AI / ML / AQ

AI Automation -- ($300-$400/Hour).

• BLUF: Develop automation processes, workflows using AI (No Code). 

• Steps: (3)

  1. Map the Workflow -- Create the process flow, process model, find the bottleneck... 

  2. Focus on Cash -- Find the low hanging fruit to make or save money for your client(s). -- Example: Create an AI Bot that will (1) generate leads, (2) call/email the leads, (3) answer questions, and (4a) schedule the leads, or (4b) close the sale -- (5) repeat #2 to offer future products & services.

  3. Build Copilots -- Build an "AI Buddy" that will observe, analyze, and offer actionable, context-aware solution at real time to enhance my daily work output. 

• AI Automation Platforms: [YT] 

  1. n8n --

  2. Make --

  3. Zapier --

  4. Gumloop --   

AI Coding / AI SW Development -- ($100-$200/Hour).

• BLUF: Develop software/Apps using AI platforms without using any code (Code less). 

• Steps To DevOps: (3)

  1. Create a course -- Use AI to teach you how to code. -- Example: "Act as a software engineer. Build me a course on how to write code for an app. Suit your prompts  towards  Replit as an example. The outcome is a working prototype having it very detailed and step-by-step.

  2. Find a Problem -- Solve the problem... to sell the solution over, and over again!

  3. Build the Solution --  Implement it, prototype it, make it, build it (The "Physical Twin") 

• AI Coding Platforms: [YT] 

  1. Cursor --

  2. Replit --

  3. Retool --  

AI Content Marketing -- ($200-$300/Hour).

• BLUF: . 

• Use Case:

  1. Arnold's Pump Club -- This is Arnold Schwarzenegger's pod cast, newsletter. He never did a pod cast. All generated by AI systems, voice clones, etc.

• STEPS: (3)

  1. Define the Outcome -- Pod cast (100 episodes), Newsletter, an App, etc.

  2. Create the Content -- Create your content...

  3. Repurpose Content -- Publish everywhere...

Prompt Engineering.

• >Start< -- Asked the AI to "Act as a role" [by Dan Martell]

  1. Define the Role -- Examples: "Act as a cloud security architect" to build a cloud security architecture; "Act as a social media marketer" to...; "Act as a lawyer..."

  2. Provide the Data -- Give examples, info, what good data looks like, etc.

  3. Make the Ask -- Give clear instructions...

  4. Request the Format -- Summary, narrative, paragraph, spreadsheet, bullets, etc.

• Master Prompt -- A detailed instruction manual you give to the AI (about you or what you want to be) at the start (make it in PDF). 

  1. It's a long, structured message (in PDF) that tells the AI: (1) Who it is (e.g., "Act as a helpful travel agent"). (2) What the rules are (e.g., "Only use facts from 2024"). and (3) Exactly how to format the answer (e.g., "Always use a bulleted list and a friendly tone").

  2. It will effectively assign the core role, define the constraints, and establishes the operating procedure for a Large Language Model (LLM) tasked with acting as the Solution Generation Module (The Coach) of a specific subject-program-project (example: "AI Buddy.")

  3. The goal of this Master Prompt is to ensure the LLM consistently delivers actionable, high-impact, personalized, and context-aware suggestions, precisely mirroring the defined architecture.

• System Prompt -- 

Link: https://clouddamcdnprodep.azureedge.net/gdc/gdc5vMw9G/original?ocid=eml_pg420158_gdc_comm_az&mkt_tok=MTU3LUdRRS0zODIAAAGQ4FxLBPj9-dCcdS9RwZcTqx55OmoBKzyxxfuRdVJpBIXYB_iKbdQonO1ccu5VggtSz8GxV0oJI0ySb4U682P39GDN3pIp9OnTVL-Z4sejw5RBk4JOoqeoLvQt

Generative AI.

• BLUF: (1) What is Generative AI -- A type of AI that can create new data, like text, images, music, or even code. (2) Data -- Uses massive datasets of existing content, and they learn to identify the patterns and relationships within that data. Then, they can use those patterns to generate entirely new content that is similar to what they've seen before.

• 5 Stages of Gen AI Adoption: -- Ref by Grammarly -- (1) Aware (2) Experimenting (3) Optimizing (4) Standardizing (5) Transforming.

• Azure Tool: 

  1. Azure Machine Learning allows developers to build applications that use generative models for various purposes. For instance, they can create more realistic product images for e-commerce sites or generate different creative text formats, like poems or code.

  2. Benefits:

  3. Advanced Threat Detection: Generative AI can create realistic simulations of cyberattacks. This can help security teams test their defenses and identify vulnerabilities that traditional methods might miss.

  4. Data Anonymization: Generative AI can create synthetic data that is statistically similar to real data without containing personally identifiable information (PII). This can be used to train security models without compromising real user data.

  5. Security Awareness Training: Generative AI can create realistic phishing simulations or social engineering scenarios. This can help employees learn to identify and avoid these types of attacks.

• Challenges:

  1. Deepfakes and Forgery: Generative AI can be misused to create deepfakes or other forms of synthetic media that can deceive users. In a zero-trust environment, these require extra verification steps to ensure legitimacy.

  2. Data Poisoning: Malicious actors could use generative AI to poison training data for AI security models, causing them to malfunction. Zero-trust principles around data integrity become even more critical.

  3. Explainability and Bias: Generative AI models can be complex, and their decision-making processes are not easily understood. This lack of explainability can make it difficult to trust their outputs in a security context. Zero-trust requires a level of transparency that generative AI models may need to improve.

• Questions:

  1. Detection and Defense:

     • What are the latest techniques for detecting deepfakes and other forms of synthetic media generated by generative AI?

     • How can generative AI be used to create adversarial examples that could fool security models?

     • What are some best practices for hardening systems against generative AI-based attacks?

  2. Data Security and Privacy:

     • How can the privacy of individuals be protected when using generative AI to create synthetic data?

     • What are the risks of data poisoning attacks on generative AI models, and how can they be mitigated?

     • How can generative AI be used to anonymize sensitive data while still preserving its utility for security purposes?

  3. Security Awareness and Implications:

     • How can generative AI be used to improve security awareness training for employees?

     • What are the potential security risks of using generative AI to create marketing content or product descriptions?

     • How can generative AI be used to automate security tasks while maintaining a zero-trust security posture?

  4. Future of Generative AI Security:

     • What are the emerging trends in generative AI that could pose new security challenges?

     • How can the security community stay ahead of the curve when it comes to generative AI threats?

     • What role can governments and regulatory bodies play in ensuring the responsible development and deployment of generative AI?

SandboxAQ: 

• BLUF (4): (1) The tool, SandboxAQ Security Suite provide a 360-degree view of an organization's cryptography usage. (2) The information can be used to identify vulnerabilities, enforce policies, and remediate problems. (3) The Suite helps organizations achieve "Cryptographic Agility" and protect their data from security threats. (4) The tool can test the adoption of AI + Quantum (AQ) to help the U.S. Government and its Allies tackle hard challenges and ensure a better, safer world for all. To protect sensitive data, deploy quantum sensors for applications such as GPS-denied navigation, accelerate material discovery, and more. Our capabilities harness the power of classical computing architecture, enabling us to deliver AQ solutions now.

• SandboxAQ Security Suite Architecture (3-Data Flow Modules).

  1. Column-1: Cryptosense Module (3) -- BLUF: Collects data from the analyzers and builds a comprehensive cryptographic inventory. -- via -- 

     1. Network Analyzer: Captures network traffic and identifies cryptography used to protect data in transit. -- Azure Tools (2) -- (1) Azure Network Watcher: Provides traffic monitoring and analysis capabilities, including packet capture and flow analysis. It can help identify suspicious network activity or anomalies related to data transfer. (2) Azure Sentinel: This security information and event management (SIEM) solution can ingest network logs and data from various sources, allowing you to analyze network-related security events and potential threats to data in transit.

     2. Application Analyzer: Detects and records all calls to cryptographic libraries made by an application at run time, identifying vulnerabilities and policy breaches -- Azure Tools (2) -- (1) MS Defender for Endpoint: This endpoint protection platform includes capabilities for monitoring application behavior and detecting potentially malicious activity. It can monitor API calls and function calls related to cryptographic libraries, providing insights into how applications handle sensitive data. (2) Azure Application Insights: This application performance monitoring tool can be used to track application events and metrics, including calls to cryptographic libraries. This can help identify suspicious behavior or potential vulnerabilities in how applications handle encryption.

     3. Filesystem Analyzer: Scans files to find and parse cryptographic artifacts in data at rest (DAR). -- Azure Tools (2) -- (1) Azure Key Vault: This service securely stores encryption keys and secrets, allowing you to centrally manage and audit access to data encryption resources. While not directly analyzing files, it provides control over the keys used for data encryption at rest. (2) Azure Security Center: This cloud security management platform is for identifying potential security vulnerabilities in Azure resources, including storage solutions where encrypted data might reside. It can help pinpoint potential misconfigurations or access risks related to encrypted data storage.

  2. Column-2: Control Center (6) -- BLUF: Provides a dashboard view of the cryptographic inventory and includes benchmarking tools to monitor the performance of the cryptography. -- via -- (1)  Cryptographic Inventory (2) Performance Monitoring (3) Policy Definition (4) Off-Policy Detection (5) Enforcement (6) Remediation

  3. Column-3: CryptoService (3) -- BLUF: Enforces cryptographic policies and can be used to remediate vulnerabilities. -- via -- (1) High-Level Library (2) Network Proxy (3) Sidecar Proxy

Classical Computing Architecture (CCA):

BLUF: CCA refers to the design and organization of hardware and software components in traditional computers that operate based on binary logic (0s and 1s). It involves various aspects, like:

• Central Processing Unit (CPU): The brain of the computer, executing instructions and performing calculations.

• Memory: Various types like RAM (volatile, holds temporary data) and ROM (non-volatile, stores permanent data).

• Input/Output (I/O): Devices like keyboards, displays, and storage drives for interaction and data exchange.

• Bus: Channels for communication between different components.

• Operating System (OS): Manages hardware resources and provides an interface for applications.

Steps to Implement: (6+4=10)

1. Define Requirements: Identify the purpose of the computer, performance needs, budget, and software compatibility.

2. Choose Hardware: Select the CPU, memory, storage, and other components based on the defined requirements. Compatibility and performance balance are crucial.

3. Install the Operating System (OS): Choose an OS compatible with the hardware and suitable for the intended use (e.g., server, desktop, gaming).

4. Install Applications: Install the necessary software programs for the user's tasks.

5. Configure and Optimize: Set up system settings, install drivers, and adjust configurations for optimal performance and security.

6. Testing and Maintenance: Regularly test the system for functionality and stability, and perform preventive maintenance to avoid issues.

 (>) Additional Considerations:

1. Network connectivity: For internet access and communication with other devices.

2. Security: Implement firewall, antivirus, and other security measures.

3. Power management: Optimize power consumption and cooling for energy efficiency.

4. Physical considerations: Environmental factors like temperature and humidity should be suitable for the hardware.

Azure Tools for Classical Computing Architecture: (8)

1. Azure VM: Create on-demand, scalable virtualized computers running various operating systems and software configurations.

2. Azure Container Instances (ACI): Run containerized applications without managing VMs, ensuring efficient resource utilization.

3. Azure Functions: Serverless platform for running event-driven code without maintaining infrastructure.

4. Azure App Service: Web app hosting platform with built-in scaling and security features.

5. Azure Storage: Scalable and secure storage for various data types like blobs, files, and disks.

6. Azure Cosmos DB: Globally distributed NoSQL database for flexible and highly available data management.

7. Azure AD: Cloud-based identity and access management service for secure authentication and authorization.

8. Azure Monitor: Centrally monitor performance, health, and cost of Azure resources for efficient management.