AI / ML / AQ

Artificial Intelligence / Machine Learning (AI / ML) -- is Data-Driven

Artificial Intelligence + Quantum (AQ)

Automation -- is Process-Driven

AI -- AI Agent -- 101.

AI Agent 101.

AI Agent: An AI Agent is a system that can autonomously perform tasks, make decisions, and interact with its environment based on prompts. It can use APIs to communicate with other systems and microservices to perform specific functions. However, an AI Agent is more autonomous and capable of complex decision-making than a microservice or an API.
1. PQC Contribution (5): (1) Cryptographic Algorithm Design: AI Agents can assist in designing new cryptographic algorithms that are resistant to quantum attacks. They can analyze existing algorithms, identify vulnerabilities, and suggest improvements or new approaches. (2) Cryptanalysis: AI agents can be used to test the strength of cryptographic algorithms by simulating quantum attacks. This helps identify weaknesses and ensure the algorithms are robust against potential threats. (3) Optimization: AI agents can optimize the implementation of cryptographic algorithms, making them more efficient and faster. This is particularly important for PQC, as some quantum-resistant algorithms can be computationally intensive. (4) Automated Key Management: AI Agents can manage cryptographic keys, ensuring they are generated, distributed, and stored securely. They can also automate the process of key rotation and revocation, reducing the risk of key compromise. (5) Threat Detection and Response: AI agents can monitor cryptographic systems for signs of attacks or anomalies. They can quickly respond to potential threats, mitigating the impact of any security breaches.
2. Inventory of all cryptographic systems to ID quantum-vulnerable assets (5): (1) Automated Discovery: AI Agents can automatically discover and catalog all cryptographic systems within an organization. They can scan networks, systems, and applications to identify where cryptographic algorithms are being used. (2) Vulnerability Assessment: Once the cryptographic systems are identified, AI Agents can assess their vulnerability to quantum attacks. They can analyze the algorithms in use and determine if they are susceptible to quantum computing threats. (3) Prioritization: AI Agents can prioritize the identified vulnerabilities based on the risk they pose. This helps organizations focus their efforts on the most critical areas first. (4) Reporting and Compliance: AI Agents can generate detailed reports on the inventory and vulnerability assessment. These reports can be used to ensure compliance with industry standards and regulations. (5) Continuous Monitoring: AI Agents can continuously monitor the cryptographic systems to detect any new vulnerabilities or changes in the environment that might affect the security posture. -- TOOL -- Like TYCHON Quantum Readiness provides automated cryptography discovery and inventory capabilities, helping organizations to rapidly gather, inventory, and prioritize cryptographic systems

AI -- AI Prompt Engineering -- 101. -- (AI Idea Link)

AI Prompt Engineering 101.

AI Prompt Engineering "How To Make an AI Agent Smarter". (23)
- State the Goal First: Begin with the main objective of your request.
- Focus on clarity, specificity, context, format, and iteration. Think of it as giving the AI precise instructions and the necessary background information to understand and execute your request effectively.
- Be Crystal Clear and Specific.
- Avoid Ambiguity: Don't use vague language. For example "write something about cats," try "write a three-paragraph essay comparing and contrasting the characteristics of Persian and Siamese cats, including their temperament, grooming needs, and common health issues."
- Define the Task Explicitly: Clearly state what you want the AI to do (e.g., "summarize," "translate," "generate," "analyze," "classify," "brainstorm").
- Specify the Output Format: Tell the AI how you want the information presented (e.g., "a bulleted list," "a table with three columns," "a short story with a clear beginning, middle, and end," "JSON format").
- Set Length Constraints: If you have a specific length in mind, state it (e.g., "in under 100 words," "approximately 500 words," "no more than five bullet points").
- Provide Relevant Context.
- Provide Background Information: Give the AI any necessary background information it needs to understand the prompt. For example: Imagine you're briefing a human – what would they need to know to do the task well?
- Specify the Audience: If the output is for a specific audience, mention it (e.g., "Explain this concept to a five-year-old," "Write a marketing email for potential investors," "Draft a technical report for engineers"). This helps the AI tailor its language and complexity.
- Mention Relevant Keywords or Concepts: Include keywords that are central to your request to guide the AI's understanding.
- Define Constraints and Boundaries.
- Specify What NOT to Include: Sometimes it's helpful to tell the AI what to avoid (e.g., "do not include any personal opinions," "exclude any information about the company's financial performance," "avoid using jargon").
- Set Tone and Style: Specify the desired tone (e.g., "formal," "informal," "humorous," "professional," "persuasive") and style (e.g., "concise," "descriptive," "analytical").
- Mention Data Sources (if applicable): If you want the AI to consider specific information, you can try to guide it (though direct access to external URLs varies by AI). For example, "based on the information in the provided article..."
- Structure Your Prompts Effectively.
- Use Clear Formatting: Break down complex prompts into logical sections using bullet points, numbered lists, or clear paragraphs. This makes the prompt easier for the AI to parse.
- Provide Examples (if helpful): Showing the AI an example of the desired output can significantly improve the results. For instance, "Follow this format: [Product Name] - [Key Feature 1], [Key Feature 2], [Benefit]."
- Iterate and Refine:
- Don't Expect Perfection on the First Try: AI agents learn from your feedback. If the initial output isn't what you're looking for, don't be afraid to refine your prompt.
- Analyze the Output: Identify what aspects of the AI's response were good and what could be improved.
- Adjust Your Prompt Based on the Output: Rephrase your prompt, add more specific instructions, or provide additional context based on the AI's previous response. This iterative process helps you "teach" the AI what you need.
- Experiment with Different Phrasing: Sometimes, rewording your prompt can lead to significantly different and better results.

Examples of Good & Poor Prompts:

Poor Prompt: "Write about a trip." (Too vague)
Good Prompt: "Describe a memorable three-day hiking trip in the Appalachian Mountains, focusing on the scenery, the challenges faced, and the personal reflections of the hiker. Write in a descriptive and evocative style, aiming for approximately 400 words." (Clear, specific, provides context and formatting guidance)

AI -- Examples of Good & Poor Prompts -- 101

Examples of Good & Poor Prompts:

- BLUF: By understanding these principles and experimenting with different prompting techniques, you can effectively harness the power of generative AI to achieve your desired outcomes. Remember that prompt engineering is an iterative process, and refining your prompts based on the AI's output is often key to success.
- Clarity and Specificity: The more precise you are, the better the AI can understand your intent. Avoid vague language.

- 1. Poor: "Write a story."
  2. Good: "Write a short science fiction story set in Virginia Beach in the year 2150, featuring a marine biologist who discovers a new form of bioluminescent sea life."
  3. Poor: "Write about a trip." (Too vague)
  4. Good: "Describe a memorable three-day hiking trip in the Appalachian Mountains, focusing on the scenery, the challenges faced, and the personal reflections of the hiker. Write in a descriptive and evocative style, aiming for approximately 400 words." (Clear, specific, provides context and formatting guidance).
- Context: Providing background information helps the AI generate more relevant and coherent responses.
  1. Without Context: "What are the benefits of renewable energy?"
  2. With Context: "Explain the economic and environmental benefits of solar and wind energy in the context of reducing reliance on fossil fuels in the United States."
- Format: Specify how you want the output to be structured.
  1. No Format: "Tell me about the planets in our solar system."
  2. With Format: "List the planets in our solar system in order from the Sun, including their primary composition in a bulleted list."
- Role-Playing/Persona: Asking the AI to adopt a specific persona can influence the style and tone of the output.
  1. Without Persona: "Explain the theory of relativity."
  2. With Persona: "Explain the theory of relativity as if you were Albert Einstein speaking to a high school physics class."
- Tone and Style: Specify the desired tone (e.g., formal, informal, humorous, professional) and writing style (e.g., descriptive, analytical, persuasive).
  1. Generic: "Write a marketing email for a new product."
  2. Specific Tone: "Write a short, enthusiastic marketing email announcing the launch of our new noise-canceling headphones, targeting young adults."
- Constraints: Define what the AI should or should not include in its response.
  1. Without Constraints: "Summarize the history of the internet."
  2. With Constraints: "Summarize the key milestones in the history of the internet in under 150 words, focusing on the developments before the year 2000 and excluding any discussion of social media."
- Examples: Providing examples of the desired output format or style can be very helpful.
  1. "Write a product description for a coffee maker following this format: [Product Name] - [Key Feature 1], [Key Feature 2], [Benefit]."
- Iteration and Refinement: Don't be afraid to adjust your prompts based on the AI's initial responses. You can ask the AI to revise, elaborate, or change the style of its output.
- Writing & Content Creation:
  1. "Write a three-paragraph blog post about the best local seafood restaurants in Virginia Beach, highlighting their signature dishes and ambiance."
  2. "Compose a persuasive essay arguing for the importance of funding space exploration, targeting a general audience."
  3. "Create a short poem about the feeling of walking on the beach at sunrise in Virginia Beach."
  4. "Write a screenplay scene where two characters discuss a mysterious artifact they found on the beach."
- Marketing & Business:
  1. "Brainstorm five catchy slogans for a new eco-friendly surf shop in Virginia Beach."
  2. "Write a social media post announcing a sale on paddleboards, targeting outdoor enthusiasts in the Hampton Roads area."
  3. "Generate a list of ten potential topics for blog posts aimed at tourists visiting Virginia Beach in the summer."
  4. "Create a template for a customer service email addressing a complaint about a hotel booking."
- Creative Arts & Design:
  1. (For Image Generation AI): "Generate a photorealistic image of a sunset over the Atlantic Ocean as seen from the Virginia Beach boardwalk in the style of a professional photograph."
  2. (For Music Generation AI): "Compose a short, upbeat jingle suitable for a tourism advertisement for Virginia Beach."
  3. "Describe the visual elements of a minimalist logo for a local coffee shop named 'Ocean Grind'."
- Education & Learning:
  1. "Explain the concept of coastal erosion in simple terms suitable for a middle school science class, using examples relevant to the Virginia coastline."
  2. "Create a multiple-choice quiz with five questions about the history of Virginia Beach."
  3. "Generate a study guide outlining the key points of the American Revolutionary War."
- Coding & Development:
  1. "Write a Python function that calculates the average daily temperature given a list of temperature readings."
  2. "Generate the HTML and CSS code for a simple webpage displaying information about local events in Virginia Beach."

AI -- Key Responsibilities -- 101.

AI 101.

Key Responsibilities:
1. Design and architect scalable and robust AI/ML solutions aligned with business needs.
2. Lead the end-to-end lifecycle of AI/ML projects, including requirement analysis, system design, algorithm selection, implementation, and deployment.
3. Collaborate with various teams to integrate AI/ML solutions into existing systems and business processes.
4. Stay abreast of the latest trends in AI/ML technologies, applying best practices in solution development and team mentorship.
101: https://www.youtube.com/watch?v=1aM1KYvl4Dw

AI -- AI Readiness Checklist, by Azure

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

AI -- Generative AI.

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?

AQ -- AI + Quantum (AQ)

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

AQ -- Classical Computer Architecture

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)

Define Requirements: Identify the purpose of the computer, performance needs, budget, and software compatibility.
Choose Hardware: Select the CPU, memory, storage, and other components based on the defined requirements. Compatibility and performance balance are crucial.
Install the Operating System (OS): Choose an OS compatible with the hardware and suitable for the intended use (e.g., server, desktop, gaming).
Install Applications: Install the necessary software programs for the user's tasks.
Configure and Optimize: Set up system settings, install drivers, and adjust configurations for optimal performance and security.
Testing and Maintenance: Regularly test the system for functionality and stability, and perform preventive maintenance to avoid issues.

(>) Additional Considerations:

Network connectivity: For internet access and communication with other devices.
Security: Implement firewall, antivirus, and other security measures.
Power management: Optimize power consumption and cooling for energy efficiency.
Physical considerations: Environmental factors like temperature and humidity should be suitable for the hardware.

Azure Tools for Classical Computing Architecture: (8)

Azure VM: Create on-demand, scalable virtualized computers running various operating systems and software configurations.
Azure Container Instances (ACI): Run containerized applications without managing VMs, ensuring efficient resource utilization.
Azure Functions: Serverless platform for running event-driven code without maintaining infrastructure.
Azure App Service: Web app hosting platform with built-in scaling and security features.
Azure Storage: Scalable and secure storage for various data types like blobs, files, and disks.
Azure Cosmos DB: Globally distributed NoSQL database for flexible and highly available data management.
Azure AD: Cloud-based identity and access management service for secure authentication and authorization.
Azure Monitor: Centrally monitor performance, health, and cost of Azure resources for efficient management.

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