Cloud AI and ML are powerful tools for solving complex problems, but they also come with risks and challenges. How can you ensure that your data and models are secure, compliant, and ethical when using cloud services? In this article, we will explore some best practices and tips for protecting your data and models when using cloud AI and ML.
One of the most basic and essential steps for protecting your data is to encrypt it before sending it to the cloud. Encryption ensures that only authorized parties can access and read your data, and prevents unauthorized access or leakage. You can use encryption tools and libraries provided by your cloud provider, or use your own encryption methods. You should also encrypt your data at rest, in transit, and in use, and use strong encryption keys and algorithms.
When using cloud AI and ML, it’s crucial to encrypt data before transmission to protect it from unauthorized access; selecting a reputable cloud provider with solid security certifications aids in ensuring compliance and security. Establishing stringent identity governance practices, implementing multi-factor authentication, and maintaining strict access controls are vital. Leveraging cloud-native security tools integrates enhanced security features seamlessly. Regularly monitoring and auditing your data and models are essential in promptly identifying and addressing any security anomalies or breaches.
With NPU’s and over-clocking, passwords are an “achilles heel”. With hackers sharing passwords in a mesh network, a very different method of encryption (read ‘proprietary’) is a blunt-instrument to make thieves go elsewhere where it’s easier.
Not all cloud providers are created equal when it comes to security, privacy, and compliance. You should choose a cloud provider that has a good reputation, offers transparent and robust security policies and features, and complies with relevant regulations and standards. You should also review the terms and conditions of your cloud provider, and understand how they handle your data and models, what rights and obligations you have, and what recourse you have in case of a breach or dispute.
Seed Stage Stealth Startup in USA. Co-founder, executive, mentor, inventor, jounalist
‘Your mileage may vary’, but, with the stolen NSA hacking tools integrated into AI-enabled work-flow hacking engines, we need a sobering acknowledgement that the rules have changed. If you want security, it’s up to you to secure it, no external tools, with exception of authorization/authentication which can be cleverly implemented to prevent access to a sandbox - without passwords. Partitioning the IP and hiding it in different locations (even FPGA over mellanox) will make it accessible to only the best “tool-builder” thieves. In other words, make it too expensive to steal in man-hours/effort expended. There are easier targets.
Another important aspect of protecting your data and models is to use access control and identity management tools and techniques. Access control and identity management allow you to define who can access your data and models, what level of access they have, and how they authenticate and authorize themselves. You should use strong passwords, multi-factor authentication, encryption keys, and roles and permissions to control access to your data and models. You should also monitor and audit access logs and activities, and revoke access when necessary.
Triage of a penetration incident is never fun. Access control and identity management need fresh eyes. Current methods are little more than an eroded speed-bump. Passwords, no matter how complicated still use the same old hashing and cipher techniques. Elliptic-curve and ad-hoc one-time pads should be considered.
Data governance and ethics are not only about complying with laws and regulations, but also about ensuring that your data and models are fair, accurate, and responsible. Data governance and ethics involve defining and enforcing policies, standards, and processes for collecting, storing, processing, and using data and models. You should use data quality checks, data lineage tracking, data anonymization, data minimization, and data deletion techniques to ensure that your data and models are trustworthy and ethical. You should also consider the potential impact and consequences of your data and models on users, stakeholders, and society.
Until the processor vulnerabilities are addressed, the weak link is inside the processor…many, including myself can poke into memory locations and clone L2 and L3 contents as fast as the data pipeline itself. More “rules” won’t help - better ideas are rquired.
Training and testing your models on the cloud can expose them to various threats and vulnerabilities, such as data poisoning, model stealing, model inversion, or adversarial attacks. You should train and test your models securely, using methods such as differential privacy, federated learning, homomorphic encryption, or secure multi-party computation. These methods allow you to train and test your models without revealing your data or model details, or compromising their performance or accuracy.
Retaining pentesters to abuse your proprietary methods is recommended; if their methods are blunted, then you’ve accomplished a meaningful goal.
Deploying and monitoring your models on the cloud can also pose risks and challenges, such as model drift, model bias, model tampering, or model misuse. You should deploy and monitor your models safely, using methods such as model encryption, model watermarking, model verification, or model auditing. These methods allow you to protect your models from unauthorized access or modification, and to ensure that they are performing as expected and intended. You should also update and maintain your models regularly, and respond to any issues or feedback promptly.