Generative AI Security

Introduction

In Healthcare, Generative AI applications and workflows using LLMs have the potential to transform patient care by analyzing medical records, research papers, and clinical data to provide more accurate diagnoses.

These models can identify patterns and correlations in large datasets that might be overlooked by human practitioners, leading to earlier detection of diseases and more effective treatment plans.

Generative AI applications and workflows using LLMs can also assist Healthcare providers in making data-driven clinical decisions by offering personalized treatment recommendations based on the latest research and patient history.

Additionally, these models can streamline administrative tasks, such as managing patient records, scheduling appointments, and handling billing inquiries, freeing up Healthcare professionals to focus on patient care.

Generative AI applications and workflows using LLMs can enhance telemedicine services by powering AI-driven virtual assistants that provide patients with instant access to medical information and support.

Moreover, use of Generative AI applications can support medical research by analyzing vast amounts of scientific literature and identifying new areas for investigation.

Overall, Generative AI applications using LLMs offer Healthcare organizations the ability to improve patient outcomes, reduce costs, and increase operational efficiency.

Key Generative AI Use cases in Healthcare

Earlier detection of diseases

Clinical Research data analytics

Accurate diagnoses

Virtual patient collaborator

Patient record Manager

Personalized patient care (PPC)

Use case summary

Clinical Decision-Making Workflow

Personalized patient care (PPC)

Analytics report generation for Personalized patient care (PPC)

from diverse data sets—making it a suitable option for identifying a patient’s

potential health risks with broader spectrum of variables comprehensive and personalized patient care to assist with diagnosis tailored treatment options

evidence-based therapies and individualized care.

Generative AI application

Main Considerations

•        accuracy
•       security
•       privacy
•       fairness

Retrieval-Augmented Generation

• Used for improving accuracy
• Data outside the Foundation Model
• Combining the LLM with search
• Enrich prompts
• Data in context

Domain-specific GenAI Security

ALERT AI – security integration for Domain-specific LLM guardrails

• Application integrity
• Data privacy
• bias mitigation
• HIPAA-eligibility
• PHI detection
• Redaction, Obfuscation

AlertAI Domain-specific GenAI LLM Application Security Integration for Healthcare 

• PHI sensitive information detection pipeline
• Redaction and Obfuscation pipeline
• HIPAA regulations violation module
• Healthcare domain-specific LLM Alert and vulnerability  detections

Infrastructure

To build, scale Generative AI , Set up your infrastructure in

• AWS Bedrock
• Azure OpenAI
• NVidia DGX Cloud
• GCP Vertex AI
• Kubernetes.

Model Selection, Alignment, Customization

• Pre-process
• Train
• Validate
• Test
• Fine-tuning

Foundation models

•      Llama-2/3
•      Mixtral 8x7B, and Mistral 7B LLMs
•      Claude 3
•      Nvidia NeMo
•      AWS Bedrock

Pre-Training

• Supervised Fine-Tuning (SFT)
• Parameter-Efficient Fine-Tuning (PEFT) techniques to datasets.
• Setup launch foundation model pre-training

Data Curation

• Use classifiers for evaluating data quality and for identifying data domains.
• Annotation and enhancing the combination of diverse datasets essential for the training of foundational models.
• synthetic data generation, and qualitative score assignment to prepare a dataset for PEFT of LLMs.

Deployment

• Deploy LLMs trained with NeMo Framework using NVIDIA NIM,
• Deployment with TensorRT-LLM.
• AWS Bedrock, Azure Open AI , GCP Vertex AI , Nvidia DGX

RAG Pipeline Overview

• Retrieval-augmented generation (RAG)
• combines information retrieval
• system prompts
• accurate, up-to-date, and precise, contextual responses LLMs
• data from various sources, dbs, internet, feeds
• pipeline basic stages:
• Indexing from text, embedder
• Generating- Embed the query with contexts embeddings
• Query to feed into the LLM to generate answer

Intermediate stages

• Reranker, Adaptive RAG, Self-RAG, etc
• BERT embedder
• Models like GPT, LLama
• Text-based RAG: Gemma, Mistral, Mamba
• Multimodal-based RAG: NeVa (Visual Language Model)
• To orchestrate the RAG steps, RAG pipeline can use  LlamaIndex

Alert AI Domain-specific Security guardrails

PHI detection pipeline

PII detection pipeline

HIPPA regulation

regulation violoation detection pipeline

Patient record advisor chat agent automation

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…”

Here some potential security risks using Generative AI in Business.

Data Privacy and Security

Sensitive Data Exposure

Generative AI applications in Business using LLMs can inadvertently reveal sensitive information if not properly managed.

For example, if an LLM is trained on proprietary or customer data, there’s a risk of that information being exposed during interactions.

Data Breaches

Generative AI applications in Business must have protection,  if  an LLM’s underlying data infrastructure is compromised, attackers could 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 using LLMs 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.

Fraud and Manipulation

Phishing and Social Engineering

Generative AI applications in Business using LLMs 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.

Dependencies-on Automation

Over 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 using LLMs 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.

Generative AI is new attack vector can endanger business applications and enterprises..

A variety of concerns around Gen AI, include  Copyright Legal exposures,

Sensitive information disclosure, Data privacy violations, Domain specific exposures.

Generative AI opens up all kinds of opportunities to obtain sensitive data without even building malware. Anyone to get a hold of the prompt of an LLM and find out sensitive data that has been absorbed with the model’s training process.

“What makes AI security complex?

The Answer is its moving parts.

“Best way to secure AI is to start right now…”

Choosing right security solution

• Right solution is actually what it means to Your organization. Your system, environment, use case.
• Generic service generic product solution may not be right for you. May not cater your Organizations needs.

Vulnerabilities Management in Models, LLMs

• Detect Prompt Injection
• Information leak
• Misinformation
• Perturbations

Mitigation

• Scan Vulnerabilities in Generative AI applications and ML Models
• Models Detection & Management
• Associated risks
• Compliance Score
• Severity Score
• Domain specific AI security
• Manage access to resources in your AI clusters
• Assign the AI service roles on the AI resource’s to Managed identities
•  Detect Poison, Evasion
•  Exfiltration
•  ML supply chain compromise
•  Training time, Inference time attacks
• Spills, leaks, contamination

Risk Analysis

Modelling Adversarial ML, LLM attacks,

ML Supply chain attacks.

Threat intelligence with Alerts (MITRE ATLAS, OWASP)

AI Threat Detection

Threat hunting Alerts in Models and Pipelines,

Model Behaviour Alerts

Anomaly Detection

Pipeline, Data Lineage and Prompt Interaction Alerts

Know your Generative AI and AI attack surface

AI Discovery

Discovery of AI assets, AI Inventory, Catalog

Models, Pipelines, Prompts

Cluster Resources, Compute, Networks

Tracking Analysis

Experiments, Jobs, Runs, Datasets

Tracking Models, Versions, Artifacts

Tracking Parameters, Metrics, Predictions, Artifacts

LLM Tracking, Interactions

Lineage & Pipeline Analysis

Data sources, Data sinks

Map, Topology of Streams

ALERT  AI 

Interoperable end-to-end security solution to help enhance, optimize, manage security of “Generative AI and AI  in Business applications and workflows” against potential adversaries, model vulnerabilities, privacy, copyright and legal exposures, sensitive information leaks, Intelligence and data exfiltration, infiltration at training and inference, integrity attacks in AI applications, anomalies detection and enhanced visibility in AI pipelines. forensics, audit,AI  governance in AI footprint.

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.

Eliminate security Blind-spots

The New Smoke Screen and 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 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.

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