What is AI Security Observability and AI Security Resilience?   AI Security Observability is the capability to understand the state of AI system and Security Intelligence Indicators of compromise, attack, Proactive Threat modelling by analyzing its user, system prompts, responses, AI access. Model logs, AI metrics, and AI traces, Agentic AI Tool calling, response, RAG […]

Rise Of AI and Explosion of Newer risks: The ALERT AI’s Unique Value Proposition   Alert AI’s Unique Value Proposition and Key Differentiators     Holistic AI Security: Alert AI offers a unified platform for AI Security, AI Posture, AI Vulnerabilities, AI Access Control, AI Tool Leakage Protection, and managing AI Polices and Compliance throughout […]

The rapid adoption of Artificial Intelligence, from intelligent agents to sophisticated copilots, is transforming the enterprise landscape. But with great innovation comes great responsibility, particularly concerning security. Traditional security measures are often ill-equipped to handle the unique challenges posed by AI’s dynamic and interconnected nature. This is where the Alert AI “Secure AI Anywhere” Zero […]

Securing Enterprise Copilots and Enterprise AI Tools:  A Proactive Approach with Alert AI Secure AI Anywhere,  Zero-Trust AI Security Gateway to protect and de-risk new AI tools.. The rise of AI-powered productivity tools like Microsoft 365 Copilot, AI Apps, AI Tools, Enterprise AI Agents presents unprecedented opportunities for enterprise efficiency. Copilot can revolutionize how IT […]

Attacks on AI Agent Systems - MCP service exploitation via Rougue Tools, Tool Appropriation/Shadowing, Tool Poisoning, Supply chain, Cross server attacks AI Agent Systems are susceptible to exploitation via 3 key taxonomy of Attack vectors based on techniques and IOC and IOA. Tool Appropriation category Supply chain attack category Cross server attack category Exploiting Model [...]

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 [...]

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 and Tracking Model Lineage & Data provenance 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 [...]

Top Alerts in Custom LLM Applications and AI Agents - Metrics, Evaluations, 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 [...]

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

Understanding Data Poisoning: Data Spills, Leaks, Contamination in AI Pipelines And, Detection and  Alerts from Data Curation to Data Lineage, And Adversarial Data attacks.   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, […]

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 [...]