Generative AI Courses Online with Real-Time Case Studies
                     HOW TO SECURE ENTERPRISE 
GENERATIVE AI APPLICATIONS
Enterprise Generative AI Security
Security controls, governance, monitoring, and best practices
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INTRODUCTION TO ENTERPRISE 
GENERATIVE AI
Generative AI applications use large language models 
(LLMs) to generate text, code, summaries, 
recommendations, and insights. Enterprise 
deployments connect models with business data, APIs, 
databases, and users.
These connections increase the potential attack 
surface.Security teams should protect models, 
applications, identities, data, and infrastructure 
together.
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IDENTIFY AI SECURITY RISKS
 Identify risks across models, applications, users, data, APIs, 
and infrastructure.
 Common threats include prompt injection, data leakage, model 
abuse, and unauthorized access.
 Attackers may manipulate prompts to bypass application rules.
 Sensitive information can appear in prompts, responses, logs, 
or datasets.
 Regular risk assessments help discover weaknesses before 
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PROTECT ENTERPRISE DATA
 Apply strict data access controls to protect confidential 
enterprise information.
 Use encryption for data stored in databases, storage systems, 
and AI platforms.
 Encrypt sensitive information while it moves between 
applications and services.
 Use data classification to identify confidential and regulated 
information.
 Apply masking or redaction before sending sensitive 
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SECURE PROMPTS AND AI INPUTS
 Validate and sanitize user inputs before sending them to 
generative AI models.
 Use input filters to detect malicious instructions and 
prompt injection attempts.
 Limit the information users can provide to AI applications.
 Separate system instructions from user-controlled content 
whenever possible.
 Add guardrails to prevent users from bypassing 
application policies.
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CONTROL MODEL ACCESS
 Restrict AI model access based on user roles and business 
requirements.
 Apply role-based access control to models, APIs, datasets, 
and AI resources.
 Use strong authentication and authorization for 
enterprise AI applications.
 Monitor API keys, tokens, and service credentials 
carefully.
 Follow least-privilege principles to reduce unnecessary 
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SECURE AI OUTPUTS
 Treat AI-generated responses as untrusted application 
output.
 Scan outputs for sensitive information before displaying 
or storing them.
 Apply content filters to identify harmful, inappropriate, or 
restricted responses.
 Validate generated code before allowing it into production 
systems.
 Use human review for high-impact decisions and sensitive 
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MONITOR AND DETECT THREATS
 Continuous monitoring helps detect unusual AI activity.
 Log authentication, prompts, responses, API calls, and 
access attempts.
 Monitor abnormal usage patterns, excessive requests, 
and unusual data access.
 Use security alerts to identify potential attacks quickly.
 Regular audits help improve controls as threats 
continue to evolve.
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BUILD RESPONSIBLE AI GOVERNANCE
 Enterprise AI security requires clear governance policies and 
responsibilities.
 Define rules for acceptable AI usage, data handling, model 
selection, and human oversight.
 Document AI models, datasets, applications, and security controls.
 Test AI applications regularly using security assessments and 
adversarial testing.
 Keep policies aligned with organizational requirements and 
applicable regulations.
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ENTERPRISE AI SECURITY BEST 
PRACTICES
 Use defense-in-depth security across applications, models, data, 
APIs, and infrastructure.
 Combine identity management, encryption, access controls, 
monitoring, and AI guardrails.
 Train employees to recognize AI security risks and use enterprise 
AI responsibly.
 Continuously test and update AI applications as new threats 
emerge.
 Visualpath can support structured learning and practical enterprise 
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For More Information About 
Generative AI
Address:- Flat no: 205, 2nd Floor,
   Nilagiri Block, Aditya Enclave,  Ameerpet, 
Hyderabad-16
Ph. No: +91-7032290546
www.visualpath.in
 [email protected]
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Thank You
For More 
Information
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