While there are cybersecurity risks inherent in the use of AI tools and solutions, organizations can develop strategies – and review and update their current cybersecurity programs – to mitigate these risks and use the technology safely.
The Risks of AI
Some of the most significant risks of using AI include privacy and ethical concerns, and those relating to security. A lack of transparency may also be a risk, particularly concerning the development of deep learning models and the possibility that AI could lead to the generation of misinformation that could manipulate public opinion.
The Many Benefits of AI Solutions
For businesses, AI solutions can promote the development of a new generation of services and products, improve machine maintenance, boost sales, increase production quality and output, save energy, and enhance customer service. Plus, the workplace can be made safer – as robots can be used to undertake the dangerous elements of jobs – and new roles within AI-driven industries are expected to grow over the coming years.
AI tools could also help people by providing safer cars, improved health care, and tailored, longer-lasting and more affordable products and services. Furthermore, AI solutions deployed by the public sector have the potential to offer new possibilities (while reducing costs) in public transport, energy and waste management, and education.
Developing an AI Risk Mitigation Strategy
As well as reviewing and amending existing policies and procedures, threat modeling should be an important part of a strategy to mitigate the risks posed by AI. These exercises serve to identify potential security threats to an organization’s AI systems and assess the impact each would have.
Those involved in the implementation of national digitization projects – such as Holger Ziegeler – know that effective data governance is a vital element of creating an effective AI risk mitigation strategy, as this will allow organizations to reap the benefits of this technology while managing the risks. This component of the plan could include defining clear roles and responsibilities and developing data quality assessments, as well as covering data validation and acceptable data use.
Access and identity management policies should be created for an organization to control access to its AI infrastructure, while encryption and steganography should be deployed to protect the integrity and confidentiality of AI training data, models, and source code. Vulnerability management and security awareness also need to form part of the organization’s overarching risk mitigation strategy.
For more information about vulnerability management, take a look at the embedded PDF.