The development, amendment, and adoption of artificial intelligence (AI) is faster than ever thanks to cloud computing and advancements in the computational capacity of machines. The emergence of AI chatbots that can mimic human conversations was a game changer. As per Forbes, the global AI market is expected to cross US $1,800 billion by 2030.

AI unlocks limitless opportunities for all industries. However, it also presents an array of challenges for organizations. Thus, it is important for enterprises to forge a robust AI strategy to meet their goals.

AI Under Attack

Enterprises are embracing AI models to achieve high performance outcomes, improve decision-making capabilities, and deliver truly differentiated experiences. However, these models are prone to adversarial attacks during various phases of the development lifecycle.

Some of the common adversarial AI attacks are:

• Backdoor attack: Modern utilities are designed to make accurate decisions in real-time for smooth business operations. For instance, self-driving cars can classify traffic signals and make autonomous decisions based on external data on the basis of the underlying models that are being trained intensively. Attackers target the training data and manipulate it to misinterpret objects. This kind of attack is known as a backdoor attack, and it can affect the accuracy of decisions made by AI models. Such attacks have high implications, from the financial as well as societal perspectives.
• Transfer learning attack: Today, there are many pre-trained machine learning (ML) models that perform specific tasks and are readily available in the market. These models can be efficiently used by other ML models to rapidly scale operations. Enterprises have adopted this ‘transfer learning’ practice to reduce the cost and time spent in training new models. However, attackers can introduce malicious data into the pre-trained ML model, which can be transferred just as easily as the training knowledge. This is called a ‘transfer learning’ attack. It reduces the accuracy and performance of the new ML model.
• Model extraction attack: As organizations increasingly adopt ML models, they are vulnerable to model extraction attacks. Here, an attacker tries to steal the model’s functionalities by carefully crafting inputs to acquire an output containing information about the model’s training data.

Combating AI Adversarial Attacks

Enterprises realize that the threats posed by AI models do not outweigh the multifold benefits they deliver. Hence, there is a pressing need for a tactfully drafted quality engineering strategy that will foil any adversarial attacks. The five pillars of a test-driven and well-structured strategy are:

• Security controls: Organizations should focus on implementing strict security controls such as identity management, authentication, and access control policies throughout the AI lifecycle.
• Integrity check: AI developers must ensure that the data used for training is legitimate and without any malicious elements. Once validated, the data must be hashed so that whenever it is required in future its integrity can be checked. Moreover, pre-trained models should only be sourced from trusted resources that provide proper integrity checks.
• Security incident response: AI systems need to be equipped with clear paths to detect and respond to incidents. ML models too should be subject to regular security audits and monitoring.
• Cyber-resilience strategy: The AI architecture and operational processes must follow cybersecurity best practices to build cyber-resilience in all the applications that have been developed.
• Robustness: Enterprises should identify potential weakness by testing and validating the training data, source code, and components used to build AI systems. This will ensure that the AI system is well-secured and resilient in real time in the face of actual adversarial attacks.

Creating Responsible AI

Enterprise adoption of ML models is growing exponentially, making it imperative to set standardized benchmarks for a well-synchronized quality engineering strategy. Adversarial AI attacks have significant financial and societal implications and can damage the reputation of organizations. Hence, there is a need for a testing approach that ensures the integrity and reliability of AI models by leveraging best practices while keeping security at the core. By prioritizing the principles of security testing and deploying the right defense mechanisms, enterprises can navigate the disruptive and rapidly evolving technological landscape. The key objective must be to foster the growth of responsible AI and harness its full potential across diverse domains and use cases.