Zero Trust for Multi-Agent Systems // Surendra Narang | Venkata Gopi Kolla
Abstract //
Don’t trust AI agents. Just because an agent is in your system doesn’t mean it should have overly permissive privileges. Restrict access. Defining a clear role for each agent from the beginning. Give each agent only the tools and information access it really needs. Monitoring of the agent's activity. Keep an eye out for odd behavior or agents that step out of line. The sooner you catch it, the easier it is to fix. Keep things safe without making them slow. Good security shouldn’t get in the way of your agents doing their job. You can have both speed and safety.
Bio //
Surendra Narang //
I am a seasoned cybersecurity and cloud infrastructure leader with over 20 years of experience in cybersecurity, driving innovation at the intersection of AI Security, Zero Trust, and enterprise security. I currently lead advanced security initiatives at Palo Alto Networks, where I oversee the deployment of secure architectures for large-scale systems, including AI-powered infrastructure and Zero Trust models. I am the author of an upcoming book on AI Security and a regular reviewer for IEEE cybersecurity conferences for AI papers from a security perspective. My current focus includes evaluating the security and scalability of multi-agent systems (MAS), where I explore risks such as prompt injection, rogue agent behavior, data leakage, and adversarial manipulation. I am also focusing on integrating trust mechanisms, enforcing secure communication channels between agents, and deploying strict access controls to secure interactions within decentralized agent environments.
Venkata Gopi Kolla //
Venkata Gopi Kolla is an Edge Computing & Network Security Specialist with over a decade of experience designing and scaling distributed systems. At Salesforce, he has led major initiatives at the intersection of edge architecture, cybersecurity, and high-availability content delivery — including secure multi-CDN failover, bot protection, and authenticated caching using platforms
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Our 1st MLOps Meetup // Luke Marsden // MLOps Meetup #1
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Remote Collaboration as a Data Scientist
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MLOps Manifesto with Luke Marsden from Dotscience
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MLOps lifecycle description
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What Does Best in Class AI/ML Governance Look Like in Fin Services? // Charles Radclyffe // MLOps #2
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Life purpose and too many spreadsheets
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Explainability, Black boxes and EU white paper on reproducibility
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Hierarchy of Machine Learning Needs // Phil Winder // MLOps Meetup #3
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Automatically Retrain Machine Learning Models? Are best practices worth it?
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Building an MLOps Team? Key ideas to keep in mind
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Hierarchy of MLOps Needs
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Bare necessities for getting an ML model into production
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MLOps and Monitoring
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How Phil Winder got into Data Science and Software Engineering
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Provenance and Reproducibility in Machine Learning; what is it and why you need it?
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Friction Between Data Scientists and Software Engineers
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ML Platforms - The build vs buy question
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ML Services Gateway at SurveyMonkey
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Message buses, Async and sync architecture
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MLOps #4: Shubhi Jain - Building an ML Platform @SurveyMonkey
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Hybrid Data Science Teams @SurveyMonkey
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How do you handle ML version control at SurveyMonkey
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Doing ML with Personal Information
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Evolution of the ML feature store @SurveyMonkey
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Developing a Machine Learning Feature Store
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Auto retrain ML models is not the question
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3 key parts to Machine Learning monitoring
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MLOps Meetup #6: Mid-Scale Production Feature Engineering with Dr. Venkata Pingali
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MLOps meetup #5 High Stakes ML: Active Failures, Latent Factors with Flavio Clesio
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MLOps: Airflow Pros and Cons
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Specific challenges in Machine Learning
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Current State Of Machine Learning
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Humans in the Loop are a defining factor in Machine Learning
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Learning from real life Machine Learning failures
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Survivorship Bias in machine learning tutorials
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Swiss Cheese model in Machine Learning
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Resume driven development in Machine learning & software engineering
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Who has the highest standards in ML?
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Venkata Pingali of Scribble Data Thoughts on the Current State of Machine Learning
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Dependable data and being able to Trust in your Data with Venkata Pengali of Scribble Data
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Speed, Trust, Evolution and Scale in MLOps
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More difficult transition for data scientists to become ML engineers
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How many models in prod til I need a dedicated ML platform?
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Deeper thinking from data scientists around platform blackholes
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Checkpointing, metadata, and confidence in your data
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Adjacent usecases and multistep feature engineering
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Standardization of Machine Learning tools like in Software Engineering with Venkata Pingali
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Reproducability flaws in end to end Machine Learning debugging
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3rd wave of data scientists
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MLOps meetup #7 Alex Spanos // TrueLayer 's MLOps Pipeline
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MLOps Meetup #8 Optimizing Your ML Workflow with Kubeflow 1.0
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Are Kubeflow and Airflow complementary?
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Why Kubeflow gained so much traction=open community
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Who decides the dirrection of Kubeflow
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Versioning your ML steps with Kubeflow
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Machine Learning Lifecycles//Perception vs Reality
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Kubeflow vs SageMaker in Machine Learning
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