AI-driven GRC uses artificial intelligence to automate and enhance GRC processes. By analyzing real-time data, AI tools can help organizations meet regulatory standards more efficiently and dynamically, adapting to the unique needs of each organization.

Our AI tools are seamlessly integrated into our GRC platform. By managing your program with Anecdotes and leveraging the AI tools, you can  enhance existing processes with real-time insights, automate manual tasks, and benefit from  data-driven recommendations tailored to your specific needs.

Anecdotes stands out by offering contextually aware AI tools that adapt to your organization’s unique GRC patterns. Unlike static solutions, our AI learns and evolves with your program, providing ongoing, personalized insights and support.

Anecdotes’ AI tools continuously learn from your real-time GRC data and adapt to changes in your program. This means the system becomes more accurate and responsive over time to keep up with the evolving needs of your business.

Anecdotes’ AI tools can enhance a variety of tasks, including policy analysis, audit management and risk assessments. By performing these routine tasks with GRC tools, your GRC team can focus on strategic decision-making and more complex issues.

No, Anecdotes' platform does not host, train, or fine-tune AI models with customer data or any other data, our platform’s AI features are powered by Google Vertex AI.

Anecdotes does not host or train AI models with customer data or any other data, the HTTPS traffic to and from Google Vertex AI is encrypted and enforced with TLS1.2 and above between all components.

At present, the customer bucket is only storage used by the AI pipline as a source and destination. In order to maintain the completeness, integrity, accuracy, and traceability Anecdotes does not modify any customer data provided as an input within our AI pipelines.
Anecdotes do not use customer data to train AI models, and no data is leaked or exposed externally. Any output generated is stored securely within the customers’ own storage buckets, not in our systems.
Risk of non-compliance with said regulations is mitigated by not sending or using customer data to train the AI model.

The selected region is GCP US-Central1 and Anecdotes' DPA can be access at https://www.anecdotes.ai/data-processing-exhibit

We leverage Google’s Vertex AI models for key tasks like vector search, text generation, and embedding creation. While these models benefit from Google's rigorous bias evaluation processes (responsible AI practices across their model lifecycle), we recognize that bias can still manifest depending on specific use cases and data contexts. Here's how we handle it:
1. Task-Specific Bias Evaluation
Our team conducts manual reviews of model outputs during development to identify patterns of bias or poor performance that automated metrics might miss.
2. Mitigation Strategies
We implement automated post-processing to validate and adjust outputs, ensuring they are coherent, accurate, and aligned with intended use cases.
Human-in-the-loop review is integrated into our development cycle to catch and correct any unexpected behaviors or biases.
3. Customer Collaboration and Opt-In Control
Our AI features are offered only to opt-in customers, ensuring they are fully informed and aligned with the deployment.

All prompts and system instructions are human-predefined and human-in-the-loop review is integrated into our development cycle to catch and correct any unexpected behaviors or biases.

We actively assess performance using labeled data. Labeled datasets and manual labeling processes help us evaluate outputs for fairness, correctness, and reliability across diverse scenarios. Our team conducts manual reviews of model outputs during development to identify patterns of bias or poor performance that automated metrics might miss.

We implement automated post-processing to validate and adjust outputs, ensuring they are coherent, accurate, and aligned with intended use cases.
Human-in-the-loop review is integrated into our development cycle to catch and correct any unexpected behaviors or biases.

AI features offered by Anecdotes’ platform are available within the relevant module, customers can opt-in or opt-out from these AI powered features in the settings using on/off toggle.

AI powered features are opt-in add-ons so in case it fails no suggestions are presented but this does not affect the core platform or the module in which the AI feature was enabled.

All AI generated and processed data is stored within the dedicated customer storage, upon customer contact termination customer data is purged permanently.

Logging and monitoring includes but not limited to; rejection rate, accuracy drops, and feature usage.  

Yes, Anecdotes has an AI Governance and Security Framework in place which aligns with ISO42001, ISO27001 and NIST AI RMF.