How do you keep visual data safe in AI projects?

In addition to this, the use lg hashes is in term of scoring a 5/10, I would suggest the use of digital signatures and original, safe and secure data sources.

Assume that all data that you put into an AI you don't own and run locally will become non-confidential and may be exposed. Try to anonymize your data before putting it into a public model. And consider running a private, local model for any important confidential files and processes. The Microsoft Power Platform can be a good place for this (storing data and acting on it with AI while maintaining confidentiality).

Make sure you know who accesses what. Step 1 is data minimization - try not to give people more data than they need. Lock your data down. Step 2 is PIM/PAM - identity and access management. Make sure only the right people can access specific data that they need, and no more than what they need. Step 3 is audit logging - make sure you record every action a user takes, ideally. And consider storing the audit log in a separate system, so if system 1 is breached, the audit logs will be safe and allow you to backtrace what happened.

TBH, you need to think of these tools in many ways like an employee. What should this "new employee" have access to? Why? Most people are so bent on getting Ai implemented to show how progressive they are. They never take a look at why and if they should. Access controls and auditing is all mission-critical and should be an active part of the governance model for Ai at the organization.

The main concept is to introduce a controlled amount of noise to meet privacy requirements while minimizing the impact on model accuracy. - Add Gaussian noise to the model updates on each client before sending them to the server. - Use differential privacy libraries like TensorFlow Privacy or Opacus which provide differentially private optimizers like DP-SGD. - Use secure aggregation techniques like Secure Multi-Party Computation (SMPC) where the model updates are securely summed across clients before applying noise. This improves privacy and utility. - Apply clipping or normalization to bound the sensitivity of each client's update before adding noise. This improves the noise to signal ratio.

Safeguarding visual data in AI projects involves a multi-pronged approach. Begin by employing robust encryption techniques to secure data both at rest and in transit. Implement stringent access controls, such as role-based permissions and multi-factor authentication, to restrict unauthorized access. Utilize anonymization methods like blurring or hashing to protect individual identities while retaining data utility. Regular security audits and updates are essential to address evolving threats and vulnerabilities. Lastly, establish comprehensive data governance policies to outline proper data handling, storage, and disposal procedures.

In the realm of AI projects, particularly those related to computer vision, visual data is pivotal. However, as the article states, it carries inherent privacy and security risks. Visual data often contains sensitive information, making it vulnerable to malicious intent. When securing visual data within AI projects, consider the following: The big call out that I don’t see many talking about is the potential of [API leakage]. API leakage, is a real thing, and it’s where someone inadvertent exposes a key. One way to a safeguard against this risk is through proactive education and vigilant monitoring. By adhering to these measures, we enhance the security of visual data and contribute to the success of AI projects.

Visual data are usually very big in size. So not only you need to secure the data but remember you will need adequate storage size. This will probably involve using a cloud platform service so yoy need yi check the cloud service offerings and their security practices before deciding which one to use.