Effective Enterprise Architecture (EA) for Generative AI  Applications   Effective enterprise architecture practices deliver remarkable IT and business benefits. Today’s Enterprise Architectures are driving Organizations AI Transformation. A remarkable pattern of  Enterprise Architecture is  Architectural layers and Separation of Concerns. When it comes to Generative AI in Business, Enterprise architecture  layers are Business, Data, Technology, […]

INTEROPERABLE, END-TO-END, EASY TO DEPLOY AND MANAGEALERT AI | SECURITY PLATFORM FOR GEN AI APPLICATIONS AND WORKFLOWS   MADE FOR ENTERPRISESecurity platform for Generative AI applications   Alert AI platform  Services AI Visibility and AI Asset Access Usage Analytics Tracking and Lineage Analysis Adversarial ML Detections in AI Footprint Alert Engine Data leakage AI Incidents [...]

Strategies for New RisksThe Paradigm of Security: Generative AI in Business In the shifting landscape of Business ... Generative AI is game-changing and transforming the Industries. GenAI is the new standard of Business. A new IT Perimeter. Organization's Data Science is new Security Realm. Generative AI is new attack vector endangering enterprises mired with high [...]

Prompt security and Tokenizer security Tokenizer manipulation attacks Adversaries can modify tokenizers configuration to corrupt the output of the model Recommendations Tokenizer manipulation Detection Versioning tokenizers Auditing tokenizers Logging In Large language models (LLMs): 1. Prompts are passed through Tokenizer 2. Tokenizer creates an array of token IDs a list of integers 3. LLM outputs [...]

  Generative AI Vulnerability Database An GenAI vulnerability database is a repository that catalogs known vulnerabilities and security issues in Generative AI applications. These databases help researchers, developers, and organizations to:   Identify Common Vulnerabilities: Understand frequent weaknesses in AI systems, such as data poisoning, adversarial attacks, and model inversion. Mitigate Risks: Implement best practices […]

What is the integration of LLM Evaluation with Pipelines? The integration of Large Language Model (LLM) evaluation with pipelines involves systematically incorporating the process of assessing the performance and effectiveness of LLMs into the broader workflow of data processing, model training, and deployment. This integration ensures that the LLMs are evaluated continuously and consistently, facilitating [...]

Enhancing Model Governance Key Components of Model Governance: Model Development Guidelines: Documentation: Maintain comprehensive documentation of model objectives, design, assumptions, and limitations. Transparency: Ensure transparency in model building, including data sources, preprocessing steps, feature selection, and algorithm choices. Model Validation and Testing: Validation Frameworks: Implement rigorous validation frameworks to test model performance across different datasets [...]

Data Spills, Leaks, Contamination in AI Pipelines   Data breaches have been significantly increasing. Records of confidential Data prior to the digital period were prone to security breach through hardcore in person theft. With the evolving digital world, data breach of all kinds is happening through cyber attacks. Emerging Artificial Intelligence which relies wholly on […]

Alert AI  Alerts in LLM Metric Evaluation risks   Introduction   LLMs encounter many issues when running but is it easy to detect these issues? To solve this issue, Alert AI uses Detections. An LLM Alert is a detailed alert that describes errors and provides a recommendation to users and developers. When alerts aren't used [...]

Alerts and Risks in Generative AI applications and workflows Metric events , logs, events,  traces Anomalies Vulnerabilities Risks Threats   Introduction  Generative AI  Large language models (LLMs) are deep learning algorithms that can generate new content, such as text, images, music, or code. Using very large datasets they can recognize, summarize, translate, predict, and generate [...]