Artificial intelligence (AI) is rapidly transforming industries, reshaping everything from business processes to healthcare, finance, and cybersecurity itself. As AI-powered systems become more widespread and essential, they also become attractive targets for cyberattacks. While traditional cybersecurity controls, such as firewalls, encryption, and intrusion detection systems, play a crucial role in protecting digital infrastructure, they are insufficient to defend against the unique risks and vulnerabilities in AI systems. To effectively safeguard AI models and applications, organizations must go beyond conventional security measures and adopt a holistic, security for AI-specific approach to manage the risks.

The Value and Vulnerabilities of AI

AI systems process vast amounts of data, make decisions, and often operate autonomously. As AI becomes more embedded in critical applications like healthcare diagnostics, autonomous driving, and financial trading, the vulnerabilities create a serious concern. Even traditional predictive AI used for years in many industries is at risk and can cause material impact to organizations if compromised. These AI-specific vulnerabilities can arise at different stages in the AI lifecycle, including:

• Data Poisoning: AI models are trained on large datasets, and if adversaries can manipulate or corrupt this data, they can affect the model's performance and accuracy.
• Model Inversion: Attackers can exploit an AI system's outputs to reverse-engineer sensitive information used in its training, leading to privacy breaches.
• Adversarial Attacks: AI systems, particularly in image and speech recognition, are vulnerable to adversarial examples—inputs subtly altered to trick the AI into making incorrect predictions without raising human suspicion.
• Model Extraction: Cybercriminals can extract and steal intellectual property by querying a machine learning model repeatedly to infer its underlying algorithms and parameters.

Given these unique threats, traditional cybersecurity controls fall short of providing comprehensive protection for AI systems. 

Why Traditional Cybersecurity Controls Are Necessary

Traditional cybersecurity measures like encryption, firewalls, access control, and intrusion detection systems remain essential as a first line of defense against general cyberattacks.

These tools are invaluable for protecting the broader infrastructure where AI systems operate. Here are some of the fundamental roles these controls continue to play:

1. Perimeter Defense: Firewalls, network segmentation, and virtual private networks (VPNs) provide necessary barriers to unauthorized access. While these don’t address AI-specific threats, they create a strong baseline security posture to prevent general attacks, like ransomware or distributed denial of service (DDoS) attacks, which can still impact AI systems.

2. Access Control and Authentication: Identity and access management (IAM) protocols, multi-factor authentication (MFA), and privileged access management (PAM) are essential for ensuring that only authorized users and applications can interact with AI models and the systems they run on.

3. Data Encryption: Secure transmission and storage of data are critical, particularly when AI models are handling sensitive information like medical records or financial transactions. Traditional encryption methods protect this data from being intercepted or tampered with during transit or at rest.

4. Security Monitoring: Security information and event management (SIEM) tools, along with intrusion detection/prevention systems (IDS/IPS), offer continuous monitoring for abnormal behavior in networks and applications. While they may not detect AI-specific attacks like data poisoning, they can still identify broader threats targeting the underlying infrastructure.

Where Traditional Cybersecurity Falls Short

Despite their importance, traditional cybersecurity controls are insufficient for protecting AI systems from AI-specific threats. Below are some of the key gaps:

1. Lack of Model-Specific Protections: Traditional controls are not designed to protect the intricacies of AI models, such as their training data, model architectures, or prediction outputs. This leaves AI systems vulnerable to model extraction, inversion, and adversarial attacks. Without specialized safeguards, attackers can exploit these vulnerabilities to manipulate or steal AI models.

2. Inability to Detect Adversarial Inputs: Conventional security tools are typically not equipped to recognize adversarial examples—subtle perturbations in input data designed to fool an AI system. For instance, a slightly altered image might trick an AI-powered security camera into misidentifying an intruder as a harmless object. Traditional IDS or antivirus software would likely overlook this type of sophisticated attack.

3. Inadequate Focus on Data Integrity: AI systems are highly dependent on the quality and integrity of their training data. Traditional cybersecurity often focuses on protecting data at rest or in transit but doesn’t offer mechanisms to ensure the validity of the data used to train AI models. This makes AI models particularly vulnerable to data poisoning, where malicious actors manipulate the training data to corrupt the model.

Adapting Cybersecurity for AI

Organizations need to integrate AI-specific cybersecurity strategies alongside traditional controls to effectively manage and mitigate attacks on AI models. Some key strategies include:

1. AI Model Monitoring and Validation: Continuous monitoring of AI model inputs and outputs can help detect anomalies or unexpected behavior that could indicate an attack, such as model drift or data poisoning. Validating the training data's integrity and the models' robustness against adversarial examples should become standard practices.

2. Adversarial Robustness Testing: Organizations should subject their AI models to adversarial robustness testing, simulating adversarial inputs to evaluate how the model reacts. This helps identify vulnerabilities that attackers might exploit and improve model resilience.

3. Collaboration Between AI and Security Teams: AI development teams and cybersecurity professionals must work closely together. AI experts can help identify potential vulnerabilities in models, while cybersecurity teams can implement safeguards to protect against both conventional and AI-specific attacks.

The Coalition for Secure AI: A Coordinated Response to AI-Specific Threats

As the digital world shifts toward AI-driven processes, CoSAI plays a vital role in setting security standards and driving awareness of AI-targeted vulnerabilities. CoSAI’s collaborative approach combines expertise from AI research, cybersecurity, academia, and industry, creating a comprehensive framework to combat AI-specific threats. These efforts are critical to protecting AI systems not only from traditional cyber risks but also from novel attack types that exploit AI’s unique vulnerabilities, which are often overlooked by conventional defenses.

While traditional cybersecurity controls remain vital, they do not safeguard AI models from the unique AI model risks we all face. The complexity and evolving nature of AI-specific threats demand a new set of tools and strategies to complement conventional defenses. CoSAI’s initiatives empower organizations to proactively address these threats, safeguarding their AI investments from increasingly sophisticated cyberattacks. With CoSAI’s comprehensive frameworks and best practices, organizations can better protect their AI investments from increasingly sophisticated cyberattacks by adopting a proactive, AI-tailored approach to cybersecurity.

CoSAI welcomes and encourages open technical participation from all developers. You can follow our code, documentation, and contributions through GitHub. For any questions or further information, please contact us at info@oasis-open.org.