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Webinar_ Building Your Secure AI Roadmap.pptx
Building Your Secure AI Roadmap Meetup Stay safe and join us virtually for our upcoming "Building Your Secure AI Roadmap" Meetup to hear directly from practitioners who are addressing these challenges today — from discovery and risk analysis to runtime monitoring and governance. AI is reshaping security risks across every stage of development and deployment. This session will help you cut through the noise, understand the key risks, and walk away with practical steps to strengthen your AI security posture. Whether you're just starting your AI security journey or looking to mature your program, you'll come away with actionable guidance on aligning innovation with security and governance. What You’ll Learn: 1. How to tackle AI security challenges from planning through runtime 2. Real-world use cases, risks, and solutions from top AI security leaders 3. How to embed AI security into your organization’s DNA 4. Key SAIL Framework principles in action
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Similar to Webinar_ Building Your Secure AI Roadmap.pptx
Webinar_ Building Your Secure AI Roadmap.pptx
- 1. Building Your AI securityRoadmap
- 2. Agenda The ”AI seachange" The Practitioner's panel Q&A Securing the new computing paradigm
- 3. Brandon Dixon, FormerPartner AI Strategist, Microsoft The AI Sea Change
- 5. Trends Observed Advancements in AIcontinue Agent(ic)s are the default Security is augmenting Lacking strong fundamentals
- 6. Trends Observed Advancements in AIcontinue Agent(ic)s are the default Security is augmenting Lacking strong fundamentals
- 8. Trends Observed Advancements in AIcontinue Agent(ic)s are the default Security is augmenting Lacking strong fundamentals
- 9. What forms anagent? Plugins, Automation, Code Execution Actions Embeddings, Graphs, Files, Data Knowledge Short, Long, Personalization Memory Planning, Reasoning, Learning, Verification Orchestration Access, Authorization, Role, Governance Identity
- 10. Measuring agents • None •Assistive agency • Partial autonomy • High autonomy • Full autonomy Autonomy • None • Task • Job • Role • Domain Scope • None • Basic • Proficient • Advanced • Mastery Scale • None • Basic • Developing • Complex • Rich Personality • None • Passive • Limited • Moderate • Fully interactive Interactivity
- 11. Trends Observed Advancements in AIcontinue Agent(ic)s are the default Security is augmenting Lacking strong fundamentals
- 12.
- 13. Trends Observed Advancements in AIcontinue Agent(ic)s are the default Security is augmenting Lacking strong fundamentals
- 15. The best wayto predict the future is to build with it. The durable advantage in a shifting landscape is experience. Companies who are experimenting and adopting AI today will be in the best position to understand how it can transform their business.
- 16. Dor Sarig, Co-founder& CEO, Pillar Security Securing the new computing paradigm
- 17. How software is secured today TH E C H A L L E N G E Are these controls enough to secure AI? Plan Threat modeling Deploy SPM Operate WAF, RASP Monitor SIEM, SOC Test Fuzzing, PT Build SCA, SAST
- 18. AI is changing theway we build software, introducing new security risks Vibe Coding Software AI Inference attacks Data leakage and privacy Insecure model output Supply chain risks Compliance T H E C H A L L E N G E
- 19. ISO 27001, SOC2,GDPR, S-BOM GRC Manager ISO 42001, NIST AI RMF, EU AI Act, AI-BOM AI bias/fairness Data privacy Model governance Role DAST, SAST, SCA, Fuzzing & PT Traditional Tools/Controls PT & Risk Analyst AI Red Teaming, Model scanning Prompt injection AI Tools/Controls New AI Risks Addressed Model extraction Jailbreaking CSPM, SIEM, Security dashboards CISO AI-SPM, Business AI Risks, AI asset inventory Strategic AI risks Shadow AI usage AI security ROI IDS/IPS, PAM, Shadow IT discovery DLP tools, Encryption, Access controls Pipeline scanners, Container scanners IT Security Data Security Engineer DevSecOps Engineer Shadow AI discovery, Agent controls, AI Governance Sensitive data leakage Rogue models Agent compromise Data & Model integrity, Differential privacy Data poisoning Training data leakage Model inversion AI Supply Chain tools, ML pipeline security Model tampering Pipeline poisoning Model drift WAF, API Gateway, ADR, RASP Application Security Engineer Guardrails, Data Masking PII Reduction Data leakage Data privacy Application abuse/takeover Security frameworks, Threat modeling, Zero-trust design CSPM, Cloud IAM, Multi-cloud governance Security Architects Cloud Security Engineer LLM threat modeling, Secure AI patterns Unknown LLM vulnerabilities Unsafe AI system interactions Architectural blind spots AI workload isolation, AI security configs AI Telemetry & Logging Resource hijacking Model & data theft Security Responsibilities in the AI Age
- 20. Introducing SAIL OWASP Top10 Vulnerability-Oriented ISO/IEC 42001 Management-System- Oriented NIST AI RMF Risk-Management-Oriented Google SAIF Conceptual framework Databricks DASF Component-Oriented SAIL Framework Process-Oriented Comprehensive risk mapping Process driven framework Practical guide - Written by practitioners
- 21. Introducing SAIL Comprehensive riskmapping Process driven framework Practical guide - Written by practitioners 70+ AI-specific risks 30+ core AI components Across 7 Development phases Aligned with ISO, OWASP, NIST, DASF
- 22. Introducing SAIL Comprehensive riskmapping Process driven framework Practical guide - Written by practitioners
- 24. AI Security Maturity Holistic Advanced Intermediate Developing Initial End-to-end,automated & process-driven security ops across the entire AI lifecycle Context-aware security guardrails, DLP controls, Enforced policies AI asset risk scanning, Threat modeling, AI red teaming AI inventory management and foundational security posture Ad-hoc AI tracking and basic access controls Security Posture Gains Desired state Current state
- 25. Sandbox Code/No Code Build Deploy Operate Monitor Test P l a n AI Telemetry Workbenc h AI Discovery Adaptive Guardrails AIRed Teaming AI-SPM >prod //dev Infuse security across every stage of the AI lifecycle P I L L A R’S U N I Q U E A P P R O A C H
- 26. Addressing Critical QuestionsAround Companies AI/ML Lifecycle How and where are we utilizing AI/ML in development and production? What risks and compliance gaps exist in our AI/ML systems? How can we mitigate these risks while enabling innovation? Discover Evaluate Protect
- 27. From Theory to Reality ThePractitioner's Panel
- 28. Topic 1: Planning,Policy & Discovery How are you establishing AI governance and visibility at an organizational level? What initial actions or tools helped you understand your AI landscape and risks?
- 29. How has yourrisk strategy evolved to address unique AI threats? What are key elements for effective AI red teaming? Topic 2: Risk Analysis and Red Teaming
- 30. What are someof the key initiatives or technologies your teams are implementing for runtime security? How are you approaching the detection of emerging threats or ensuring the security and auditability of autonomous AI agents? Topic 3: Runtime, Monitoring and Governance
- 31.
- 32. Meet us atBlackHat
- 33.
Editor's Notes
- #15 What is durable? Been working with the tech daily for nearly 2 years Much of the original knowledge has remained durable Those leaning in now will reap the benefits faster Who wins? Those adopting now will see how AI influences existing biz processes including development and security Using AI now puts you in the best position to understand how it will evolve over time, or where it needs bolstering AI applied strategically yields speed gains Moat is in ability to execute, retool and optimize for a future state
- #17 This is a small example of one attack vector out of infinite amount of new options to attack and compromise software in the AI age. And this is why we started Pillar. AI is changing the way we build software, introducing new Lifecycle and new security risks. From poisoned training data and in development to jailbreaks and data leakage in production
- #18 This is a small example of one attack vector out of infinite amount of new options to attack and compromise software in the AI age. And this is why we started Pillar. AI is changing the way we build software, introducing new Lifecycle and new security risks. From poisoned training data and in development to jailbreaks and data leakage in production
- #27 This understanding is what sparked our journy here at Pillar.
- #28 Assaf Tomer Mor
- #29 Tomer Mor Assaf
- #30 Mor Assaf Tomer
- #31 This understanding is what sparked our journy here at Pillar.
- #32 This understanding is what sparked our journy here at Pillar.