How do you monitor AI system security?

1. Data Poisoning: Think of data poisoning like sneaky tricks in training AI. It's like someone messing with the teacher's notes to confuse the class. To stop it, we need to check our data carefully and keep an eye on it. 2. Model Theft: Imagine someone trying to steal the secret recipe of a famous dish. Model theft is like that but with AI. We need strong locks and special codes to protect AI secrets. 3. Model Evasion: Picture a game where you try to outsmart your opponent. In AI, some people try to outsmart it with tricky inputs. We need to keep updating our AI to stay one step ahead. 4. Model Inversion, Adversarial Attacks & Misuse or Abuse.

These are various other threats/risks to AI system 1. Ethical risk - Exploiting AI system to make unethical decisions that humans never does .For eg , autonomous vehicle poses ethical risks 2. Data poisoning- with out right guardrails or tight security measures, AI system is under huge risk if someone manipulates training / inference data 3. Model theft - A part of Model can be theft by re-engineering if there is less security policies in place

We have implemented encryption through our entire AI end-to-end, so that the input data can not be poisoned, models can not be tampered with, or taken, abused, or misused, or inverted. The encryption also prevents unauthorized extraction, and reverse engineering. Our security prevents model evasion by limiting inputs to properly authenticated data. Only authenticated data and models are loaded and processed. In addition to this, we have added other security measures. When all these security measures are combined, it helps protect against adversarial attacks and AI system misuse. Security always includes comparmentalization, on-going evolution, and continuous improvement on all fronts. Be paranoid, Test all, Don't be lazy, Be Secure!

To keep your AI system safe, focus on: 1. Spotting Dangers: Know the risks like hacks and data leaks. 2. Set a Safety Standard: Have a basic safety level to compare against. 3. Use Security Tools: Use firewalls and unique algorithms to catch problems. 4. Best Practices: Follow good safety rules to ensure your AI is secure and ethical. 5. Make Everyone Aware: Teach people using the AI to be security-smart. Also, think about the tools you'll use to manage your AI. And remember to test it thoroughly to ensure it's safe and works well.

AI tools IBM Security QRadar Suite and IBM Security Guardium. Identify & mitigate threats while verifying & strengthens access management. Vectra's Cognito offers real-time threat detection, reduce risk of data breaches & system compromises. Sophos Intercept X uses deep learning for proactive security & minimizes damage. Symantec's Targeted Attack Analytics efficiently identifies, responds to targeted attacks & threat mitigation. BioHAIFCS povides adaptable protection against threats. StringSifter enhances efficiency by prioritizing data for malware analysts for threat response. DefPloreX automates web-defacement monitoring, quickly identifying and categorizing defacements, AI-Powered Security Tools like EDR, XDR, NDR, MDR, SIEM, and SOAR

Human elements are often the weakest link in the security chain. While we can optimize and harden systems, the human aspect introduces unpredictability. Proper vetting of individuals involved in infosec is paramount. Even the most fortified systems can be compromised if the individuals managing them aren't trustworthy or properly trained. Insider threats, both malicious and unintentional, can have devastating effects. Moreover, security isn't just about preventing unauthorized access; it's about ensuring those with access act responsibly. As AI systems become integral to operations, ensuring the integrity of human operators is more crucial than ever.

A robust audit trail is indispensable for the security and integrity of any AI system. It provides a chronological record of system activities, ensuring transparency and accountability. In the event of an incident or anomaly, an audit trail allows for rapid identification of the source, facilitating quicker response and remediation. Moreover, it acts as a deterrent against unauthorized or malicious activities, knowing that actions are logged. From a compliance and governance standpoint, maintaining a comprehensive audit trail is often mandatory. It's not just about detection; it's about fostering trust and ensuring responsible AI system management.

We use Microsoft Purview; I don't love all MS products, but I do really like this one, especially its flow charts. Microsoft Purview provides capabilities to manage and monitor data security, enabling governance and compliance. It allows classification of sensitive data, scanning for compliance, and role-based access controls. Collibri focuses more on overall data governance, with some security features like encryption and access controls. Other tools like Informatica, IBM InfoSphere, and erwin Data Intelligence also support security and compliance as part of data governance. Overall, Purview provides robust security governance with automated classification, policy enforcement, and integration with Microsoft security.

A strong security culture starts with leadership setting the tone to promote security as a shared value. Open communication about vulnerabilities, secure coding practices, and cross-team collaboration on security strategies is critical. Security should be baked into development processes through secure design, threat modeling, and automated checks. An open, blameless culture allows issues to be reported without fear. Shared ownership spreads responsibility beyond security engineers. Rewarding secure behaviors reinforces priorities. Overall, an organization-wide culture of mindfulness, transparency, and collective responsibility around security is essential to manage AI risks responsibly.

Threat Detection Models in real-time: AI system that analyzes network traffic for unusual patterns indicative of cyberattacks. Ethical AI Audits: Employ AI tools to conduct regular audits for ethical compliance, such as algorithms that scan text data for bias and fairness concerns. Secure Data Sharing Platforms: Utilize secure data-sharing platforms that leverage AI encryption techniques, enabling confidential data exchange while protecting against unauthorized access. Behavioral Analysis: Used to identify unusual user interactions, crucial for safeguarding sensitive systems like healthcare AI applications. Automated Patch Management: Ensuring rapid response to vulnerabilities and reducing the window of exposure to potential threats.

Confidential Computing (CC): It's an approach to data security by protecting the data in use (confidentiality, integrity) in the system. Homomorphic encryption is another technique that also promises similar protection but comes with performance bottlenecks. From the context of AI, say 1) if you want to do multiparty computation say 5-6 hospitals want to train their cancer detection model from data collected by each other without revealing it to others or 2) if you want to host LLMs trained on your proprietary data on a 3rd party cloud -- how can you ensure your data don't get leaked or tampered with? CC will allow you to host your model (say LLM) on a confidential container/ VM and hardware ensures data protection that is attestable.