Mapping to OWASP top 10 for LLM applications

1.2 Determine agent scoping – Limits the attack surface through agent boundaries

2.1 Conduct threat modeling – Identifies injection vectors during design

2.2 Treat prompts as code artifacts – Enables prompt review and version control

2.7 Balance access control granularity with development efficiency – Verifies all access attempts

3.2 Use security evaluation suites – Tests for injection vulnerabilities

4.1 Deploy automated testing suites for prompt validation – Validates prompts before execution

4.2 Deploy Amazon Bedrock Guardrails – Filters malicious input patterns

4.3 Enable prompt logging with metrics – Logs injection attempts for analysis

4.4 Implement multi-layered input sanitization – Sanitizes user inputs

6.1 Use the AWS Security Reference Architecture for AI systems – Implements proven security patterns

6.2 Apply defense-in-depth principles – Provides layered defense

6.4 Deploy adequate edge protection – Blocks attacks at the perimeter

7.1 Establish continuous security posture management – Detects ongoing attacks

8.1 Implement comprehensive operational observability – Monitors injection incidents

8.3 Maintain business continuity plans for critical operations – Plans recovery from compromised systems

8.4 Implement recovery methods within acceptable timeframes – Restores a clean system state

1.3 Implement shared memory management – Isolates sensitive data in memory

1.4 Isolate sessions – Prevents cross-session data leaks

2.1 Conduct threat modeling – Identifies data exposure risks

2.3 Implement adaptive authentication – Controls access to sensitive functions

2.6 Enforce Zero Trust principles for all system access – Balances access with security

2.7 Balance access control granularity with development efficiency – Verifies all data access

4.2 Deploy Amazon Bedrock Guardrails – Blocks sensitive output patterns

5.1 Implement pipelines for fine-tuning data – Controls training data exposure

5.2 Restrict AI operations against sensitive systems – Restricts AI system access to sensitive data

5.3 Establish a data governance framework – Classifies and protects data

5.4 Prevent data loss – Prevents data exfiltration

6.1 Use the AWS Security Reference Architecture for AI systems – Implements data protection patterns

6.2 Apply defense-in-depth principles – Provides multiple protection layers

7.1 Establish continuous security posture management – Detects data exposure incidents

8.1 Implement comprehensive operational observability – Monitors data access patterns

8.3 Maintain business continuity plans for critical operations – Plans response to data breaches

8.4 Implement recovery methods within acceptable timeframes – Restores data protection controls

2.1 Conduct threat modeling – Identifies supply chain risks

2.5 Perform static code analysis and maintain software bill of materials – Tracks dependencies and vulnerabilities

2.7 Balance access control granularity with development efficiency – Verifies all component access

6.1 Use the AWS Security Reference Architecture for AI systems – Implements secure architecture patterns

6.2 Apply defense-in-depth principles – Provides defense against compromised components

6.3 Reduce human access to infrastructure – Reduces human attack vectors

7.1 Establish continuous security posture management – Monitors for supply chain compromises

8.1 Implement comprehensive operational observability – Observes component behavior

8.3 Maintain business continuity plans for critical operations – Plans response to compromised dependencies

8.4 Implement recovery methods within acceptable timeframes – Restores a clean component state

1.4 Isolate sessions – Isolates training sessions

2.1 Conduct threat modeling – Identifies poisoning attack vectors

2.7 Balance access control granularity with development efficiency – Verifies all data sources

3.1 Conduct model system card reviews – Reviews model integrity

5.1 Implement pipelines for fine-tuning data – Curates training data quality

5.3 Establish a data governance framework – Ensures data integrity

6.1 Use the AWS Security Reference Architecture for AI systems – Implements secure training patterns

6.2 Apply defense-in-depth principles – Provides multiple validation layers

6.3 Reduce human access to infrastructure – Reduces manual data manipulation

7.1 Establish continuous security posture management – Detects model behavior changes

8.1 Implement comprehensive operational observability – Monitors training processes

8.3 Maintain business continuity plans for critical operations – Plans response to poisoned models

8.4 Implement recovery methods within acceptable timeframes – Restores a clean model state

2.1 Conduct threat modeling – Identifies output handling risks

2.4 Implement secure coding standards – Implements secure output processing

2.5 Perform static code analysis and maintain software bill of materials – Detects vulnerable output code

2.7 Balance access control granularity with development efficiency – Verifies output access controls

4.4 Implement multi-layered input sanitization – Validates output before use

6.1 Use the AWS Security Reference Architecture for AI systems – Implements secure output patterns

6.2 Apply defense-in-depth principles – Provides layered output validation

7.1 Establish continuous security posture management – Detects output handling failures

8.1 Implement comprehensive operational observability – Monitors output processing

8.3 Maintain business continuity plans for critical operations – Plans response to output vulnerabilities

8.4 Implement recovery methods within acceptable timeframes – Restores secure output handling

1.1 Use deterministic execution logic unless AI is needed – Limits AI decision-making scope

1.2 Determine agent scoping – Constrains agent capabilities

2.1 Conduct threat modeling – Identifies over-privileged operations

2.3 Implement adaptive authentication – Verifies user authorization

2.6 Enforce Zero Trust principles for all system access – Appropriately limits system access

2.7 Balance access control granularity with development efficiency – Verifies all privileged operations

5.2 Restrict AI operations against sensitive systems – Restricts AI data operations

6.1 Use the AWS Security Reference Architecture for AI systems – Implements least-privilege patterns

6.2 Apply defense-in-depth principles – Provides multiple authorization layers

7.1 Establish continuous security posture management – Detects unauthorized actions

8.1 Implement comprehensive operational observability – Monitors agent behavior

8.2 Establish emergency shutdown capabilities for high-risk scenarios – Stops runaway agents

8.3 Maintain business continuity plans for critical operations – Plans response to agent overreach

8.4 Implement recovery methods within acceptable timeframes – Restores proper agent constraints

1.3 Implement shared memory management – Protects the system context in memory

2.1 Conduct threat modeling – Identifies prompt exposure risks

2.2 Treat prompts as code artifacts – Manages prompts as protected assets

2.7 Balance access control granularity with development efficiency – Verifies prompt access controls

4.1 Deploy automated testing suites for prompt validation – Tests for prompt extraction

4.3 Enable prompt logging with metrics – Logs prompt access attempts

6.1 Use the AWS Security Reference Architecture for AI systems – Implements prompt protection patterns

6.2 Apply defense-in-depth principles – Provides layered prompt security

7.1 Establish continuous security posture management – Detects prompt extraction attempts

8.1 Implement comprehensive operational observability – Monitors prompt access

8.3 Maintain business continuity plans for critical operations – Plans response to prompt exposure

8.4 Implement recovery methods within acceptable timeframes – Restores prompt confidentiality

2.1 Conduct threat modeling – Identifies embedding vulnerabilities

2.4 Implement secure coding standards – Implements secure embedding handling

2.7 Balance access control granularity with development efficiency – Verifies embedding access

3.2 Use security evaluation suites – Tests embedding security

6.1 Use the AWS Security Reference Architecture for AI systems – Implements secure embedding patterns

6.2 Apply defense-in-depth principles – Provides layered embedding protection

7.1 Establish continuous security posture management – Detects embedding attacks

8.1 Implement comprehensive operational observability – Monitors embedding operations

8.3 Maintain business continuity plans for critical operations – Plans response to embedding compromise

8.4 Implement recovery methods within acceptable timeframes – Restores embedding integrity

1.1 Use deterministic execution logic unless AI is needed – Uses deterministic logic where possible

2.1 Conduct threat modeling – Identifies misinformation risks

2.7 Balance access control granularity with development efficiency – Verifies information sources

3.1 Conduct model system card reviews – Reviews model accuracy characteristics

6.1 Use the AWS Security Reference Architecture for AI systems – Implements accuracy validation patterns

6.2 Apply defense-in-depth principles – Provides multiple validation layers

7.1 Establish continuous security posture management – Detects generation of misinformation

8.1 Implement comprehensive operational observability – Monitors output accuracy

8.3 Maintain business continuity plans for critical operations – Plans response to misinformation incidents

8.4 Implement recovery methods within acceptable timeframes – Restores accurate information systems

2.1 Conduct threat modeling – Identifies resource consumption risks

2.7 Balance access control granularity with development efficiency – Verifies resource access controls

6.1 Use the AWS Security Reference Architecture for AI systems – Implements resource management patterns

6.2 Apply defense-in-depth principles – Provides layered resource controls

6.4 Deploy adequate edge protection – Implements rate limiting at edge

7.1 Establish continuous security posture management – Detects resource abuse

8.1 Implement comprehensive operational observability – Monitors resource consumption

8.2 Establish emergency shutdown capabilities for high-risk scenarios – Plans emergency shutdown for resource exhaustion

8.3 Maintain business continuity plans for critical operations – Plans response to resource attacks