This section here onwards explains security considerations, potential threats and mitigations and tools required in enabling SPML phases –

Security Requirements & Secure Design:

The security team or owner to ensure that business requirements for prompt design are not shared with unauthorized individuals, and sensitive or proprietary prompt logic remains protected. And, verify that initial prompt designs are not tampered, version control and audit trails are maintained. The access to design documentation and tools during this phase should be made available and delays to be avoided to the team participating here.

Example:

• Use secure collaboration tools (e.g., encrypted file sharing platforms) for sharing initial designs.
• Use Git repositories with access control to ensure all changes to prompts are logged and monitored.
• Store documents in a high-availability storage solution with proper backup mechanisms (e.g., Cloud Storage).

Potential Threats

• Prompt Injection Attacks during brainstorming.
• Inherent bias in generated prompt templates.
• Lack of secure design for sensitive tasks.

Mitigations

• Threat modelling and secure design principles (STRIDE, DREAD, OWASP).
• Bias testing with adversarial datasets.
• Secure prompt pattern libraries (to prevent common issues like biased outputs, data exposure, unauthorized access, or misuse of prompts by providing standardized patterns)

Example Tools

• MITRE ATLAS (for adversarial threat modelling)
• Bias Mitigation Toolkit (e.g., Aequitas).

Secure Development

In this phase, prompts and test outputs from unauthorized access, especially when dealing with sensitive data must be protected. And, validation should be done for test data and outputs to prevent corrupted prompts or faulty input data from generating biased or incorrect results. For iterative development, the prompt testing environments need to be security hardened, and should be reliable and accessible to the intended users.

Example:

• Mask or encrypt sensitive test data (e.g., using scripts, Data Loss Prevention API from GCP or similar) during prototyping.
• Implement data validation pipelines to ensure input quality for prompt testing.
• Use high-reliability compute instances (e.g., Compute Engine from GCP or similar) to avoid disruptions during testing.

Potential Threats

• Prompt Leakage exposing sensitive information.
• LLM hallucinations impacting prototype outputs.
• Insecure API communications.
• Model bias inherited from external datasets.

Mitigations

• Use secure storage for prompt versions.
• Use test cases for adversarial prompt attacks.
• Secure API with TLS and OAuth.
• Retrain on debiased datasets.

Example Tools

• Github, Langfuse (for prompt versioning and monitoring).
• OWASP ZAP (to test API communication vulnerabilities).

Secure Deployment

In this phase, preventing unauthorized access to prompts embedded in APIs or systems to be enforced, and API keys and credentials must be protected. The deployment owner to ensure that prompts are not manipulated during deployment or integration else could lead to undesired or harmful outputs. And, ensure that prompts are deployed in redundant systems so they are always available in production environments.

Example:

• Use secrets management tools (e.g., Secret Manager from GCP or similar) to store and access sensitive configurations.
• Use secure deployment pipelines with signed artifacts to prevent unauthorized changes.
• Use load balancing and failover mechanisms in GCP or similar for prompt-serving APIs.

Potential Threats

• Exposure of sensitive environment credentials.
• Prompt injection during CI/CD.
• Insufficient compliance checks for deployed prompts.
• Unsecure logging pipelines.

Mitigations

• Secure secrets management (e.g., HashiCorp Vault, GCP or similar).
• Embed prompt analysers in pipelines.
• Secure CI/CD pipelines with MFA (Multi factor authentication and RBAC (Role based access control)
• Encrypt logs with TLS 1.3.

Example Tools

• HashiCorp Vault (secrets management).
• Jenkins (CICD pipeline with plugin support for security scanning).

Security Monitoring

In this phase, the logging and monitoring systems need to ensure they don’t expose sensitive inputs, outputs, or user interactions with the AI, and ensure that the logs are immutable and cannot be tampered with to falsify evaluations.

Monitoring systems should be operational and accessible to enable continuous evaluation of prompt performance.

Example:

• Encrypt monitoring data stored in tools like syslog server, SIEM (Security information and event management), Cloud Logging in GCP or similar.
• Use write-once storage solutions (e.g., WORM (write once read many) whether hardware or software based as appropriate) for sensitive audit logs.
• Implement monitoring dashboards with failover and alerting systems (e.g., GCP Operations Suite).

Potential Threats

• Prompt drift leading to unexpected outputs.
• Insufficient visibility into prompt behaviours.
• Over-retention of sensitive telemetry data.
• No tracking for changes to prompt behaviour.

Mitigations

• Use telemetry monitoring for prompt outputs.
• Implement data retention policies.
• Enable monitoring for prompt drift and correctness (e.g., Langfuse dashboards).

Example Tools

• Grafana or similar (real-time telemetry monitoring).
• Prometheus or similar (telemetry collection).
• Langfuse or similar (monitoring).

Threat Mitigations and Risk Assessment Tuning

Generative AI systems are dynamic by nature and constantly evolving through refinements and optimizations. During this process new vulnerabilities may emerge due to changes in functionality, and security flaws could be inadvertently introduced, and compliance requirements might be overlooked as performance tuning becomes the focus.

In this phase, security professionals need to focus on analysing, optimizing, and fine-tuning the security controls those are associated with prompts, and its performance and operational environment.

Ensure sensitive data used for optimization (e.g., user queries) is anonymized or pseudonymized to protect user privacy and optimization processes do not introduce errors, bias, or harmful behaviour into prompts.

Example:

• Apply techniques like differential privacy when using customer data for fine-tuning prompts.
• Perform regular audits of optimized prompts using explainability tools to detect anomalies or bias.
• Use resilient cloud-based ML training systems to ensure availability during optimization efforts.

Potential Threats

• Overfitting of prompts to specific tasks.
• Data drift reducing prompt accuracy.
• Unintended prompt behaviour impacting compliance or ethical requirements.
• Introduction of new biases.

Mitigations

• Re-evaluate prompt performance on diverse datasets.
• Periodically retrain on unbiased datasets.
• Incorporate user feedback loops.
• Audit optimized prompts for compliance.

Example Tools

• Hugging Face Evaluate (for retraining and performance metrics).
• Langfuse (evaluating prompt refinements).

Governance & Ethical Review

The sensitive data and discussions during ethical reviews of prompts need to be protected to prevent unauthorized disclosures. Consider necessary safeguards to ensure that all audit and ethical review findings are accurately recorded and not tampered with. Maintain access to ethical review frameworks, guidelines, and tools during governance processes.

Example:

• Use role-based access control (RBAC) for accessing ethical review documentation.
• Use blockchain-based logging mechanisms for tamper-proof record-keeping.
• Host ethical review tools on high-availability platforms.

Potential Threats

• Lack of accountability for prompt-generated decisions.
• No ethical review of prompt use cases.
• Privacy concerns in sensitive tasks.
• Absence of audit logs for prompt changes.

Mitigations

• Enforce explainability in prompt logic.
• Integrate privacy-preserving methods (e.g., anonymization).
• Use audit logs and ethical evaluation pipelines.

Example Tools

• Langfuse Audit Logs (compliance tracking).
• AI Fairness 360 Toolkit (bias and explainability testing).

Secure Archival Storage and Sharing

In this phase, archived prompts and associated metadata from unauthorized access or leaks need to be protected, and one need to ensure that archived prompts and metadata are preserved in their original form and not corrupted over time. Additionally, archived prompts and metadata should be accessible for future retrieval without delays or data loss.

Example:

• Encrypt archived prompts and store them in secure systems like GCP or similar Archive Storage.
• Use checksum-based integrity verification for archived files.
• Use redundant storage systems with geographical replication for prompt archives.

Potential Threats

• Exposure of archived prompts with sensitive data.
• Inadequate controls for shared prompts.
• No encryption for archived knowledge.
• Lack of tracking for shared prompt access.

Mitigations

• Encrypt archived prompts with AES-256 encryption.
• Use granular access control (RBAC).
• Secure shared data with secure sharing mechanisms (e.g., signed URLs).

Example Tools

• Langfuse RBAC (access control for prompts).
• Sealed Secrets (Kubernetes or cloud or similar secret management, Vault).

Secure Decommissioning

Prevent unauthorized access to retired prompts and metadata through encryption, access control, and deletion.

Ensure retired components or prompts are securely removed without affecting active systems or data.

Retire prompts only after ensuring replacements are fully deployed and operational, minimizing downtime.

Example:

Encrypt and securely delete sensitive data, revoke access.

Ensure no unintended impact on active systems during decommissioning.

Avoid downtime by deploying replacement systems before retiring old ones.

Potential Threats

• Unauthorized access to decommissioned prompts
• Residual sensitive data leakage
• Lack of audit trails for retired prompts

Mitigations

• Use encryption and access control during the decommissioning process.
• Revoke API keys, credentials, and user access tied to retired prompts.
• Perform secure deletion of retired prompts and metadata using shredding or data-wiping tools.
• Use a Data Loss Prevention (DLP) tool to monitor and prevent unauthorized sharing.
• Maintain audit logs of prompt deprecation and removal actions.

Example Tools

• Langfuse, Vault/KMS from HashiCorp, GCP or similar for cryptographic operations.
• IAM from Okta, GCP or similar for access control.
• Langfuse, Splunk, ELK or similar for logs and audit.

Safe Harbor
The content shared on this blog is for educational and informational purposes only and reflects my personal views and experiences. It does not represent the opinions, strategies, or endorsements of my current or previous employers. While I strive to provide accurate and up-to-date information, this blog should not be considered professional advice. Readers are encouraged to consult appropriate professionals for specific guidance.