Artificial Intelligence (AI): Security and Privacy Implications, Challenges, and the Way Forward for Organizations

Almost every organization is either using or exploring the use of AI, especially Generative AI or GenAI — including large language models (LLM) such as ChatGPT and Gemini. However, as AI becomes part of our daily lives, it also brings significant security and privacy concerns as more applications and devices store and process our personal data for various purposes. AI's global adoption has led to many security risks and flaws, leading to several data breaches and adversarial attacks on AI models to which we have been eyewitnesses. It is apparent, and one cannot ignore the undoubted need to address these challenges to ensure that AI technologies remain secure, ethical, and trustworthy. This article highlights AI's security and privacy implications, challenges, and optimum solutions that can guide organizations in the right direction.

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Statistics: How Has AI Usage Grown

Over the last couple of years, we have witnessed the widespread adoption of AI at an unprecedented rate. One cannot deny the incorporation and close linking of AI systems into mainstream business functionalities across distinct industries and sectors. However, modern research highlights the security and privacy-related risks and flaws in AI models. Adding to the concern is organizations' blindfolded trust in AI models when handling sensitive data.

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• According to the statistics, the global AI software market revenue currently stands at $100 billion and is bound to increase in the consequent years.
• According to the Report released by Immuta, approximately 80% of data professionals believe existent AI models are challenging data security protection and making it difficult to safeguard it.
• The Cisco 2024 Data Privacy Benchmark study states that over 90% of enterprises have positively realized the significance of reassuring their customers about how AI closely handles their sensitive data while maintaining transparency.

How are Cyber Adversaries are Using AI?

As the AI technology advances, scammers are taking advantage of these tools to deceive their victims. For example, in a recent incident, three Canadian men fell victim to deepfake videos of Justin Trudeau and Elon Musk. Believing the videos were real, they ended up investing $373,000 and losing it all. It's a stark reminder of how important it is to stay vigilant and verify the authenticity of what we see online. In another example of AI-driven cyber-attacks, hackers leveraged botnets under AI control to employ a distributed denial-of-service (DDoS) attack on TaskRabbit’s servers (TaskRabbit is an online platform connecting freelance handymen and clients). These are just a few examples out of many, but are enough to prove that adversaries are increasingly leveraging AI-ML to entice individuals and organizations.

Security and Privacy Risks of AI and Challenges Facing Organizations

Artificial intelligence systems and models are highly susceptible to adversarial attacks. For example, tailored inputs can be injected into the models to disrupt their logical workings and eventually hamper generated outputs. AI-powered malware can learn and adapt to well-known attack signatures that detect and identify malicious software, making it more complicated for organizations to safeguard their information systems. Data poisoning introduced during the training phase of the AI model might lead to the generation of unreliable predicted outputs. Also, AI can assist in automating hacking and phishing attempts, enhancing the speed and scope of social-engineering attacks.  However, non-coompliance or ethical use of AI can also be a concern facing organizations. In May 2022, the UK Information Commissioner’s Office (ICO) fined Clearview AI (a US-based facial recognition firm) £7.5 million for illegally collecting and storing images from social media without consent. The growing utilization of AI might necessitate organizations to reconsider the current privacy protection measures. For example, the improper handling and usage of biometric data, such as facial and fingerprint scans, can have detrimental effects if compromised. Italy recently banned ChatGPT, in April 2024, due to privacy concerns raised by the Italian data protection authority. The regulator announced an immediate ban and investigation into OpenAI.

Despite its shortcomings, building trust in AI systems is paramount. Given the risks that can sometimes outweigh the benefits, organizations may have to deal with increasing and ever-changing local and global privacy regulations and scrutiny regarding the use or development of AI applications and systems. Organizations must recognize that building AI systems that are ethical, transparent, and unbiased is a major challenge to maintain trust in the AI system's capability to make decisions and meet regulatory requirements. For example, Google was recently fined €250m by French regulators for breaching an agreement to pay media companies for using their content- a classic case of copyright infringement. The fine also cites concerns about Google's AI service, Gemini (previously Bard), which was trained on news content without notifying publishers.

The Way Forward: How Organizations Can Prepare Themselves for the Secure AI Adoption

The concerned organizations and firms should incorporate comprehensive and robust strategies that help build secure, ethical, and resilient AI systems that align with the recent industry standards and frameworks, and also offer regulatory compliance in order to ensure the safe and effective adoption of AI. Here are a few steps that organizations can take:

 1. Resilient AI Governance Framework

An AI governance framework is essential to assign well-defined roles and responsibilities for AI security and privacy across the organization's perimeters. It must be thoroughly checked that AI systems are transparent and unbiased, developed and implemented ethically and responsibly, and, most importantly, in alignment with the firm's goals and objectives.

2. Integrating Security & Privacy into AI Development Lifecycle

Organizations should incorporate a risk-based approach while handling AI/ML models. They must focus on establishing robust governance framework, as talked earlier, while embedding and/or integrating security and privacy into every stage of the AI development lifecycle. This step is crucial so as to protect information systems, protect sensitive data, and maintain public trust and brand image.

3. Embracing the Fundamental Principals of AI

One must carefully handle biases in trained AI systems, as they can output unfair and untreated responses that might lead to hefty regulatory penalties, compliance issues, and reputational damage. Hence, firms must adopt a human-centric design approach to eradicate such nuisances later. To start with, the EU AI Act could be one of the starting points to look at.

4. Invest in AI and Cybersecurity Training

Organizations building AI systems and applications certainly require a workforce that understands the nuances of AI attacks and adversarial machine learning and is aware of secure AI development practices. However, those who are using AI or not may also need to train their employees in identifying, handling, and reporting AI-based attacks.