AI and LLMs are transformative, and continue to enrich and permeate our digital lives, the importance of planning incident response and detection specific to these platforms for security teams cannot be overstated.

Speaking to my network of security professionals, the prospect of malicious actors exploiting vulnerabilities in production AI systems sends a shiver down even the most seasoned of security professional’s spines.

It was from these conversations I decided to venture into the nuances of detecting incidents around AI platforms, providing guidance on what to look for and how to respond, this isn’t using AI in incident response, but responding to incidents with AI (LLMs), the former is a topic for a future blog, maybe.

Traditional notions of an “incident” take on a new dimension with AI platforms. Gone are the days of simple malware detection or network intrusion. AI systems introduce a plethora of complexities as I have raised in previous posts, from data manipulation to model poisoning. A comprehensive understanding of these threats is essential to effective incident response.

The Anatomy of an Incident, the Subtle Deviations from Expected Behavior

Incidents around AI platforms often manifest as subtle deviations from expected behavior. These anomalies can be difficult to detect, as they may not conform to traditional signatures or patterns. For example, an attacker might inject fake data into an AI-powered recommendation system, causing it to recommend high-risk investments to unsuspecting customers. In this case, the anomaly is not a sudden spike in activity, but rather a gradual shift in behavior that can be difficult to detect.

An Eye for Detail

To identify potential incidents, security professionals must have a keen eye for detail and be able to analyze large amounts of data quickly. This requires a deep understanding of the system’ s underlying mechanisms, including the algorithms used to process data, the relationships between different components, and the expected patterns of behavior.

Unusual Patterns in Data Processing

Unprecedented spikes in request volumes are always a good indicator of activity that needs investigation. If a system is suddenly receiving a large number of requests that are outside the normal range, it may indicate an attempt to overwhelm or distract the system. Atypical distribution of input data is another pattern. If the input data to a system is significantly different from what is normally expected, it could indicate an attempt to manipulate or deceive the system. Aberrant model predictions, automation is key here to evaluate expected responses. If a system is consistently making predictions that are outside the normal range or not consistent with known patterns, it may indicate an attack on the system.

Complex Interdependencies between Components

AI- powered systems often rely on complex interdependencies between components. For example, a natural language processing (NLP) model might rely on multiple algorithms to process text data, including tokenization, part-of-speech tagging, and sentiment analysis. If one of these algorithms is compromised, it can have far-reaching consequences for the entire system.

A Single Point of Failure

When investigating an incident, security professionals must consider the intricate relationships between modules, models, and algorithms. A single point of failure in one component can have cascading effects on the entire system, making it even more difficult to detect and respond to incidents.

The Response Protocol

Once an incident is detected or suspected, a swift and effective response is paramount. The following steps serve as a guiding framework for incident response: Isolate the affected system; gather intelligence on the incident’s scope, timeline, and any observed behavior; identify root causes by analyzing the incident’s underlying mechanisms; and develop a mitigation plan based on findings.

A good incident management platform really helps co-ordinate everyone and gather intelligence from across the business here.

Analyze system logs and data to identify unusual patterns or behavior.

Review system configuration files and settings to ensure that they are consistent with known best practices and what development teams expect.

Mitigation Strategies

The specific measures taken will depend on the nature of the incident. Some possible approaches include updating models or algorithms to address identified vulnerabilities, implementing additional monitoring and logging mechanisms to detect similar incidents in the future, or conducting thorough risk assessments to identify potential attack vectors.

This is not easy when you have a live incident on a SaaS platform using a AI model, business continuity planning and failover strategies are essential. Detection demands a nuanced understanding of these complex systems. By recognizing unusual patterns, isolating affected components, and developing targeted mitigation strategies, security professionals can effectively respond to these threats.

What could this look like?

Hypothetically, take a completly made up fintech bank, “Digital Bank” (DB), uses an AI- powered system to analyze customer transaction data and predict financial behavior. The system, called “Financial Insights” (FI), is trained on a large dataset of customer transactions and uses machine learning algorithms to identify patterns and make predictions.

One day, DB’s security team notices that the FI system is consistently recommending high-risk loans to customers who are actually low-risk borrowers. The system is producing false positives at an alarming rate, leading to unnecessary loan approvals and potential financial losses for the bank.

Incident Response: The incident response team (IRT) is notified of the issue and begins to investigate. They quickly realize that the issue is not related to a software bug or hardware failure, but rather appears to be an intentional attack on the FI system.

The IRT starts by:

• Isolating the affected system, immediately quarantine the FI system to prevent further damage.
• Gathering intelligence, reviews logs and monitors system activity to understand the scope of the attack. They discover that an unknown actor has been injecting fake data into the FI system, causing it to misclassify customers.
• Identifying root causes, analyzes the incident’s underlying mechanisms and determines that the attacker used a combination of social engineering and technical expertise to gain access to the FI system.
• Developing a mitigation plan, recommend implementing additional authentication and authorization controls to prevent similar attacks in the future. They also suggest retraining the FI system using more robust machine learning algorithms.

Mitigation Strategies:

The IRT implements the following measures:

• Implementing multi-factor authentication (MFA) for all users accessing the FI system.
• Enforcing stricter access controls on the FI system, limiting access to authorized personnel only.
• Conducting regular security audits and vulnerability assessments to identify potential weaknesses in the FI system.
• Developing a comprehensive incident response plan that includes procedures for detecting and responding to similar attacks in the future.

Lessons Learned:

The IRT learns several valuable lessons from this incident:

• The importance of monitoring system activity and logs to detect unusual behavior.
• The need for robust authentication and authorization controls to prevent unauthorized access to sensitive systems.
• The value of retraining machine learning models using more robust algorithms to reduce the risk of misclassification.

This fictional example illustrates how incident responders might respond to an attack on an AI-powered platform in fintech banking, including identifying the root cause of the issue, developing a mitigation plan, and implementing measures to prevent similar attacks in the future.