The importance of artificial intelligence security

Before implementing the AI strategy, manufacturers must consider adopting new technologies to protect privacy and ensure compliance with security standards.

If manufacturers want to participate in next-generation digital product projects, trying to use artificial intelligence will help enterprises establish new business models, revenue streams and experiences.

But enterprises should learn more about the headlines about AI technology innovation. For example, alphafold has solved the problem of protein folding in the past 50 years and some possibly more influential artificial intelligence technologies. These advances make artificial intelligence more responsible and more privacy oriented.

As algorithms absorb and use more and more data sets in training and deployment, especially the release of new privacy regulations such as gdpr, CcpA and HIPAA, data privacy related to artificial intelligence / machine learning will only become more and more important. In fact, the U.S. Food and Drug Administration recently released a new action plan to regulate the use of artificial intelligence in medical devices. The expanding regulatory framework partly explains that data privacy is one of the most important issues in a decade.

When enterprises plan to invest in artificial intelligence in the future, the following three artificial intelligence technologies will ensure compliance and security in the future.

1. Joint learning.

Joint learning is an increasingly important machine learning and training technology, which can solve one of the biggest data privacy problems in machine learning, especially in the field of sensitive user data (such as medical care). The traditional practice over the past decade has been to isolate data as much as possible. However, the aggregated data required to train and deploy machine learning algorithms cause serious privacy and security problems, especially when enterprises share data.

Joint learning can enable enterprises to provide insight into aggregated data sets and ensure the security of data in non aggregated environments. The basic premise is that the local machine learning model is trained in private data sets, and the model update flows and aggregates between data sets. The important thing is that data never needs to leave the local environment.

In this way, data can still bring group wisdom to the organization while maintaining security. Joint learning reduces the risk of a single attack or leak because the data is not stored in a single repository, but scattered in multiple repositories.

2. Interpretable artificial intelligence (Xai)

Many AI / machine learning models (especially neural networks) are black box models. After a lot of data training, these models are usually irresponsible because it is difficult to determine how to make decisions. In order to make them more accountable and transparent, they need to be more explanatory.

The emerging research field is called interpretability. It uses complex technologies to help simple systems, such as decision trees, neural networks and other complex systems. Explanation helps build trust in the system and helps researchers understand why mistakes are made and how to correct them quickly.

In sensitive fields such as medical treatment, banking, financial services and insurance, artificial intelligence decision-making cannot be blindly believed. For example, when approving bank loans, understand why someone is rejected, especially considering the example of racial prejudice sneaking into other artificial intelligence systems. As artificial intelligence becomes more and more complex, these black box models become more and more clear and important. Artificial intelligence to explain should become the main field of concern for organizations developing artificial intelligence systems in the future.

3、AIOps/MLOps

About 20 years ago, Devops completely changed the mode of application development, deployment and management. It standardizes pipelines, significantly improving efficiency and reducing delivery time.

Now aiops / mlops is the same in artificial intelligence. Cognitityca predicts that by 2025, the global mlops market will expand to $4 billion.

The idea is to accelerate the life cycle of the whole machine learning model through standardized operation, performance measurement and automatic repair. Aiops can be applied to the following three levels:

(1) Infrastructure layer.

This is where containerization works. Automation tools can expand an organization's infrastructure and team to meet capacity needs. The emerging subset of Devops is called gitops, which is a cloud computing based micro service that applies the principle of Devops to container operation.

(2) Application performance management (APM)

According to IDC, the annual loss caused by global application downtime is between us $125 million and US $2.5 billion. APM (APM) helps organizations achieve application performance management to the greatest extent by simplifying application management, limiting downtime. Aiops solutions are combined with aiops methods, using artificial intelligence and machine learning to actively identify problems rather than passive methods.

(3) Information technology service management (ITSM)

The scale of information technology services is huge. In fact, it can represent any hardware, software or computing resources provided by information technology organizations for end users, whether the end users are internal employees, customers or business partners. ITSM uses aiops to automate ticketing process, manage and analyze events, authorize and monitor files.

Although aiops / mlops is implemented by most organizations to improve efficiency, many organizations find that, for example, APM application performance management platform can use its rich data resources as an early warning system to increase additional security. As the artificial intelligence / machine learning life cycle is more strictly optimized and structured, security and privacy risks are easier to identify and reduce.

Responsible for the experiment.

In recent years, people have seen many powerful AI use cases, but the future will ensure that the AI system behind these use cases has the responsibility to use data. As more and more privacy regulations are issued and organizations see that regulations actually increase transparency and trust in customers, it's time to try responsible artificial intelligence. Joint learning, interpretable artificial intelligence and aiops / mlops will be three good starting points.