AI security
16 moments from 15 videos · 39:00 min total
Explore safety practices for building applications on top of large language models. These expert talks guide security specialists and developers in preventing injection and leaks.
Current state of security in AI applications
Why the security domain is playing catch-up with rapidly evolving AI protocols and agent interactions.
Defining the core dimensions of generative AI security
Comprehensive system trustworthiness merges responsible AI, robust security controls, and strict privacy compliance to ensure safe user interaction.
Moving from secure software behavior to trustworthy AI
Traditional security mechanisms cannot guarantee trusting generative AI behavior in the face of evolving prompts and external content.
Evaluating supply chain risk with AI security assistants
Interacting directly with AI assistants on the web accelerates security assessments of open source dependencies like Bootstrap.
Increasing security stack investments to mitigate novel AI vectors
The surge in new AI attack vectors requires proactive frameworks and robust investment in security tooling.
Navigating new cybersecurity challenges in artificial intelligence applications
Treating external intelligence as a black box introduces significant security vulnerabilities that require dedicated exploratory testing strategies.
Treating AI responses as untrusted user data inputs
Protecting core infrastructure by isolating execution environments and enforcing classical application security checks on intelligent agents.
Securing AI agents against internal and external threat vectors
Operating AI at scale necessitates strict devsecops guardrails to prevent accidental secret exposure and defend against outside aggressors.
Distinguishing AI-assisted users from experienced security professionals
Why relying on AI-generated security answers still requires critical skepticism, strict accountability, and deep domain expertise.
Security integration and AI skepticism in developer tooling
Applying AI agents to large pull requests while maintaining security guardrails and acknowledging growing skepticism around AI investments.
Core AI security risks and data poisoning
Why trusting language model outputs equates to trusting potentially poisoned training data.
Utilizing industry threat models for AI security
Navigating frameworks from OWASP, NIST, and MITRE to understand and mitigate machine learning vulnerabilities.
Scaling AI-native security capabilities across the system
Matching engineering velocity requires continuous learning and embedding validation seamlessly into code generation workflows.
Consolidating fragmented security tools with precision AI methodologies
Replacing disconnected security toolchains with integrated posture management future-proofs the enterprise vulnerability lifecycle.
Building a vulnerable application for security testing
Demonstrating the security risks of generating code with artificial intelligence tools.
Hardware security features necessary for compliance and responsible AI
Meeting strict regional regulatory mandates requires establishing foundational hardware trust services that continuously protect sensitive data throughout processing lifecycles.
Your mix. Instantly.
More mixes
AI & Machine Learning
AI coding agents
AI & Machine Learning
MCP (Model Context Protocol)
AI & Machine Learning
RAG
Languages & Frameworks
TypeScript
Languages & Frameworks
React
Languages & Frameworks
Java
Cloud, Platform & Data
Platform engineering
Cloud, Platform & Data
Observability
Cloud, Platform & Data
Infrastructure as code
Architecture, Quality & Security
System architecture
Architecture, Quality & Security
Code review
Architecture, Quality & Security
Software supply chain security
Leadership, Career & Community
Engineering strategy
Leadership, Career & Community
Developer experience
Leadership, Career & Community