In today’s AI activated world, there are ample number of AI related tools that organizations can use to tackle diverse business challenges. In line with this, Amazon has it’s set of Amazon Web Services for AI and ML, to address the real-world needs.
This blog provides details on AWS services, but by understanding this writeup you can also get to know how AI and ML capabilities can be used to address various business challenges. To illustrate how these services can be leveraged, I have used a few simple and straightforward use cases and mapped the AWS solutions to them.
AI Use Cases : Using AWS Services
1. Employee On boarding process
Any employee onboarding process has its own challenges which can be improved by better information discovery, shortening the onboarding timelines, providing more flexibility to the new hire, option for learning and re-visiting the learning multiple times and enhancing both the security and personalization of the induction experience.
Using natural language queries, the AWS AI service – Amazon Kendra, enables new hires to easily find HR manuals, IT instructions, leave policies, and company guidelines, without needing to know exact file names or bookmark multiple URLs.
Amazon Kendra uses Semantic Search which understands the user’s intent and contextual meaning. Semantic search relies on Vector embeddings, Vector search, Pattern matching and Natural Language Processing.
Real-time data retrieval through Retrieval-augmented Generation (RAG) in Amazon Kendra empowers employees to access up-to-date content securely and efficiently.
Following are examples of few prompts a new hire can use to retrieve information:
- How can I access my email on my laptop and on my phone.
- How do I contact the IT support.
- How can I apply for a leave and who do I reach out to for approvals.
- How do I submit my timesheet.
- Where can I find the company training portal.
- ….etcetera.
Data Security
To protect organizational data and ensure compliance with enterprise security standards, Amazon Kendra supports robust data security measures, including encryption in transit and at rest, and seamless integration with AWS Identity and Access Management (IAM).
Role-based access ensures that sensitive information is only visible to authorized personnel.
Thus, in the Onboarding process, the HR team can provide the personalized touch, and the AI agent ensures the employees have easy, anytime access to the right information throughout their on-boarding journey.
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2. Healthcare: Unlocking Insights from Unstructured Clinical Data
Healthcare providers always need to extract critical patient information and support timely decision-making. They face the challenge of rapidly analyzing vast amounts of unstructured medical records, such as physician notes, discharge summaries, and clinical reports.
From a data perspective two key features are required, namely, Entity Recognition and Attribute detection. Medical entities include symptoms, medications, diagnoses, and treatment plans. Similarly Attribute detection includes identifying the dosage, frequency and severity associated with these entities.
Amazon provides the service, Amazon Comprehend Medical which uses NLP and ML models for extracting such information from unstructured data available with healthcare organizations.
One of the crucial aspects in healthcare is to handle Security and compliance related to patient’s health data. AWS has Amazon Macie as a security related service which employs machine learning & pattern matching to discover, classify, and protect Protected Health Information (PHI) within Amazon S3 bucket. Such a service helps organizations maintain HIPAA compliance through automated data governance.
3. Enterprise data insights
Any large enterprise has data spread across various tools like SharePoint, Salesforce, Leave management portals or some accounting applications.
From these data sets, executives can extract great insights, evaluate what-if scenarios, check on some key performance indicators, and utilize all this for decision making.
We can use AWS AI service, Amazon Q business for this very purpose using various plugins, connectors to DBs, and Retrieval Augmented Generation for up-to-date information.
The user can use natural language to query the system and Amazon Q performs Semantic search to return back contextually appropriate information. It also uses Knowledge Grounding which eventually helps in providing accurate answers not relying solely on training data sets.
To ensure that AI-generated responses adhere strictly to approved enterprise protocols, provide accurate and relevant information, we can define built-in guardrails within Amazon Q, such as Global Controls and Topic blocking.
4. Retail company use cases
a) Reading receipts and invoices
The company wants to automate the financial auditing process. In order to achieve this we can use Amazon Textract to read receipts and invoices as it uses machine learning algorithms to accurately identify and extract key information like product names, prices, and reviews.
b) Analyse customer purchasing patterns
The company intends to analyse customer purchasing patterns to predict future sales trends from their large datasets of historical sales data. For these analyses the company wants to build, train, and deploy machine learning models quickly and efficiently.
Amazon SageMaker is the ideal service for such a development.
c) Customer support Bot
The firm receives thousands of customer calls daily. In order to smoothen the process, the firm is looking to create a conversational AI bot which can take text inputs and voice commands.
We can use Amazon Bedrock to create a custom AI application from a dataset of ready to use Foundation models. These models can process large volumes of customer data, generate personalized responses and integrate with other AWS services like Amazon SageMaker for additional processing and analytics.
We can use Amazon Lex to create the bot, and Amazon Polly for text to speech purposes.
d) Image analyses
The company might want to identify and categorize their products based on the images uploaded. To implement this, we can use Amazon S3 and Amazon Rekognition to analyze images as soon as the new product image is uploaded into the storage service.
AWS Services for Compliance & Regulations
AWS Services for Compliance & Regulations
In order to manage complex customer requirements and handling large volumes of sensitive data it becomes essential for us to adhere to various regulations.
Key AWS services supporting these compliance and governance needs include:
- AWS Config
Continuously monitors and records resource configurations to help assess compliance. - AWS Artifact
Centralized repository for on-demand access to AWS compliance reports and agreements. - AWS CloudTrail
Logs and tracks all user activity and API calls within your AWS environment for audit purposes. - AWS Inspector
Automated security assessment service that identifies vulnerabilities and deviations from best practices. - AWS Audit Manager
Simplifies audit preparation by automating evidence collection and compliance reporting. - AWS Trusted Advisor
Provides real-time recommendations to optimize security, performance, and cost efficiency.
Security and Privacy risks: Vulnerabilities in LLMs
Vulnerabilities in LLMs
While dealing with LLMs there are ways available to attack the prompts, however there are various safeguards also against them. Keeping in view the attacks I am noting down some vulnerabilities which are useful to understand the risks around your LLMs.
| S.No | Vulnerability | Description |
| 1 | Prompt Injection | User input intended to manipulate the LLM |
| 2 | Insecure o/p handling | Un-validated model’s output. |
| 3 | Training data poisoning | Malicious data introduced in training set. |
| 4 | Model Denial Of Service | Disrupting availability by identifying architecture weaknesses. |
| 5 | Supply chain vulnerabilities | Weakness in s/w, h/w, services used to build or deploy the model. |
| 6 | Leakage | Leakage of sensitive data. |
| 7 | Insecure plugins | Flaws in model components. |
| 8 | Excessive autonomy | Autonomy to the model in decision making. |
| 9 | Over – reliance | Relying heavily on model’s capabilities. |
| 10 | Model theft. | Leading to unauthorized re-use of the copies of the model |
Can you co-relate the above use cases with any of your challenges at hand? Have you been able to use any of the AWS services or other AI platforms for dealing with such challenges?
References:
https://aws.amazon.com/ai/services/
https://www.udemy.com/share/10bvuD/
Great read! The blog does a solid job of breaking down how AWS’s AI services map to real-world business challenges.
The use-case examples are clear and actionable—you make me rethink how we could apply AI for onboarding and enterprise insights in our setup.
Thanks for sharing this—it’s going on my must-refer list for cloud + AI strategy.