Aporia

🚨 New on the #GenAI Academy: "Red Teaming for LLMs: A Comprehensive Guide" With the rise of AI-powered systems like chatbots and medical assistants, ensuring safety and ethics in AI is more crucial than ever. Red teaming—a strategy originally from military adversary simulations—has emerged as a vital tool to identify security vulnerabilities in LLMs. By simulating real-world attacks and stress-testing models, red teaming uncovers potential threats like misinformation, biases, and security flaws, paving the way for safer and more reliable AI systems. 🔗 Learn how red teaming enhances #AI safety and reliability: https://lnkd.in/dtFc6gzm #redteaming #llms

Aporia is ranked #2 in G2's “Top 16 Generative AI Infrastructure Software Products”. This ranking reflects the genuine satisfaction of our users, who find Aporia's product invaluable for deploying secure and reliable AI through the use of Guardrails. 🔗 View the rankings here: https://lnkd.in/dNjJP-BA Thank you to our incredible team and loyal customers for helping us achieve this recognition. We remain committed to delivering the best generative Guardrails that are setting the standard for AI security and reliability. #G2 #AISecurity #TopAIInfrastructure

Proud to be sponsoring such an exciting event! 🔥 MLOps World

Protecting against the accidental exposure of Personally Identifiable Information (PII) is essential when deploying AI systems. At Aporia, we provide the most robust anomaly detection engine designed specifically for this purpose. With Aporia, AI engineers can seamlessly integrate PII protection policies into their applications. This includes safeguards not only against inadvertent PII leakage by the AI itself but also by users during interactions. Our platform offers customizable options for the types of PII you wish to protect, including: ✔ Phone numbers ✔ Email addresses ✔ IBANs ✔ Credit card information ✔ Names ✔ Social Security Numbers ✔ Currencies And, in case of PII exposure, you can define the response action: ➡ Rephrase ➡ Override ➡ Issue a warning Every action and incident is automatically logged in the Session Explorer and Dashboard for real-time monitoring and analysis. With Aporia, you're not just deploying AI—you're deploying secure AI. 📹 Check out how this policy integrates below, or read our Docs for more info 🔗 https://lnkd.in/dfCvKZj8 #AI #DataPrivacy #PIIProtection #AIDevelopment #AISecurity #SecureAI #Aporia

Join me at the LangTalks Meetup to understand the fundamentals of building agents for production use. I will cover all the concepts and terms you need to know when building LLM-based agents and lay out the fundamentals for the next speakers to discuss advanced concepts and challenges. We currently have a waiting list for registration due to limited availability. The meetup will be in Hebrew and won't be recorded, so hurry up and register here: https://lnkd.in/dn38M28f Thanks to our sponsors: Zenity, Aporia, Lasso Security, Amazon Web Services (AWS), Sap.Hi

The Interview vs. The Job: AI Engineers ~ Signed *Not the AI Engineer* #AImeme #AIengineer

Get ready to dive deeper into AI and its observability practices! 🤖      The latest Q/A interview of #ExtraMile by HiTechNectar is live now! 🌠  In this insightful conversation, we are delighted to speak with Liran Hason, CEO and co-founder of Aporia, a front-running AI guardrails, observability, and monitoring firm. 💡  As a true AI enthusiast and impactful leader, Hason has taken Aporia to the top position among the trusted tech companies worldwide that focus on safe AI integration. 🎇      Gain insights on the professional journey of our guest alongside in-depth takeaways on Aporia’s services, accomplishments, protected AI implementation, and much more. ✅     Click here now: https://lnkd.in/dhi_tE4x   #HiTechNectar #htn #AI #aisecurity #aporia #interviewseries #q&a #aiobservability #security

What makes Aporia stand out in AI hallucination detection, surpassing even OpenAI GPT-4o and NVIDIA/NeMo? The secret sauce… ⚙️ A multiSLM detection engine! Aporia's AI Guardrails leverage a unique infrastructure that trains multiple specialized small language models (SLMs) instead of depending on a single large language model. Combine this with proprietary databases that combine: → insights from LLM applications → open-source data → in-house AI research And you've got an AI Guardrails system that outshines them all! I dove into the recently released benchmark report that explains the multiSLM detection engine concept (the AI nerd in me can't get enough of this! 🤓) It covers a deep dive into benchmarking too, based on detection accuracy, latency, security and reliability. Check it out here! https://lnkd.in/eWUAyJf2 Does your organization have guardrails in place for your chatbots? If not, are you aware of the benefits over prompt engineering? Guardrails are the only scalable, reliable, accurate way forward. Learn more through the report I shared and feel free to reach out for further resources, I'll be happy to share! 💚 I am a proud partner of Aporia, helping bring responsible and safe approaches for AI to the masses. #artificialintelligence #generativeai #data #chatbots #genai

Ever wondered how Notion Ask AI works? Here's a glimpse into the architecture of a real-world RAG system: 1. Notion receives a question from a user. 2. The question is passed to an LLM provider (OpenAI / Anthropic) - If the request does not require searching the Notion workspace, the LLM generates a response at this point, skipping the rest of the process. - If the user's request requires searching their workspace, the LLM generates the most relevant search query for the user's request. 3. The query is passed to a vector database (Pinecone), which identifies a list of pages based on their relevance to the query. - For each page in the Notion workspace, an embedding is generated using the OpenAI embeddings API. 4. Notion sends the query — along with the pages identified by the vector database — to a self-hosted LLM, where the pages are refined and ranked by relevance to the query. 5. The query, refined list of pages, and page rankings are processed by the LLM provider to generate a response that fulfills the user's request. 6. Notion processes the output to ensure it adheres to the right format and language, then displays it to the user. Source: https://lnkd.in/d6xe6pmD

No one will remember: ➡ Your salary ➡ How “busy you were” ➡ How many hours you worked  People will remember: ➡The time a dog was in the office Happy International Dog Day🐶 #InternationalDogDay