Using Zero Trust strategies forresponsible Generative AIadoption
Jim Reavis, CEO, Cloud Security AllianceMay 2024

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How CSA is thinking about
Generative AI

With irony

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Cloud history by version number
Cloud 1.0 – Cloud delivers traditional IT services (e.g. Virtual Machines) innew business model (2008-2016)
Cloud 2.0 – Cloud Native Technologies & Frameworks: DevOps,Containers, Serverless, CNAPP, etc. Pandemic accelerates move to cloudand rise of Zero Trust as the strategy for securing cloud sprawl (2016-mid2022)
Cloud 3.0 – Tech economic downturn, Rise of Generative AI and its
merger with Cloud 2.0 (mid 2022-)

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History repeating itself with GenAI as Cloud
Layered Cloud Model From the 2009 CSA archives
L a y e re d C l o u d Mo d e l
SaaS is theprimary delivery
mechanism for AI
Robust API layerfrom LLMs

L a rg e L a n g u a g e M o d e l s(LLMs) are part of cloudinfrastructure (IaaS)
Public/Private/Hybrid/M
ulti LLMs• Enterprises will varyin adoption pace
• Soon, GenAI will bepervasive in SaaS,App stores• Viral adoption
impacts ALL!

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Dimensions of the AI question CSA is investigating• Improving cybersecurity through appropriate use of AI• How AI can be directly attacked to assist AI’s continuousimprovement• How malicious actors can, will and are using AI• AI usage guidelines tied to existing security & governanceframeworks• Build the new tools and frameworks need for AI’s uniquecharacteristics• Anticipate future challenges of AI and set a roadmap in place

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Investigating Large Language Models
A Large Language Model is an artificial intelligence model that's trained to understand andgenerate human-like text.
1. Tokenization: Text broken down into smaller units
2. Transformation: Tokens processed by the model in relation to all the other tokens
3. Generation: Model predicts the following tokens in succession• Temperature is a parameter controlling how random vs deterministic the output isIt is a statistical model, not consciousness
Recommend Cloud (Public, Private, Hybrid, Multi LLMs) plus Edge nomenclature
Enabled by astonishing amounts of compute power
Three LLMs are considered the Frontier Models: OpenAI, Anthropic & Google Deepmind

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9Thinking About Near-Term Issues• Data Leakage from LLM queries isprobably overhyped• Using your Data with LLMs has thetypical data lifecycle /data
governance issues• Prompt Injection has tremendous
potential for attacks• Data poisoning & model evasion• Hallucination, Deepfakes & Bias
• AI-enabled malicious attackers, e.g.
automated vulnerability discovery &deception attacks

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LLM Data leakage is traditional data security
Data Leakage Risk is Low

Scenario 1

LLM (OpenAI)

Model Hosting API
Validation

Employees

Orchestration

Scenario 2

ModelsAgentsStorage(OpenAI,LLAMA..)

Plugins

Vector DBs

CustomCachesFine Tuning

LLM
Customers

Data
Ingestion

Cleaning

Tokenize

Orchestration
AgentsVector DB
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Data Leakage becomes a real
threat. ACL is critical11• Center is positioning Zero Trust as “Philosophy informing Strategy”••••

Assume everything can be compromised
Protect assets with least privilege access
Identity as a foundation
Continuous verification•

Training, Research, Resource Hub•

Full Exam & Certification available•

www.cloudsecurityalliance.org/zt

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12• ZT Weakness is “Cloud Native”• Priority to apply ZT to DevOps &Microservices• 72% of containers live less than 5 minutes• 90% of granted permissions are not used
• 87% of container images have high orcritical vulnerabilities• 15% of high or critical vulnerabilities are inuse• Source: Sysdig Cloud Native Security & Usage
Report 2023•••••••

Extend 5 Pillars to “Cloud Native”
Container and Serverless deployment
Focus on Automation & Tooling
Layer 7 Access Control
Infrastructure as CodePolicy as CodeUse 5 Step process in Cloud Native Process

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13• Developed Cloud Native ZT POC• Protect FedRAMP Authorization
Boundary containing healthinformation• Developed in AWS public cloudwith mock data
• Heavy reliance on Serverless• Use Terraform and Open Policy
Agent to orchestrate

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Zero Trust should be central to AI Data Transformation &Governance
Data Transformation Steps

Data Governance Framework

1. Data Collection and Integration

1. Governance Structure

2. Data Cleaning and Preprocessing

2. Data Policies and Standards

3. Data Augmentation

3. Data Quality Management

4. Data Annotation and Labeling

4. Data Privacy and Security

5. Data Storage and Management

5. Data Access and Control

6. Data Privacy and Security

6. Training and Awareness7. Ongoing Data Monitoring and Quality Assurance 7. Technology and Tools
8. Compliance and Regulatory Requirements
9. Performance Measurement

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ExecutiveCommittee

CSA AI Safety Initiative
Executive Committee• Frontier Model Companies• OpenAI• Google Deepmind• Anthropic• Hyperscalers• Amazon AWS• Google Cloud• Microsoft• CISA (USA)• Key stakeholders fromgovernment and industry
around the world

Non Profit Council

Corporate MemberCouncil

AI Provider
Certification &Compliance

Professional
Development &Credentialing

Global Chapters

Organizational
Roles &
Responsibilities

Controls
Framework

ResearchWorking Groups

Technology &Risk

Governance &Compliance

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International Computing Centre

Additional Support

Corporate Member Council• 1,500 volunteer experts inresearch working groups• Represent 500 corporate
members from AI, Cloud,Cybersecurity, Audit and all
critical infrastructure industries

Copyright © 2024 CLOUD SECURITY ALLIANCE• Chapters from over 60 countries• www.cloudsecurityalliance.ai

16Summary
Pervasive Generative AI is the biggest technology trend to date
Cloud 3 is the merger with AI
The world is hungry for data – your data
Modern identity strategy
Robust data governanceZero Trust is key

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Thank You!

jreavis@cloudsecurityalliance.org

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