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AI Governance

Ensure Responsible AI Use Within The Enterprise With AI Governance

AI TRiSM Platform that creates the guardrails for organizations to take control of their AI approach and ensure responsible, low- risk deployment. With governance now baseline, it’s simple to establish controls that transform AI user risk into a controlled environment.

Governance that unlocks AI adoption and productivity.

Governance over employee AI consumption is a baseline requirement and demands controls across the enterprise.

Risk Management

Gain visibility and control over enterprise-level AI user risk, protecting your organization from potential pitfalls through comprehensive risk scoring, and alerting systems to react to real-time threats.

Privacy Assurance

Enforce security protocols and privacy controls to ensure that confidential information remains secure.

Policy Enforcement

Make sure your policies for Gen AI are in line with your company's values and compliance needs via preventative policy enforcement through SWG integration or proxy, ensuring governance controls are embedded at every employee touchpoint.

Performance Monitoring

Keep track of AI performance and usage patterns, optimize results and allocate resources accordingly.

The Power of Control: AI Governance Within The Enterprise

Critical gaps in AI visibility and governance introduces legal, privacy, IP, and compliance risks that are challenging to see, monitor and remediate. That’s where Portal26 steps in.

Before Portal26

• Minimal visibility into AI usage and purpose
• Shadow AI creating critical observability gaps
• Legal, data privacy, IP, and compliance risks mounting
• Security teams unable to investigate AI incidents
• Business teams lack tools to measure AI impact
• Untrained employees using AI tools without policy
• Governance controls missing across organizational layers
• Missing adaptive compliance strategies for evolving regulations

After Portal26

• AI governance across all organizational layers
• Shadow AI transformed from blind spot to controlled environment
• Comprehensive risk scoring and real-time alerting systems
• Full audit trails and forensic capabilities for incidents 360-degree view of AI use within the enterprise
• Comprehensive employee training and policy distribution
• Multi-layered controls embedded at every employee touchpoint
• Adaptive compliance strategies for ever-evolving AI regulations

Experience AI Governance Today

Are you ready to take your organization to the next level of responsible AI use? Schedule a live demo with our team of experts to discover how our enterprise AI governance platform can revolutionize your AI strategy. Discover insights, enforce ethics, and safeguard your AI path.

Your AI Governance FAQs

What is the difference between Generative AI and general AI?
Generative AI is a type of artificial intelligence system that generates new content, such as images, text, or audio, often using deep learning techniques. These systems are specialized in creating data rather than understanding or completing tasks. On the other hand, general AI is a system that can understand, learn, and apply knowledge across a wide range of tasks.

What are the governance principles for a new generation of AI, and what are the potential risks and challenges associated with Generative AI usage?
GenAI Governance is a key aspect of any GenAI strategy:

• Transparency - Ensure transparency in AI decision-making processes and algorithms.
• Accountability - Clearly define roles and responsibilities.
• Fairness - Strive for fairness and avoid biases in AI systems.
• Security - Implement robust security measures.

Potential risks include:

• Bias and discrimination - GenAI systems may inherit biases from training data.
• Privacy concerns - Privacy issues may arise when generating content involving personal information.
• Regulatory compliance - Evolving regulations pose compliance challenges.
• Understanding disparity/knowledge gaps - Users may not fully understand GenAI operations, leading to mistrust.

Why is it important for organizations to integrate ethical considerations at the earliest stages of Generative AI design and development?
Integrating ethical considerations helps identify risks, build trust, ensure legal compliance, and address societal concerns, contributing to the long-term viability of AI systems.

What is the model Generative AI governance framework and what should it include?
A model GenAI governance framework includes:

• Ethical guidelines - Outline ethical considerations.
• Transparency measures - Define how transparency will be achieved.
• Accountability mechanisms - Establish roles and responsibilities.
• Data governance - Protocols for responsible data handling.
• Risk assessment - Regular assessments to mitigate potential risks.
• Regulatory compliance - Ensure alignment with regulations.
• Continuous monitoring and evaluation - Mechanisms for evaluation and improvement.
• Stakeholder involvement - Incorporate diverse perspectives.

How is the disruptive nature of AI changing the requirements for governance frameworks?
AI brings unprecedented effects in business operations, sparking innovation and automation while prompting companies to reassess their usage regarding new ethical questions. Adequate governance policies require constant review and adaptation.