Securing AI systems with AIShield and QRadar

In the evolving landscape of artificial intelligence, the emergence of AI attacks (adversarial machine learning) represents a nuanced and complex challenge distinct from traditional cybersecurity threats. Such attacks exploit vulnerabilities inherent to AI systems and manipulate or compromise machine learning models, thereby undermining the foundational principles of confidentiality, integrity, and availability. Unlike conventional cyber threats, AI attacks possess a sophisticated ability to induce model misbehavior, extract sensitive data, or degrade the performance of AI applications, leading to a significant erosion of trust in AI systems.

A concerning trend highlighted by a Gartner survey reveal that two in five organizations have reported security breaches related to their AI initiatives, marking a critical juncture in the parallel growth of AI capabilities and their associated risks. This data underscores an urgent need for a robust security framework capable of addressing the unique challenges posed by AI attacks.

In response to this emerging threat landscape, the integration of AIShield with IBM QRadar can prove as a pivotal strategy. This collaboration is designed to fortify AI applications against sophisticated threats, ensuring the security teams can safeguard the three main tenets of security—confidentiality, availability, and integrity—within the realm of AI development and deployments. By delving into the capabilities of AIShield and its seamless interoperability with IBM QRadar, this article aims to illuminate the path forward for cybersecurity teams tasked with the protection of AI-driven applications, offering a comprehensive framework to mitigate the risks across the AI lifecycle as highlighted in the recent IBM blog.

The growing dependence on AI across industries such as banking, healthcare, telecommunications, automotive and manufacturing makes it a prime target for malicious actors, posing severe threats to safety, privacy, and data integrity. Insights from a survey conducted by The Economist Intelligence Unit Survey, (47%) of bankers in financial services surveyed say that security and privacy breaches pose the greatest risk associated with AI adoption, followed closely by the failure of AI systems (41%).

The expanding risk to AI systems is a serious concern, as attacks can cause significant harm. Fig. 1 illustrates the kinds of damage that AI attacks can do, highlighting why it's crucial to focus on securing AI systems.

Organizations using AI expect their security measures to not only protect against AI attacks but also to fit seamlessly with their cybersecurity functions, meet regulatory requirements, and enhance post-deployment oversights. In the world of Security Operations (SecOps), the increasing variety of threats, including those targeting AI, brings specific needs:

• Security teams should have access to efficient tools designed for AI, like endpoint detection and response (EDR) systems that work well with their current security information and event management (SIEM) platforms. These tools are essential for improved threat intelligence and dealing with new types of AI attacks, helping to lower the mean time to detect (MTTD) and mean time to react (MTTR) security issues, which are critical metrics for security leaders.
• Managers need a clear view of how secure their AI and ML models are. This visibility is crucial for ensuring good governance, staying compliant with regulations, and managing the risks associated with their models at the organizational level.

With the expansion of threat vectors that also includes AML attacks, the following are the key requirements for security teams in organizations deploying AI:

1. Guard: As it currently stands, analyzing and identifying threats are exceedingly challenging with the sophistication of AI attacks. Organizations need the capability to identify sophisticated threat incidents such as an AI attack.

2. Intelligence: The ability to decipher actionable insights from the threat data and be able to better classify the risks. Tracking patterns becomes imperative for optimizing security workflows and establishing resilient models.

3. Speeds: The capability to detect and accurately identify AI security incidents, alert the relevant stakeholders timely and reduce the time to detect and remediate such attacks.

AIShield is a robust AI security solution, dedicated to safeguarding organizations running AI and ML workloads. Its comprehensive AI security platform offers proactive measures to mitigate AI security risks both pre- and post-deployment, ensuring AI systems remain resilient against adversarial manipulation. Leveraging a fully self-serve model, AIShield empowers organizations to detect, protect, and defend their AI-based workloads across diverse environments, including cloud and edge.

AIShield offers an API-based AI security vulnerability assessment, which can be used in the development workflow by developers to assess the security posture of their AI application. It delivers vulnerability assessment report and a threat informed defense model (see Fig. 2). The threat-informed defense model can be deployed within the model deployment environment for real-time detection of any attack vector. The defense model is trained to bifurcate the effective attack vectors from the original data. This ensures organizations have the capability to guard against AI attacks (see Fig. 3).

In cybersecurity, Security Information and Event Management (SIEM) consists of technologies that provide analysis, threat mitigation and logging of security events and packets of data flowing in the wire within a network. SIEM provides a general view of all technical infrastructure, with specific data of security events and flows and the mitigation of any security threat vectors that are found in the environment. A SIEM solution responds to advanced threats which cannot be analyzed with general monitoring tools.

IBM QRadar SIEM is one of the most popular SIEM solutions in the market today among larger organizations in IT & Services, financial services, and security industries. It has been the Leader in the Gartner’s Magic Quadrant for more than a decade now. It helps you to quickly uncover existing and potential threats by using its advanced intelligence capabilities. It also provides many useful features such as centralized visibility, flexible deployment, automated intelligence, machine learning, and pro-active threat hunting.

AIShield Platform provides the defense model which adds the much-needed capability to detect an attack on AI system. The integration of AIShield and IBM QRadar transforms Security Operations Center (SOC) efficiency by streamlining the detection and prioritization of AI security threats. This alliance addresses the challenge SOC analysts face with alert overload and manual tasks that hinder their ability to identify critical events swiftly and accurately. AIShield's specialized AI threat detection capabilities, combined with QRadar's advanced SIEM analytics, offer a unified, powerful solution that significantly reduces the volume of threats and focuses on those most relevant to the organization.

By intelligently filtering relevant intelligence and synthesizing threat data into a single, normalized view, this collaboration enables SOC teams to understand threat contexts more clearly and act decisively. The AIShield and QRadar integration enhances real-time monitoring of AI assets, providing security teams with vital insights through a dynamic dashboard into AI security incidents and prioritizing alerts based on severity and potential impact. This focused approach allows for a more efficient allocation of resources, enabling quicker detection and remediation of AI-related threats, thereby safeguarding enterprise operations against sophisticated cyberattacks.

In essence, AIShield and IBM QRadar together empower SOC teams with the capability to not just react, but proactively manage and mitigate AI security risks, ensuring a comprehensive understanding of and resilience against potential threats to the enterprise.

Few leading global banks managing billions of transactions annually grappled with the rising threat of AI attacks to their transaction fraud detection AI algorithms, leading to substantial risks to operations and profitability. To fortify defense against AI attacks, the banks now leverage cutting-edge AI security measures, ensuring the resilience of their systems against adversarial manipulation. By deploying advanced AI-based threat detection mechanisms, banks can proactively identify and mitigate potential threats, safeguarding its sensitive financial data. The incorporation of threat-informed defense models, such as one provided by AIShield, enables the banks to effectively guard against sophisticated AI attacks, ensuring the integrity of its operations.

Harnessing the power of advanced analytics, the banks can gain valuable insights into evolving threat landscapes, empowering proactive risk classification and strategic decision-making. By leveraging solutions like QRadar SIEM, such banks can analyze vast volumes of security event data in real-time, identifying patterns indicative of potential security threats to their AI systems.

By seamlessly integrating AIShield's API-based vulnerability assessment into its development workflow, banks can swiftly assess the security posture of their AI applications, enabling timely detection and mitigation of potential vulnerabilities. Additionally, leveraging QRadar SIEM's advanced intelligence capabilities, the bank can automate threat detection and response processes, reducing the time to detect and remediate AI security incidents, minimizing potential financial losses and preserving the bank's reputation in the highly competitive financial sector.

In conclusion, securing AI systems against sophisticated attacks is crucial as their deployment becomes increasingly ubiquitous. The integration of AIShield and IBM QRadar offers a forward-thinking solution, providing the necessary tools for real-time threat detection, endpoint security, and compliance assurance. This strategic alliance not only fortifies AI models against emerging threats but also supports organizations in maintaining the integrity and reliability of their AI-driven operations. Embracing these technologies ensures that businesses can continue to innovate safely in the AI space, safeguarding their advancements and competitive edge in an ever-evolving digital landscape.

Should you have any queries or require further assistance, feel free to reach out to us via email at either niranjan.nikita@in.bosch.com or ashish.kothekar@in.ibm.com.