Essential Skills for AI in Security

Whether you’re integrating a third-party AI model or deploying your own, adopt these practices to shrink your exposed surfaces to attackers and hackers: • Least-Privilege Agents – Restrict what your chatbot or autonomous agent can see and do. Sensitive actions should require a human click-through. • Clean Data In, Clean Model Out – Source training data from vetted repositories, hash-lock snapshots, and run red-team evaluations before every release. • Treat AI Code Like Stranger Code – Scan, review, and pin dependency hashes for anything an LLM suggests. New packages go in a sandbox first. • Throttle & Watermark – Rate-limit API calls, embed canary strings, and monitor for extraction patterns so rivals can’t clone your model overnight. • Choose Privacy-First Vendors – Look for differential privacy, “machine unlearning,” and clear audit trails—then mask sensitive data before you ever hit Send. Rapid-fire user checklist: verify vendor audits, separate test vs. prod, log every prompt/response, keep SDKs patched, and train your team to spot suspicious prompts. AI security is a shared-responsibility model, just like the cloud. Harden your pipeline, gate your permissions, and give every line of AI-generated output the same scrutiny you’d give a pull request. Your future self (and your CISO) will thank you. 🚀🔐

AI & Practical Steps CISOs Can Take Now! Too much buzz around LLMs can paralyze security leaders. Reality is that, AI isn’t magic! So apply the same foundational security fundamentals. Here’s how to build a real AI security policy: 🔍 Discover AI Usage: Map who’s using AI, where it lives in your org, and intended use cases. 🔐 Govern Your Data: Classify & encrypt sensitive data. Know what data is used in AI tools, and where it goes. 🧠 Educate Users: Train teams on safe AI use. Teach spotting hallucinations and avoiding risky data sharing. 🛡️ Scan Models for Threats: Inspect model files for malware, backdoors, or typosquatting. Treat model files like untrusted code. 📈 Profile Risks (just like Cloud or BYOD): Create an executive-ready risk matrix. Document use cases, threats, business impact, and risk appetite. These steps aren’t flashy but they guard against real risks: data leaks, poisoning, serialization attacks, supply chain threats.

The Cyber Security Agency of Singapore (CSA) has published “Guidelines on Securing AI Systems,” to help system owners manage security risks in the use of AI throughout the five stages of the AI lifecycle. 1. Planning and Design: - Raise awareness and competency on security by providing training and guidance on the security risks of #AI to all personnel, including developers, system owners and senior leaders. - Conduct a #riskassessment and supplement it by continuous monitoring and a strong feedback loop. 2. Development: - Secure the #supplychain (training data, models, APIs, software libraries) - Ensure that suppliers appropriately manage risks by adhering to #security policies or internationally recognized standards. - Consider security benefits and trade-offs such as complexity, explainability, interpretability, and sensitivity of training data when selecting the appropriate model to use (#machinelearning, deep learning, #GenAI). - Identify, track and protect AI-related assets, including models, #data, prompts, logs and assessments. - Secure the #artificialintelligence development environment by applying standard infrastructure security principles like #accesscontrols and logging/monitoring, segregation of environments, and secure-by-default configurations. 3. Deployment: - Establish #incidentresponse, escalation and remediation plans. - Release #AIsystems only after subjecting them to appropriate and effective security checks and evaluation. 4. Operations and Maintenance: - Monitor and log inputs (queries, prompts and requests) and outputs to ensure they are performing as intended. - Adopt a secure-by-design approach to updates and continuous learning. - Establish a vulnerability disclosure process for users to share potential #vulnerabilities to the system. 5. End of Life: - Ensure proper data and model disposal according to relevant industry standards or #regulations.

😅 We don’t talk about AI red teaming much today, but it’ll likely become super important as AI systems mature. Microsoft's recent white paper highlights really insightful lessons from their red teaming efforts. For those unfamiliar, AI red teaming is like ethical hacking for AI, simulating real-world attacks to uncover vulnerabilities before they can be exploited. ⛳ Key Lessons: 👉 Understand the system: Align efforts with the AI’s capabilities and application context—both simple and complex systems can pose risks. 👉 Simple attacks work: Techniques like prompt engineering and jailbreaking often reveal vulnerabilities without complex methods. 👉 Beyond benchmarks: Red teaming uncovers novel risks and context-specific vulnerabilities missed by standardized tests. 👉 Automation scales: Tools like PyRIT help automate testing, covering a broader risk landscape. 👉 Humans are crucial: Automation helps, but judgment and expertise are needed to prioritize risks and design attacks. 👉 RAI harms are nuanced: Bias and harmful content are pervasive but hard to measure, requiring careful, context-aware approaches. 👉 LLMs introduce new risks: They amplify existing vulnerabilities and bring new ones, like cross-prompt injection attacks. 👉 AI security is ongoing: It requires iterative testing, economic considerations, and strong policies for long-term safety. As AI becomes more mainstream, security will take center stage, and we’ll need stronger teams and initiatives to make it truly robust. Link: https://lnkd.in/eetMw4nG