Generative AI Security
Security for Generative AI applications and workflows
Generative AI in Energy, Oil and Gas Industries
Introduction
The Energy, Oil & Gas industry accounts for around 7% of global GDP and provides the dominant source of energy and fuel. Generative AI and predictive analytics are opening new possibilities for Energy, Oil & Gas industries.
Technology will enable this production increase and Generative AI is projected to be a key driver. The potential of Generative AI for business use cases in Energy industry use cases is huge.
Industries in this sector will continue developing Generative AI applications. Large language models (LLMs) are changing the Energy Industry and Business workflows. Energy industry also can leverage Generative AI to Transform Customer Service enhance Productivity.
Generative AI applications can enhance demand forecasting by analyzing historical consumption data, weather patterns, and market dynamics, enabling more accurate predictions and efficient energy distribution.
These models can also improve customer service by handling inquiries, providing real-time billing information, and offering personalized energy-saving tips through AI-powered virtual assistants.
Predictive maintenance of critical infrastructure, such as power grids and pipelines, is another key application of Generative AI applications by analyzing sensor data, these models can detect early signs of potential issues, allowing for timely intervention and preventing costly outages.
Generative AI applications can also assist in optimizing energy pricing strategies by analyzing market trends and regulatory changes, helping utilities remain competitive.
Furthermore, Generative AI applications can support the integration of renewable energy sources by forecasting generation levels and balancing supply and demand more effectively.
This contributes to a more reliable and sustainable energy system. Overall, Generative AI application provide manufacturing industry companies with the tools to improve operational efficiency, reduce costs, and enhance customer satisfaction.
Industrial Use case
Operations Insights
Automated Reports generation for Operation data analysis
Business Benefits
• Reduction of manual effort
• Ease of use-conversational
• Accurate
• Precise reports
• Enable productivity
• Data science citizens across Organization can use Operation data
• Advanced Decision making
User Benefits
• Using Natural language instead Programming Language.
Main Components of Generative AI Application Workflow
Automated Reports generation for Operation data analysis
User query
Transforming a natural language prompt into executable
• AWS lambda function
• Azure functions
• Python code
• Kubernetes job
Analytics Application
• SQL generation for data retrieval
• Spark Query, Flink, Beam etc OR
• AWS EMR
• Azure HD
• Google Dataflow
Interactive Response Context
Dashboard Insights Q&A App
Data sets
Operation data from Field systems
Data sources from Sub-Systems
Users
Data science citizens for analysis
Foundation Model selection
Anthropic Claude 3 models
zero-shot and few-shot prompting
Model selection, Evaluation and cost-performance
Design prompt for each component
Test responses
Conversational, Accurate, and Precise
Question rewriter
LLM model invoke for reformulating user queries to better align with the document space
improve the accuracy and relevance of the information retrieved
API service
Interface APIs for multi-modal front-end app
Python code generator
LLM code generation for downstream analytics and report generation
SQL generator
Text to SQL and Context injection with RAG for Operation Database
Data-to-text generator
Data-to-text Pipeline
Alert AI Security guardrails
Easy to deploy and manage Generative AI application security integration
Protection for Generative AI attack vector and vulnerabilities
Intelligence loss prevention
Domain-specific security guardrails
Eliminates Security blind spots of Gen AI Application for InfoSec team
Block diagram
Agent work flow with RAG
Block diagram
Operation Insights use case Generative AI application
Security Risks of Generative AI in Business
Generative AI in Business Applications introducing a host of new Attack vectors and threats that escape traditional firewalls.
“The risks are of High stakes..”
“Unguarded would lead to Major fallouts…”
Security risks using Generative AI in Business application
Data Privacy and Security
Sensitive Data Exposure
- Generative AI applications in Business using LLMs can inadvertently reveal sensitive information
- LLM is trained on proprietary or customer data augmentation, there’s a risk of that information being exposed
Data Breaches
- Generative AI applications in Business must have protection, if an LLM’s underlying data infrastructure is compromised, attackers gain access to confidential financial data.
Copyright and Legal information
- Generative AI applications in Business using Large Language Models (LLMs) must be designed to respect copyright laws by avoiding the unauthorized use of copyrighted text during training and deployment, ensuring that all content generated adheres to legal and ethical standards.
Sensitive content exposures
- Generative AI applications in Business using LLMs must be carefully managed to prevent the generation or dissemination of sensitive or harmful content, safeguarding user interactions and upholding privacy and security protocols.
Integrity of AI application
- Maintaining the integrity of Generative AI applications in Business using LLMs involves implementing rigorous security measures and validation processes to protect the system from tampering and ensure reliable and unbiased outputs.
Tokenizer Manipulation Attacks
- Tokenizer manipulation attacks in Generative AI applications in Business prone to exploit and vulnerabilities in text processing, potentially causing incorrect or malicious outputs, necessitating robust defenses and regular updates to counteract such risks.
Bias and Fairness
Algorithmic Bias
- Generative AI applications in Business using LLMs can perpetuate and even amplify biases present in their training data, leading to unfair treatment of certain groups of customers.
- This is particularly concerning in credit scoring, loan approvals, and other financial decisions.
Discrimination
- Unchecked biases can result in discriminatory practices, which can lead to regulatory and reputational risks for financial institutions.
Manipulation
- Spills, leaks, contaminations during training, feedback loop, retraining, inference time attacks
Phishing and Social Engineering
- Generative AI applications in Business can be used to generate highly convincing phishing emails or messages, making it easier for attackers to deceive employees or customers.
Fraudulent Transactions
- Generative AI applications in Business using Advanced LLMs could be used to manipulate transaction data or create false documentation, making fraud detection more challenging
Operational Risks
Model Inaccuracy
- Inaccurate predictions or decisions made by LLMs can lead to financial losses.
- For example, incorrect risk assessments or credit evaluations can impact the financial health of an institution.
Overreliance on Automation without survilliance
- Unguarded dependence on LLMs for critical financial decisions without adequate human oversight can result in significant operational risks.
Adversarial Attacks
Adversarial Inputs
- Generative AI applications in Business can be subjected to adversarial inputs. Malicious actors can craft inputs designed to confuse or mislead LLMs, potentially leading to incorrect outputs or actions that can be exploited.
Model Poisoning
- Attackers can manipulate the training data or the model itself to introduce vulnerabilities or backdoors.
Attack cases
- Exfiltration via Inference API
- Exfiltration Cyber means
- LLM Meta Prompt extraction
- LLM Data leakage
- Craft Adversarial Data
- Denial of ML service
- Spamming with Chaff Data
- Erode ML Model integrity
- Prompt injection
- Plugin Compromise
- Jailbreak
- Backdoor ML Model
- Poision training data
- Inference API Access
- ML supply chain compromise
- Sensitive Information Disclosure
- Supply Chain Vulnerabilities
- Denial of Service
- Insecured Output Handling
- Insecure API/plugin/Agent
- Excessive API/plugin/Agent Permissions
Regulatory Compliance
Non-Compliance with Regulations
- Financial institutions using Generative AI applications in Business must comply with various regulations related to data privacy, fairness, and transparency.
- Generative AI applications in Business must be designed and implemented in ways that meet these regulatory requirements.
Audit and Explainability
- Ensuring that Generative AI applications in Business using LLMs’ decisions can be audited and explained is crucial for regulatory compliance. Lack of transparency can pose significant challenges.
Why Alert AI?
Alert AI provides end-to-end, interoperable, easy to deploy and manage security integration to address security and risks in Generative AI & AI applications.
Alert AI help Organizations to Enhance, Optimize, Manage security of Generative AI applications in Business workflows.
About Alert AI
- Easy to deploy and manage Generative AI application security integration
- Protection for Generative AI attack vector and vulnerabilities
- Intelligence loss prevention
- Domain-specific security guardrails
- Eliminates Security blind spots of Gen AI Application for InfoSec team
- Seamless integration with Gen AI service platforms AWS Bedrock, Azure OpenAI, NVidia DGX, Google Vertex AI. Industry leading Foundation models
- AWS Bedrock, Azure Gen AI, Nvidia DGX, Google
- and Industry leading Foundation Models AWS Amazon Titan, Anthropic Claude, Nvidia Nemotron, Cohere Command, Google Gemini, IBM Granite,Microsoft Phi, Mistral AI, OpenAI GPT-4
Coverage and Features
- Alerts and Threat detection in AI footprint
- LLM & Model Vulnerabilities Alerts
- Adversarial ML Alerts
- Prompt, response security and Usage Alerts
- Sensitive content detection Alerts
- Privacy, Copyright and Legal Alerts
- AI application Integrity Threats Detection
- Training, Evaluation, Inference Alerts
- AI visibility, Tracking & Lineage Analysis Alerts
- Pipeline analytics Alerts
- Feedback loop
- AI Forensics
- Compliance Reports
- Domain specific LLM security guardrails
Generative AI security guardrails
Danger, warning, caution, notices, recommendations
Enhance, Optimize, Manage security of generative AI applications using Alert AI services.
At ALERT AI, We are developing integrations and models to secure Generative AI & AI workflows in Business applications, and domain specific security guardrails. With over 100+ integrations and thousands of detections, the easy to deploy and manage security platform seamlessly integrates AI workflows across Business applications and environments.
The New Smoke Screen in the Organization
AI Security Posture
Generative AI introduce a host of new Attack vectors and threats escape current firewalls.
Security solutions like Alert AI can help with current pain point of Breaking the glass ceiling, bridging link between
MLops and Information Security operations teams. Having right tools in hands …
Information security engineers and teams can enforce right Security Posture for AI development across the Organizations and see through that smoke screen early-on, spot issues, before production.
Enhance, Optimize, Manage
Enhance, Optimize, Manage security of Generative AI applications using Alert AI security integration.
Alert AI seamlessly integrates with Generative AI platform of your choice.
Alert AI enables end-to-end security and privacy, intelligence security, detects vulnerabilities, application integrity risks with domain-specific security guardrails for Generative AI applications in Business workflows.
Use case Description
Develop Automated report generation of Operation insights using
- Generative AI managed services like Amazon Bedrock, Azure OpenAI, Nvidia DGX, Vertex AI to experiment and evaluate industry leading FMs.
- Customization with data, fine-tuning and Retrieval Augmented Generation (RAG) and agents that execute tasks using organizations data sources.
Security Optimization using
- Alert AI integration.
- Enhance, Optimize, Manage Generative AI application security using Alert AI
