An introduction to MLSecOps

Companies around the world are looking towards technological solutions to optimise their operations and decrease their capital expenditure, especially given the current economic landscape. There has been a major propensity and transition towards CloudOps implementation for greater control, expandability, and assimilation of operations through Cloud Platforms.

What Are CloudOps?

The formalisation of best methods and processes that enable cloud-based technologies, databases, and applications to function optimally and continuously, to achieve zero-downtime in company-wide structures and procedures, is referred to as CloudOps.

Most businesses that are in the process of transitioning their machine learning and artificial intelligence (ML/AI) use cases from innovation to industrialisation. MLOps platforms from cloud vendors like Azure and GCP are great options, but they are still developing.

What Exactly Is MLSecOps?

MLSecOps is a term that encompasses three distinct technologies: machine learning (ML), security (Sec), and operations (Ops).

Machine Learning and Artificial Intelligence have undeniable benefits when it comes to solving modern security issues. Compared to more conventional passive applications, the ability for applications to learn from historical experiences and use the information to alter their behaviour when confronted with comparable issues down the road offers a meaningful advantage. Cyber-attacks can be identified using Machine Learning Algorithms by pursuing or understanding an abnormal pattern of activities.

What is a DDOS (Distributed Denial-Of-Service) attack?

A denial-of-service attack (DoS attack) is a cyber-attack in which the assailant attempts to make a device or network asset unavailable to legitimate users by interrupting services of a host connected to the Web, either temporarily or indefinitely. Denial of service is usually achieved by swamping the specific target machine or resource with unnecessary requests to overwhelm systems and inhibit some or all legitimate traffic from being satisfied.

Usage Of MLSecOps

Enterprise security architectures will have to factor in machine learning and artificial intelligence security as well. MLSecOps is concerned with the security aspects of ML/AI model automation according to the data instead of pictures alone (there are also some good papers on how AI models use images as their training data).

Evasion and poisoning techniques are the most common ways to attack ML/AI models. It’s also possible for the attacker to substitute the model with the one he’s created.

1. Model Development And Data Preparation

Data scientists must be cognizant that their models are vulnerable to attack. As a result, they must ensure that the decision limits are clear. They may also include hostile examples (noisy/perturbed inputs) in each model’s dataset.

Another option is to include sorting in the training dataset to help stop poisoned information from the live inferencing manufacturing platform from being included in the training thoughtlessly. In addition, when specifying the classification models, the framework should take data privacy into account.

2. Model Training

Test cases should be drafted for each prototype to test combative situations during training. Vulnerability assessment could also be incorporated into the training process. Before releasing the model into production, ensure that it is describable (how it makes decisions), that the trained data complies with data privacy regulations, and that it has been tested against intrusion detection and penetration.

3. Inferencing

When the model is dispatched to production, input data must be validated using rules. The input data can be filtered/alerted using light data validation fixed pattern on the model’s preconceptions. This will also aid in detecting anomalies in the model’s input pattern before it is too overdue.

MLSecOps and the pandemic

Because of this drift, many models, particularly during the COVID days, failed to obtain the required accuracy rates.

To address such scenarios, computerised monitoring techniques would be required. Consequently, the models must be safeguarded with hashes that can be verified during inferencing as “model watermarks.”

It will also be beneficial to thwart any poisoned data sets from entering the model and take some action before things spiral out of control.

Furthermore, attackers may employ a variety of techniques, such as the use of bots, to learn the behavior of these models. Web service security mechanisms such as verification, permission using OAuth, risk-based approval policies, TLS, rate constraining, and DDOS attack preventative measures, among others, can protect susceptible digital inferencing designs through REST APIs.

However, it is possible to bypass such safeguards and send malicious traffic to ML/AI models. In those cases, autonomous navigation alternatives should be able to detect such abnormalities.

Conclusion

Model protection and autonomous navigation solutions must be considered part of the Ml/AI deployment strategy to detect such unforeseen model cloud services and keep the models nourished in the manufacturing environment. More tools and remedies that are assimilated into popular MLOps platforms are needed.

Few companies are currently investing in ways to defend against or mitigate the effects of offensive AI attacks like deepfake phishing initiatives.

Most researchers suggest that organisations expand the current MLOps paradigm to include ML security (MLSecOps), which includes security testing and the ability to monitor AI/ML models, as well as to conduct more data analysis into post-processing tools that can defend software from evaluation after implementation (i.e., anti-vulnerability detection).