Threats of GenAI | New Evolving and Changed Threats

While consideiring threats of genAI - use these categories - 1. Expansion to existing AI threats, 2. Change to existing threats 3. New threats. the new threat with generative AI compared to predctive AI is, gen AI can generate new trajectories of data. These new trajectories has more blind spots.
threats



Generative AI Threats

Generative Artificial Intelligence (AI) has the potential to revolutionize various industries and enhance human capabilities. However, like any powerful technology, it also poses certain threats that need to be carefully considered and addressed. Here, we categorize generative AI threats into three main categories:

1. Expansion to Existing Threats

Threat Description
Disinformation Generative AI can be used to create highly convincing fake news and misinformation, exacerbating existing issues of misinformation and propaganda.
Cybersecurity Attacks AI-generated malware and phishing attacks can become more sophisticated and difficult to detect, leading to increased cybersecurity risks.
Privacy Breaches AI-generated content can be used to manipulate individuals or breach their privacy, leading to potential exploitation of personal data.

2. Changing Threats

Threat Description
Deepfakes Generative AI can be used to create highly realistic deepfake videos and audio, leading to potential misuse in spreading misinformation or impersonation.
Identity Theft AI-generated content can be used to create fake identities or forge documents, making it easier for cybercriminals to engage in identity theft.
Algorithmic Bias Generative AI models can inherit biases present in training data, leading to discriminatory outcomes in decision-making processes.

3. New Threats

Threat Description
AI-Enabled Social Engineering Generative AI can be used to create personalized social engineering attacks, making it harder for individuals to discern between genuine and fake communications.
AI-Generated Propaganda AI-generated content can be used to manipulate public opinion and influence political discourse, posing new challenges to democratic processes.
Autonomous Weapon Systems Generative AI in weapon systems can lead to the development of autonomous weapons that could pose significant ethical and security concerns.

It is crucial for policymakers, researchers, and industry stakeholders to proactively address these generative AI threats through robust regulations, ethical guidelines, and ongoing research to mitigate potential risks and safeguard against misuse.
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Dataknobs Blog

10 Use Cases Built

10 Use Cases Built By Dataknobs

Dataknobs has developed a wide range of products and solutions powered by Generative AI (GenAI), Agent AI, and traditional AI to address diverse industry needs. These solutions span finance, healthcare, real estate, e-commerce, and more. Click on to see in-depth look at these use cases - Stocks Earning Call Analysis, Ecommerce Analysis with GenAI, Financial Planner AI Assistant, Kreatebots, Kreate Websites, Kreate CMS, Travel Agent Website, Real Estate Agent etc.

AI Agent for Business Analysis

Analyze reports, dashboard and determine To-do

DataKnobs has built an AI Agent for structured data analysis that extracts meaningful insights from diverse datasets such as e-commerce metrics, sales/revenue reports, and sports scorecards. The agent ingests structured data from sources like CSV files, SQL databases, and APIs, automatically detecting schemas and relationships while standardizing formats. Using statistical analysis, anomaly detection, and AI-driven forecasting, it identifies trends, correlations, and outliers, providing insights such as sales fluctuations, revenue leaks, and performance metrics.

AI Agent Tutorial

Agent AI Tutorial

Here are slides and AI Agent Tutorial. Agentic AI refers to AI systems that can autonomously perceive, reason, and take actions to achieve specific goals without constant human intervention. These AI agents use techniques like reinforcement learning, planning, and memory to adapt and make decisions in dynamic environments. They are commonly used in automation, robotics, virtual assistants, and decision-making systems.

Build Dataproducts

How Dataknobs help in building data products

Building data products using Generative AI (GenAI) and Agentic AI enhances automation, intelligence, and adaptability in data-driven applications. GenAI can generate structured and unstructured data, automate content creation, enrich datasets, and synthesize insights from large volumes of information. This helps in scenarios such as automated report generation, anomaly detection, and predictive modeling.

KreateHub

Create New knowledge with Prompt library

At its core, KreateHub is designed to enable creation of new data and the generation of insights from existing datasets. It acts as a bridge between raw data and meaningful outcomes, providing the tools necessary for organizations to experiment, analyze, and optimize their data processes.

Build Budget Plan for GenAI

CIO Guide to create GenAI Budget for 2025

CIOs and CTOs can apply GenAI in IT Systems. The guide here describe scenarios and solutions for IT system, tech stack, GenAI cost and how to allocate budget. Once CIO and CTO can apply this to IT system, it can be extended for business use cases across company.

RAG For Unstructred and Structred Data

RAG Use Cases and Implementation

Here are several value propositions for Retrieval-Augmented Generation (RAG) across different contexts: Unstructred Data, Structred Data, Guardrails.

Why knobs matter

Knobs are levers using which you manage output

See Drivetrain appproach for building data product, AI product. It has 4 steps and levers are key to success. Knobs are abstract mechanism on input that you can control.

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