device, which could indicate a broader attack pattern or a more extensive compromise.

• Detecting Other Affected Devices: Analysis of IoCs extracted from the initial host is conducted to identify other devices in the network that might have encountered similar threats. This step is vital for understanding the extent of the attack and preventing further spread.

• Incident Synthesis: In the final step, an automated process aggregates all the findings into a single incident report. This report includes all relevant information such as the extent of the attack, systems affected, potential data breaches, and initial recommendations. This summary is then escalated to a Security Analyst or Incident Responder for further analysis and decision-making.

Response and Remediation with AI Integration

After preparation, a security expert typically evaluates the compiled information to decide on a response. Integrating AI at this stage could significantly augment decision-making. In this section Yossi provides detailed insights on how AI can autonomously suggest effective remediation strategies based on the analysis.

1. Analysing report data: AI excels at processing extensive volumes of raw textual data, adeptly contextualizing and extracting pertinent facts or making informed deductions from it. This proficiency enables the automation of decision-making processes in the ingestion of security reports. Through meticulous prompt engineering or the pre-training of models, AI can accurately assess whether a specific payload is malicious based on a malware analysis report. It evaluates whether the initially triaged data truly indicates malicious activity by comparing it against a database of known Indicators of Compromise (IoCs) or matching it to pre-identified patterns of malicious behavior.Essentially, with the appropriate training data, carefully crafted prompts, and comprehensive data enrichment, AI can be leveraged to make nuanced determinations about the nature of a threat. This is achieved through its ability to analyse statistical distributions and make predictions, rather than relying on conventional logic. This approach significantly enhances the efficiency and accuracy of identifying and responding to cyber threats, transforming the landscape of cybersecurity incident analysis and response.

2. Decision tree abstraction: Most security teams already heavily utilize playbooks & runbooks. When given a certain incident, an analyst will typically run along the relevant playbook to remediate the incident. In essence, all playbooks are glorified decision trees - they tell us that if X happened, or if 1,2,3 are the boundary conditions, then these should be your actions. Since AI is highly capable of distilling the essence from any given text, we can utilize AI to act upon playbooks, runbooks, and IR Plans in order to reduce the need for human intervention & decision-making in incident handling. In essence, this allows us to construct decision trees for an N amount of incidents, given an N amount (or less) of playbooks.

Automated Response and Remediation Steps

Once the AI-driven system provides remediation guidance, a series of automated steps can be initiated to mitigate the ransomware threat efficiently:

• Quarantining Affected Hosts: This step involves isolating compromised systems from the network to halt the spread of the ransomware. Automated scripts or network management tools can be utilised to immediately disconnect affected hosts, preventing further data encryption or lateral movement within the network. Quarantine actions might include disabling network interfaces or dynamically modifying firewall rules to restrict traffic to and from the infected systems.

• Updating Threat Intelligence Repositories: In this phase, IoCs (Indicators of Compromise) identified during the incident are automatically sent to threat intelligence platforms. This could be achieved through API integrations between the incident management system and various threat intelligence databases. The shared IoCs help in enriching the global threat intelligence pool, enabling other organizations to preemptively identify and block similar ransomware attacks. This step often involves updating information about malicious IPs, domains, file hashes, and anomalous patterns of behavior related to the ransomware.

• Blocking and Mitigation: Implementing immediate blocking measures at both the host and network levels based on the identified IoCs is crucial. This might involve automatically updating firewall rules, IDS/IPS signatures, and endpoint security configurations to prevent the execution of the ransomware or communication with its command and control servers. The automation here is pivotal for rapidly deploying defenses across the organization, significantly reducing the window of opportunity for the ransomware to inflict further damage.

• Collaboration with CyberSec Content Engineering and Threat Hunting Teams: The final step involves automated sharing of the incident insights with Cybersecurity Content Engineering and Threat Hunting teams. This includes detailed information about the ransomware’s TTPs (Tactics, Techniques, and Procedures), enabling these teams to develop or refine detection and prevention rules. Automated systems can facilitate this by generating detailed incident reports and pushing them to collaborative platforms or ticketing systems used by these teams. This collaboration is essential for continually improving the organization's defensive measures against evolving ransomware threats and enhancing overall cybersecurity posture.

Each of these steps represents a critical component of a comprehensive, AI-enhanced automated response and remediation strategy. By implementing such a strategy, cybersecurity teams can significantly improve their efficiency and effectiveness in combating ransomware and other sophisticated cyber threats.

Future Automation Opportunities

In our upcoming article, we will explore how the Threat Hunting process and CyberSec Content Engineering can be further automated, leveraging these advanced technologies. This future exploration promises to unveil new horizons in proactive cybersecurity measures.

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