Generative AI Security

Introduction

The pharmaceutical industry business model typically revolves around the retail sale of prescription medications, over-the-counter (OTC) drugs, health-related products, and various services.

They also help with clinical trials and research of new medicines.

Core Components

Prescription Medications

• Over-the-Counter (OTC) Drugs
• Health and Wellness Products
• Pharmacy Services

Customer Segments

Patients with Prescriptions

• Health-Conscious Consumers
• Healthcare Providers:
• Insurance Companies

Key Activities

• Inventory Management: Ensuring an adequate supply of medications while avoiding overstocking, which can lead to expiration and loss.
• Customer Service: Providing excellent service, which includes filling prescriptions accurately and offering health advice.
• Regulatory Compliance: Adhering to stringent regulations regarding the storage, dispensing, and documentation of medications.
• Marketing and Customer Engagement: Attracting and retaining customers through loyalty programs, promotions, and personalized services.

Technological Integration

• Electronic Health Records (EHRs): Integration with EHR systems allows for streamlined prescription processing and better communication with healthcare providers.
• Online Services: Offering online prescription refills, telemedicine consultations, and home delivery services to meet the growing demand for convenience.
• Data Analytics: Leveraging data to optimize inventory, personalize customer experiences, and improve operational efficiency.

Future Trends

• Telepharmacy: Providing pharmacy services remotely via digital platforms.
• Personalized Medicine: Customizing treatments based on individual genetic profiles, which may require pharmacies to offer specialized compounding services.
• Sustainability: Incorporating eco-friendly practices in operations, such as reducing plastic waste from prescription bottles.

Impact of Generative AI applications in Pharmaceutical industry

Generative AI applications can significantly enhance the pharmacy business model, particularly when integrating clinical trials and research.

Here’s how Generative AI applications and LLMs can contribute:

Streamlining Operations

• Automated Prescription Processing: Generative AI applications and LLMs can be used to automate the prescription processing workflow, reducing errors, speeding up the process, and improving accuracy. They can help verify prescriptions against patient records and provide alerts for potential drug interactions.
• Inventory Management: By analyzing sales data and predicting demand, Generative AI applications can optimize inventory levels, reducing waste and ensuring that essential medications are always in stock.
• Customer Support: Generative AI applications and LLMs can power chatbots and virtual assistants that handle customer inquiries, provide information on medications, and assist with refills, freeing up staff for more complex tasks.

Enhancing Clinical Trials

• Patient Recruitment: Generative AI applications and LLMs can analyze patient data to identify suitable candidates for clinical trials, improving recruitment efficiency. They can also generate personalized communication to engage potential participants.
• Data Analysis: Generative AI applications can analyze large volumes of clinical trial data, identifying patterns, trends, and insights that might be missed by traditional methods. This can accelerate the research process and improve the accuracy of findings.
• Regulatory Compliance: Generative AI applications can assist in ensuring compliance with regulatory requirements by automatically generating reports, monitoring adherence to protocols, and flagging potential issues for review.

Supporting Research and Development

• Literature Review and Knowledge Management: Generative AI applications can quickly process and summarize vast amounts of scientific literature, helping researchers stay up-to-date with the latest developments and identify gaps in the existing knowledge base.
• Hypothesis Generation: Generative AI applications can assist researchers in generating new hypotheses by analyzing existing data and literature, suggesting novel approaches to drug development or treatment strategies.
• Collaboration and Communication: Generative AI applications and LLMs can facilitate communication between researchers, pharmacists, and other stakeholders by summarizing complex research findings into easily understandable language, ensuring that all parties are aligned.

Personalizing Patient Care

• Medication Adherence: Generative AI applications and LLMs can analyze patient data to identify those at risk of non-adherence to their medication regimen and provide personalized reminders and support to improve compliance.
• Drug Interaction Warnings: By continuously learning from a vast database of drug interactions, Generative AI applications and LLMs can provide real-time alerts to pharmacists and patients about potential risks, ensuring safer medication use.
• Health Recommendations: Generative AI applications and LLMs can offer personalized health and wellness recommendations based on patient data, including suggestions for OTC products or lifestyle changes that complement their prescribed treatments.

Improving Customer Experience

• Personalized Marketing: Generative AI applications and LLMs can analyze customer behavior and preferences to create targeted marketing campaigns, promoting relevant products and services to individual customers.
• 24/7 Support: Generative AI applications and LLMs can power virtual assistants that provide round-the-clock support, answering questions, managing appointments, and helping with medication management, thereby enhancing customer satisfaction.
• Educational Content: Generative AI applications and LLMs can generate educational materials, such as blog posts, newsletters, or informational videos, that help customers understand their health conditions, treatment options, and the importance of adherence to prescribed therapies.

Optimizing Decision-Making

• Business Analytics: Generative AI applications and LLMs can process and analyze business data, providing actionable insights for decision-makers. This includes optimizing pricing strategies, identifying new business opportunities, and improving operational efficiency.
• Predictive Modeling: By analyzing historical data, Generative AI applications and LLMs can predict future trends in medication demand, customer behavior, and even potential regulatory changes, allowing pharmacies to plan proactively.
• Risk Management: Generative AI applications and LLMs can assess risks associated with clinical trials, drug interactions, or regulatory compliance, offering strategies to mitigate these risks effectively.

Fostering Innovation

• New Drug Development: Generative AI applications and LLMs can assist in the early stages of drug discovery by identifying potential drug candidates from vast chemical databases, predicting their efficacy, and suggesting modifications to improve their performance.
• Patient Feedback Analysis: Generative AI applications and LLMs can analyze patient feedback and social media data to identify unmet needs or emerging trends, guiding the development of new products or services.
• Collaborative Research Platforms: Generative AI applications and LLMs can power platforms that enable researchers, pharmacists, and other stakeholders to collaborate more effectively on clinical trials and research projects, sharing insights and data in real-time.

Regulatory and Ethical Compliance

• Document Generation: Generative AI applications and LLMs can automate the generation of regulatory documents, ensuring that all required information is included and formatted correctly, thus reducing the time and cost of compliance.
• Ethical Monitoring: Generative AI applications and LLMs can monitor ongoing research and clinical trials to ensure that they adhere to ethical standards, flagging any potential issues for review by human experts.

By integrating Generative AI applications and LLMs into their operations, pharmaceutical companies can enhance efficiency, improve patient care, and accelerate research and development efforts.

This not only strengthens the overall business model but also positions the pharmacy as a leader in innovation within the healthcare industry.

Automated  Prescription Processing  and Reports generation for Decision-making data analytics

Business Benefits

• Enables 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 Prescription Processing

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

• Multiple questions and answers in a Session
• Session
• Dialogs
• Session Context
• Active Dialog
• Dialog Context
• Form
• Prompt
• Filled
• Match | NoMatch | Timeout
• NLP Grammar

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

Intelligent Document Processing

Block diagram

Decision making business analytics Insights use case Generative AI application

Automated Prescription Processing

Automated Prescription Processing  workflow by analyzing  transactions , including online, chat, IVR, web/fax documents and Query inventroy, order, renewals, Generatie AI applications using LLMs can tranfrom Operational excellence and cost efficiency, precice and reduce errors more accurately.

The decision making business analytics lead to better decision-making and reduced default rates.

Automation of prescription  (document) processing identifies and classifies document types such as bank statements, cash flow statements, P&L reports, address proofs, other required documentation.

This can lead to better decision-making and reduced costs and operational efficiency.

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.

Overreliance on Automation

Overdependence 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 Stream

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.

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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

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 Prescription processing and Decision-making business analytics  workflows 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 domain-specific security guardrails
• Enhance, Optimize, Manage Generative AI application security using Alert AI