AI (artificial intelligence) adoption has taken off like wildfire by organizations across the globe, because of the business opportunities it offers. From the allure of increased efficiency, reducing manual processes and increasing accuracy, it is clear to see why leveraging AI appeals to business leaders. 

However, AI is a double-edged sword. While it has the potential to revolutionize business processes and operational efficiency, by introducing AI, businesses also introduce an array of new associated business risks. Not the least of which, some bad actors intend to use AI in malicious ways and we are seeing the rise of AI-enabled cyber attacks. In one report, nearly three-quarters (74%) of CISOs and security leaders state that AI-powered threats are now a significant issue. 

It is clear that balancing the potential benefits of AI, with the risks it presents is a challenge to which all businesses need to rise. Here, we explore what the rise of AI means for businesses, provide an overview of AI risks and discuss an approach to implement a framework that manages them. 

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The risks of AI adoption

The key to effectively leveraging the business opportunities that AI presents lies in an organization's ability to navigate its potential pitfalls and implement effective AI risk governance that safeguards against ethical, legal and operational hazards. To do this, businesses must first understand the potential risks at hand - some of the most common are: 

Bias and fairness

At the most basic level, AI is trained by “feeding” it a large volume of data. Using multiple algorithms, AI then identifies patterns in data and starts learning from them. When this data is incorrect or biased, AI is trained on false data. This then manifests as the algorithm presenting data that contains inherent biases. This risk is significant enough that the U.S. Commission on Civil Rights produced a report on it in 2023. 

Privacy and security

To deliver results, AI solutions must be fed data. Without proper governance, there is a real possibility of an individual sharing sensitive or confidential data, which raises data privacy concerns and security vulnerabilities.

Explainability and transparency

Despite the World Economic Forum urging AI developers to be more transparent about how their AI models are trained, “black box" AI models remain the norm. These models lack transparency, hindering trust and accountability.

Safety and security

AI is becoming more commonly utilized for critical tasks that present a very real risk to individuals, One example is the rise of autonomous vehicles. In these instances, organizations must ensure AI systems prioritize safety and security above other operational efficiencies.

Job displacement

One of the leading concerns about the rise of AI is its potential impact on jobs and job roles. While in the vast majority of cases, AI is not implemented to entirely replace individual jobs, it can force an evolution in job scopes when manual tasks are automated. Employers must ensure they have sufficient retraining and upskilling initiatives in place to support employees in this transition. 

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How AI is being leveraged by attackers 

The flip side of AI’s potential to help organizations is its ability to aid cyber attackers. They are leveraging its strengths to launch more sophisticated attacks on businesses at a greater speed than ever before.  Here' are just a few ways AI is being used by malicious actors:

Crafting hyper-realistic phishing attacks

Attackers feed AI vast amounts of data to create personalized phishing emails and messages that mimic the writing style and tone of legitimate senders. This makes them much harder to identify and can trick even cautious employees.

Automating attacks

With AI, attackers accelerate the rate at which they’re able to complete repetitive tasks like password cracking and vulnerability scanning. In doing so, they’re able to launch large-scale attacks in a fraction of the time it used to take. 

Bypassing defenses

Malware can be equipped with AI that allows it to learn what security systems organizations have in place and adapt its behavior to evade detection. 

Extracting data efficiently

AI not only increases the likelihood of a cyberattack but also the scale of losses once a system is compromised. AI can be used to sift through data and identify valuable information to steal, like financial records or intellectual property. Given the speed at which AI can process vast volumes of data, the amount of information it can extract before detection is significantly greater than in non-AI attacks. 

Social engineering on a new level

AI is used to create deepfakes that impersonate real people, such as a CEO or coworker. This can be used to manipulate employees into giving away sensitive information or authorizing fraudulent transactions. Deepfakes are only predicted to become more commonplace, with one report estimating that 90% of online content may be synthetically generated by 2026.  

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Implementing a robust AI risk governance framework 

While 79% of executives say steps have been taken to reduce risks associated with the adoption of AI, only 54% of respondents in hands-on roles agreed. It is clear that business leaders will need a practical construct to understand and manage AI risks across their organizations. Here is a starting point for an AI risk governance framework.

1. Risk identification: 

Conduct a comprehensive risk assessment to identify potential risks associated with your specific AI use cases. We’ve written our guide to unlocking the business value of cyber risk assessments to support CISOs to conduct effective risk assessments that deliver business value. 

2. Risk prioritization: 

Evaluate identified risks based on severity, likelihood, and potential impact on your organization. Our guide to cyber risk prioritization provides insights to support organizations in focusing their efforts on the most business-critical areas. 

3. Risk mitigation strategies:

Develop and implement strategies to mitigate prioritized risks. This may involve:

• Implementing robust data privacy and security practices.
• Investing in explainable AI solutions.
• Establishing safety protocols for critical AI applications.
• Planning for workforce transformation.

4. Governance & oversight: 

Establish a dedicated AI governance committee with representatives from diverse departments (legal, IT, ethics, and business units). This includes developing clear policies and procedures for AI development, deployment, and monitoring. It is important to remember that effective governance and oversight goes far beyond meeting compliance requirements. 

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5. Continuous monitoring and improvement: 

Regularly audit your AI systems to ensure they adhere to the governance framework and identify any emerging risks. Foster an open culture where employees can report potential AI risks and ethical concerns and ensure that effective communication channels are in operation between cybersecurity professionals and the wider business. 

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The X-Analytics perspective 

Businesses need to take a proactive and realistic approach to AI risk governance - AI is only going to become more prevalent in both businesses and cyber attacks. Despite AI being the newest emerging technology that presents business risks, the approach to managing and mitigating the risk it poses is still rooted in the same core cybersecurity principles. 

Business leaders need to ensure they are comprehensively assessing their risk, effectively prioritizing it based on what poses the greatest threat to their business and deciding whether they mitigate, transfer or accept the risk. 

X-Analytics gives organizations business-centric insights at their fingertips, so they can quickly understand key AI and cyber risk insights as part of their ongoing governance, oversight, and materiality discussions.

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