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AI Governance: Risks, Frameworks, and 5 Essential Components

What is AI Governance?

AI governance is the system of policies, standards, and accountability measures organizations use to ensure artificial intelligence systems are safe, ethical, and legally compliant. It translates ethical principles (like fairness and transparency) into concrete operational guardrails to mitigate risks such as bias, privacy breaches, and regulatory non-compliance.

AI governance refers to the set of policies, processes, and frameworks that guide the responsible development, deployment, and oversight of artificial intelligence systems. It encompasses everything from ethical guidelines and regulatory compliance to technical controls and risk management strategies.

Key risks AI governance can help address:

  • Bias and discrimination: Unfair outcomes caused by biased data, algorithms, or model design.
  • Lack of explainability: AI decisions that cannot be easily understood, audited, or justified.
  • Data leakage and privacy violations: Exposure of sensitive information through training, inference, or poor data handling.
  • Hallucinations and inaccurate outputs: AI-generated content that is false, misleading, or unreliable.
  • Shadow AI: Unapproved AI tools and deployments operating outside governance controls.

Main components of an AI governance program include:

  • AI policies and acceptable use rules: Define approved AI use, data handling requirements, and usage restrictions.
  • AI inventory and use case registry: Track AI systems, owners, use cases, and governance status.
  • Risk assessment and classification: Categorize AI systems by risk to apply appropriate controls.
  • Model development and validation controls: Govern model testing, approval, monitoring, and performance.
  • Third-party AI vendor governance: Evaluate and oversee AI vendors for security, compliance, and risk.

This is part of a series of articles about AI security

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Why Is AI Governance Important?

AI governance has become a critical business function as organizations increasingly rely on AI to automate decisions, improve efficiency, and create new products and services. Without proper oversight, AI systems can introduce risks that affect customers, employees, regulators, and the organization.

A governance framework helps ensure that AI delivers value while operating within acceptable risk boundaries:

  • Reduces risk and prevents harm: AI systems can produce biased, inaccurate, or unsafe outcomes if not properly managed. Governance helps identify, assess, and mitigate these risks before they impact users or business operations.
  • Supports regulatory compliance: Governments and regulatory bodies are introducing AI-specific laws and requirements. Governance frameworks help organizations meet legal obligations related to privacy, transparency, fairness, and accountability.
  • Builds trust with stakeholders: Customers, employees, investors, and regulators are more likely to trust AI systems when organizations can explain how they work, how decisions are made, and what safeguards are in place.
  • Improves accountability: Governance establishes clear ownership for AI systems throughout their lifecycle. Defined roles and responsibilities make it easier to monitor performance, address issues, and respond to incidents.
  • Enhances transparency and explainability: Many AI models operate as complex systems that can be difficult to understand. Governance encourages documentation, model monitoring, and explainability practices that improve visibility into AI behavior.
  • Protects data and privacy: AI often depends on large volumes of sensitive data. Governance helps ensure that data is collected, stored, processed, and used in accordance with privacy requirements and security standards.
  • Supports consistent decision-making: Standardized policies and procedures create a consistent approach to AI development and deployment across teams, reducing confusion and improving efficiency.

Common Risks Addressed by AI Governance

Let’s review some of the business risks posed by AI systems, which AI governance programs can help mitigate.

Risk Description Impact How AI Governance Helps
Bias and Discrimination AI systems produce unfair outcomes due to biased data, algorithms, or model design. Legal liability, reputational damage, unfair decisions. Audits models for bias, promotes diverse data sources, and enforces fairness reviews.
Black Box Decision Making AI decisions cannot be easily explained or understood. Reduced trust, regulatory challenges, poor accountability. Requires explainability measures, documentation, and model transparency.
Data Leakage and Privacy Violations Sensitive information is exposed through training, inference, or poor data handling. Data breaches, compliance violations, financial penalties. Enforces data governance, access controls, encryption, and privacy-preserving techniques.
Hallucinations and Inaccurate Outputs AI generates false, misleading, or nonsensical information. Misinformation, operational errors, poor decision-making. Establishes validation processes, human review, monitoring, and continuous improvement.
Shadow AI Employees deploy or use AI tools without organizational oversight. Security risks, compliance failures, unmanaged AI usage. Implements AI policies, approved tool inventories, monitoring, and governance controls.

Core Pillars of AI Governance

1. Ethics and Fairness

Ethics and fairness ensure that AI systems align with societal values, avoid discrimination, and respect human rights. Ethical AI frameworks guide organizations in evaluating the broader impact of their technologies, considering not only what AI can do but what it should do. Addressing fairness requires intentional design choices, diverse perspectives, and ongoing assessment of outcomes.

Operationalizing ethics and fairness involves establishing clear principles, such as transparency, justice, and beneficence, and embedding them into every stage of the AI lifecycle. This includes stakeholder engagement, ethical impact assessments, and mechanisms for redress when harm occurs. Organizations that prioritize ethics can better anticipate societal concerns and maintain public trust.

2. Transparency and Explainability

Transparency and explainability are critical for building trust in AI systems and ensuring accountability. Transparency involves documentation of model design, data sources, and decision-making processes. Explainability focuses on making AI outputs understandable to technical and non-technical stakeholders, particularly in sensitive or high-impact domains.

Implementing transparency and explainability requires tools and practices that clarify how AI systems function. This may include model cards, audit trails, and user-facing explanations. By clarifying AI decision-making, organizations enable better oversight, support regulatory compliance, and empower users to question or challenge AI-driven outcomes.

3. Accountability

Accountability in AI governance means assigning responsibility for AI system outcomes and ensuring mechanisms exist for addressing errors or harms. This pillar is important for internal management and regulatory compliance. Accountability structures often include defined roles, escalation paths, and procedures for incident response.

Effective accountability requires documentation of decision-making processes, regular performance reviews, and communication with stakeholders. It also involves creating feedback channels for users and affected parties to report issues or concerns. Organizations that prioritize accountability are better equipped to identify problems early and take corrective action.

4. Risk Management

Risk management in AI governance focuses on identifying, assessing, and mitigating risks associated with AI deployment. These risks may include ethical, operational, legal, and reputational factors. A systematic approach involves regular risk assessments, scenario planning, and controls tailored to specific use cases.

Organizations should integrate risk management throughout the AI lifecycle, from initial design to post-deployment monitoring. This includes establishing risk thresholds, developing mitigation strategies, and conducting impact assessments. Proactive risk management reduces the likelihood of negative outcomes and supports continuous improvement and compliance.

5. Compliance

Compliance ensures that AI systems adhere to relevant laws, regulations, and industry standards. This includes data protection laws, sector-specific regulations, and emerging AI-specific frameworks. Compliance is an ongoing process as regulatory landscapes evolve and new standards emerge.

To maintain compliance, organizations must stay informed about legal developments and incorporate regulatory requirements into their AI governance frameworks. This involves audits, documentation, and reporting to demonstrate adherence. A strong compliance posture reduces legal risk and strengthens credibility with customers, partners, and regulators.

Key AI Governance Frameworks and Standards

CIS Companion Guides for AI Security

The CIS Companion Guides for AI Security extend the CIS Critical Security Controls v8.1 to AI-enabled environments. They are not a separate AI governance framework; instead, they explain how existing CIS Controls should be interpreted and applied to large language models, AI agents, and Model Context Protocol integrations.

Applicable for:
Organizations that already use, or plan to use, CIS Controls and need practical cybersecurity guidance for enterprise AI systems, LLM applications, autonomous agents, or MCP-based tool integrations.

Structure and requirements:
The CIS AI guidance is organized into three companion guides, each focused on a different layer of the AI technology stack:

  • AI LLM Companion Guide: Focuses on security risks in large language model deployments, including prompt handling, context integrity, sensitive data exposure, model and dataset provenance, inference environments, and monitoring across the AI lifecycle.
  • AI Agent Companion Guide: Applies CIS Controls to the agent layer, where AI systems plan, reason, invoke tools, and perform multi-step workflows. Key areas include governed autonomy, identity and access control, safe tool execution, workflow monitoring, and limits on what agents can do without human oversight.
  • Model Context Protocol Companion Guide: Focuses on securing MCP environments where AI systems discover, access, and invoke tools or enterprise resources. It emphasizes authorization, non-human identity management, secure tool access, endpoint hardening, execution controls, and auditability.

The guides help organizations address AI-specific security risks that traditional cybersecurity programs may not fully cover, including data leakage, prompt injection, retrieval poisoning, excessive agent autonomy, credential misuse, unsafe tool execution, and unapproved third-party integrations.

Learn more:
https://www.cisecurity.org/insights/white-papers/controls-v8-1-ai-llm-companion-guide
https://www.cisecurity.org/insights/white-papers/controls-v8-1-ai-agents-companion-guide
https://www.cisecurity.org/insights/white-papers/controls-v8-1-model-context-protocol-companion-guide

NIST AI Risk Management Framework

The NIST AI Risk Management Framework, developed by the U.S. National Institute of Standards and Technology, is a voluntary framework for identifying, assessing, managing, and governing risks associated with AI systems. It provides a common language for AI risk management and helps organizations improve the trustworthiness, safety, security, and reliability of AI systems.

Applicable for:
Organizations developing, deploying, procuring, or governing AI systems, especially those that need a flexible and non-sector-specific risk management approach.

Structure and requirements:
The NIST AI RMF is built around four core functions:

  • Govern: Establishes organizational policies, roles, responsibilities, accountability structures, risk tolerance, and oversight processes for AI risk management.
  • Map: Identifies the context in which an AI system is used, including its intended purpose, stakeholders, potential impacts, data sources, deployment environment, and possible risks.
  • Measure: Assesses, analyzes, and tracks AI risks using qualitative and quantitative methods. This may include testing system performance, evaluating bias, measuring robustness, reviewing security risks, and monitoring impacts.
  • Manage: Prioritizes and responds to identified risks. This includes implementing controls, documenting decisions, monitoring performance, escalating issues, and improving risk treatment over time.

The framework also emphasizes characteristics of trustworthy AI, including validity and reliability, safety, security and resilience, accountability and transparency, explainability and interpretability, privacy, and fairness.

Learn more:
https://www.nist.gov/itl/ai-risk-management-framework

ISO/IEC 42001 AI Management System

ISO/IEC 42001 is an international management system standard for artificial intelligence. It defines requirements for establishing, implementing, maintaining, and continually improving an AI management system within an organization.

Applicable for:
Organizations that provide, develop, deploy, or use AI-based products or services and want a formal, certifiable AI governance management system.

Structure and requirements:
ISO/IEC 42001 follows the management system structure used by other ISO standards, making it easier to integrate with standards such as ISO 9001, ISO/IEC 27001, or ISO/IEC 27701. The standard covers:

  • Organizational context: Understanding internal and external factors that affect AI use, including business objectives, stakeholders, legal obligations, and AI-related risks.
  • Leadership and accountability: Defining AI policy, leadership responsibilities, governance roles, and accountability for the AI management system.
  • Planning and risk management: Identifying AI-related risks and opportunities, setting objectives, and planning controls to address responsible AI development and use.
  • Support and resources: Ensuring appropriate resources, competence, awareness, communication, and documentation are in place to support AI governance.
  • Operation and lifecycle controls: Managing AI systems throughout their lifecycle, including development, acquisition, deployment, monitoring, maintenance, and retirement.
  • Performance evaluation: Monitoring, auditing, measuring, and reviewing the effectiveness of the AI management system.
  • Continual improvement: Correcting issues, improving governance processes, and updating controls as AI systems, risks, and regulatory expectations change.

ISO/IEC 42001 can support certification, which may help organizations demonstrate that they have a structured AI governance program in place.

Learn more:
https://www.iso.org/standard/42001

OECD AI Principles

The OECD AI Principles are international policy principles for trustworthy AI. They promote AI that is innovative, human-centered, transparent, secure, accountable, and aligned with democratic values and human rights.

Applicable for:
Governments, policymakers, public institutions, and organizations that want high-level principles for responsible AI governance, ethics, and policy development.

Structure and requirements:
The OECD AI Principles are not a certification standard and do not prescribe detailed technical controls. They are organized around values-based principles and policy recommendations.

The core principles include:

  • Inclusive growth, sustainable development, and well-being: AI should benefit people, society, and the environment.
  • Human-centered values and fairness: AI systems should respect human rights, democratic values, diversity, and fairness.
  • Transparency and explainability: Organizations should provide meaningful information about AI systems so stakeholders can understand when AI is being used and how outcomes are produced.
  • Robustness, security, and safety: AI systems should function reliably and securely throughout their lifecycle.
  • Accountability: Organizations and individuals involved in AI systems should be accountable for their proper functioning and outcomes.

The OECD also provides recommendations for policymakers, including investing in AI research, enabling responsible innovation, building human capacity, supporting international cooperation, and creating policy environments that encourage trustworthy AI.

Learn more:
https://www.oecd.org/en/topics/ai-principles.html

EU AI Act

The EU AI Act is a binding European Union regulation that establishes harmonized rules for the development, placing on the market, deployment, and use of AI systems in the EU. It uses a risk-based approach, applying stricter obligations to AI systems that create higher risks to health, safety, fundamental rights, or public interests.

Applicable for:
Providers, deployers, importers, distributors, and other operators of AI systems that are placed on the EU market, used in the EU, or affect people in the EU.

Structure and requirements:
The EU AI Act classifies AI systems by risk level and applies different obligations depending on the category:

  • Prohibited AI practices: Certain AI practices are banned because they create unacceptable risks. These include specific uses involving manipulation, exploitation of vulnerable groups, certain forms of social scoring, and other practices identified in the regulation.
  • High-risk AI systems: High-risk systems are permitted only if they meet strict requirements. These may include risk management, data governance, technical documentation, record keeping, transparency, human oversight, accuracy, robustness, cybersecurity, conformity assessment, and post-market monitoring.
  • Transparency obligations: Some AI systems must meet transparency requirements, such as informing users when they are interacting with AI or when certain AI-generated content is being used.
  • General-purpose AI models: Providers of general-purpose AI models, including models with systemic risk, are subject to specific obligations related to documentation, information sharing, risk assessment, incident reporting, cybersecurity, and model evaluation.
  • Low or minimal-risk AI systems: AI systems that do not fall into higher-risk categories generally have fewer mandatory obligations, although voluntary codes of conduct and responsible AI practices may still apply.

Organizations covered by the EU AI Act may need to classify their AI systems, maintain technical documentation, conduct conformity assessments, implement human oversight, monitor system performance, report serious incidents, and maintain evidence of compliance.

Penalties and enforcement:
The EU AI Act includes administrative fines based on the type and severity of non-compliance:

  • Violations of prohibited AI practices may result in fines of up to EUR 35 million or 7% of total worldwide annual turnover, whichever is higher.
  • Non-compliance with certain obligations for operators or notified bodies may result in fines of up to EUR 15 million or 3% of total worldwide annual turnover, whichever is higher.
  • Supplying incorrect, incomplete, or misleading information to authorities may result in fines of up to EUR 7.5 million or 1% of total worldwide annual turnover, whichever is higher.

For SMEs and startups, the regulation applies adjusted caps based on the lower of the fixed amount or percentage amount. Enforcement is handled through national competent authorities, market surveillance authorities, and, for certain general-purpose AI obligations, the European Commission and AI Office.

Learn more:
https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai

Main Components of an AI Governance Program

1. AI Policies and Acceptable Use Rules

AI policies and acceptable use rules provide the foundation for controlling how artificial intelligence technologies are used across an organization. They establish clear expectations for employees, contractors, and business units by defining approved use cases, prohibited activities, data handling requirements, and security obligations.

These policies help ensure that AI adoption aligns with legal, ethical, operational, and business objectives. They also reduce the risks associated with inconsistent usage, unauthorized deployments, and improper handling of sensitive information. As AI capabilities and regulations evolve, organizations should regularly update policies to address emerging technologies, changing risks, and new compliance requirements.

Practical steps:

  • Develop a formal AI acceptable use policy approved by executive leadership.
  • Define approved and prohibited AI use cases across business functions.
  • Establish rules for handling confidential, regulated, and customer data in AI systems.
  • Require human review for high-risk or business-critical AI outputs.
  • Create escalation procedures for policy violations and high-risk deployments.
  • Review and update policies regularly to reflect regulatory and technological changes.

2. AI Inventory and Use Case Registry

An AI inventory and use case registry creates visibility into all AI-related activities across an organization. By maintaining a centralized record of AI systems, models, applications, and services, governance teams can understand where AI is being used and identify systems that require additional oversight.

A comprehensive inventory supports risk management, compliance reporting, audit readiness, and incident response activities. It also helps prevent unmanaged or shadow AI deployments that operate outside established governance processes. Without a reliable inventory, organizations may struggle to assess exposure, prioritize resources, or demonstrate compliance with regulatory requirements.

Practical steps:

  • Create a centralized registry for all AI systems and AI-enabled applications.
  • Document system owners, business purpose, deployment status, and risk level.
  • Track data sources, model types, and applicable governance controls.
  • Require new AI initiatives to be registered before deployment.
  • Perform periodic reviews to identify unregistered or retired systems.
  • Integrate the inventory with risk assessment and audit processes.

3. Risk Assessment and Classification

Risk assessment and classification help organizations evaluate the potential impact of AI systems throughout their lifecycle. These processes identify factors such as data sensitivity, business criticality, regulatory obligations, and potential harm resulting from inaccurate or unsafe outputs. By understanding risk exposure before deployment, organizations can implement controls that are proportional to the level of risk.

A structured classification framework also helps governance teams prioritize reviews, allocate resources efficiently, and maintain oversight across a growing AI portfolio. Continuous reassessment ensures that controls remain effective as systems, data, and business requirements change.

Practical steps:

  • Establish a standardized AI risk assessment methodology.
  • Evaluate systems based on business impact, data sensitivity, and regulatory exposure.
  • Classify AI systems into defined risk categories such as low, medium, and high risk.
  • Apply additional testing and approval requirements to higher-risk systems.
  • Reassess risk levels after significant model, data, or operational changes.
  • Maintain documentation of assessments and mitigation decisions.

4. Model Development and Validation Controls

Model development and validation controls ensure that AI systems are built, tested, and deployed according to established governance standards. These controls address critical areas such as data quality, model performance, bias testing, documentation, and approval workflows. Their purpose is to reduce the likelihood of inaccurate outputs, operational failures, and unintended consequences that could affect users or business operations.

Validation should be performed before deployment and continue throughout the model’s operational life. Effective controls help organizations maintain confidence in model performance while supporting compliance, accountability, and ongoing risk management.

Practical steps:

  • Define development standards for data quality, testing, and documentation.
  • Conduct bias, fairness, and performance evaluations before deployment.
  • Implement independent model validation for high-risk systems.
  • Establish formal approval workflows before production release.
  • Monitor models continuously for drift, degradation, and data quality issues.
  • Maintain audit trails for model changes, testing results, and approvals.

5. Third-Party AI Vendor Governance

Third-party AI vendor governance helps organizations manage the risks associated with external AI providers, including model vendors, software platforms, cloud services, and AI-powered applications. Because organizations often depend on external providers for critical AI capabilities, they must ensure that these vendors meet internal requirements for security, privacy, compliance, and responsible AI practices.

Effective vendor governance extends oversight beyond organizational boundaries and helps address supply chain risks that could impact operations or regulatory obligations. Continuous monitoring and periodic reviews are essential because vendor practices, technologies, and risk profiles can change over time.

Practical steps:

  • Establish due diligence requirements for all AI vendors and service providers.
  • Assess vendor security controls, privacy practices, and compliance programs.
  • Review how vendors develop, train, and maintain AI models.
  • Include AI-specific contractual requirements and audit rights.
  • Monitor vendor performance, incidents, and changes to services.
  • Reassess vendor risks regularly throughout the relationship.

Best Practices for Effective AI Governance

1. Govern AI Agents as API Consumers

AI agents frequently interact with internal and external systems through APIs to retrieve data, execute actions, and complete workflows. From a governance perspective, these agents should be treated like any other API consumer rather than as a special class of application. Every AI agent should have a defined identity, approved access patterns, and documented business purpose.

Organizations should inventory AI agents that interact with APIs and subject them to the same security, compliance, and monitoring requirements applied to human users and traditional applications. This approach creates consistent oversight, improves accountability, and reduces the risk of uncontrolled access to sensitive systems and data.

2. Enforce Identity-Based Authentication and Authorization for AI Workflows

AI workflows should operate using strong authentication and authorization mechanisms that verify both the identity of the agent and its permissions. Shared credentials, hardcoded API keys, and anonymous access increase the risk of misuse and make it difficult to track activity. Identity-based access controls provide accountability for actions performed by AI systems.

Organizations should integrate AI agents with existing identity and access management platforms where possible. This allows administrators to apply centralized authentication policies, enforce multi-factor authentication where appropriate, and maintain audit logs of AI-driven actions. Strong identity controls support security and regulatory compliance.

3. Apply Least Privilege to AI Agents and Agent Personas

AI agents should receive only the permissions necessary to perform their intended tasks. Excessive privileges increase the impact of prompt injection attacks, configuration errors, compromised credentials, and unintended agent behavior. Applying the principle of least privilege helps contain risks and limit potential damage from misuse.

Permissions should be assigned based on specific agent roles and business functions. Organizations should regularly review access rights, remove unnecessary privileges, and separate high-risk actions from routine operations. Granular authorization controls help ensure that AI agents cannot access sensitive resources beyond their approved scope.

4. Monitor AI Agent Behavior Continuously

AI systems can change behavior over time due to model updates, new prompts, changing data sources, or evolving user interactions. Continuous monitoring helps organizations detect anomalies, policy violations, unexpected actions, and emerging security threats before they cause harm. Monitoring is particularly important for autonomous or semi-autonomous agents that interact directly with business systems.

Effective monitoring programs combine activity logging, behavioral analytics, alerting, and regular reviews of agent actions. Organizations should track API usage, tool execution, access patterns, and decision outcomes. Continuous visibility enables faster incident response and supports ongoing governance and risk management.

5. Protect APIs From AI-Driven Abuse and Business Logic Attacks

AI agents can generate requests at a speed and scale that exceed human activity. While this can improve efficiency, it can also expose APIs to abuse, excessive resource consumption, and exploitation of business logic vulnerabilities. Governance programs should account for authorized AI agents and external AI systems interacting with organizational APIs.

Protective measures may include rate limiting, anomaly detection, API gateways, bot management controls, and business logic validation. Organizations should monitor for unusual transaction patterns and automated behaviors that bypass intended workflows. Securing APIs against AI-driven abuse helps preserve system integrity and reduce operational risk.

6. Use Guardrails for Prompt Injection, Tool Misuse, and Unsafe Outputs

AI systems can be manipulated through prompt injection attacks, misleading instructions, malicious data sources, or improper tool usage. Without safeguards, agents may disclose sensitive information, perform unauthorized actions, or generate harmful outputs. Guardrails help keep AI behavior within approved boundaries.

Common guardrails include input filtering, output validation, policy enforcement engines, tool access restrictions, content moderation, and human approval workflows for high-risk actions. Organizations should test AI systems against known attack techniques and failure scenarios. Layered guardrails reduce the likelihood of unsafe behavior and improve the reliability of AI-powered workflows.

How to Govern and Secure Agentic AI with the Cequence AI Gateway

Putting AI governance into practice becomes especially challenging when AI agents start interacting with enterprise applications and data. The Cequence AI Gateway provides the security, governance, and control organizations need to confidently deploy agentic AI workflows at scale. Acting as a secure layer between AI agents and your applications, it uses context-aware security policies to govern every stage of AI interaction—from authentication and authorization through to continuous monitoring—so you can operationalize governance principles like accountability, least privilege, and oversight without slowing down adoption.

Key capabilities of the Cequence AI Gateway:

  • Agentic zero trust architecture: Authenticates every agent and verifies each action it takes, enforcing policy inline in the request path for the full session and on every tool call.
  • Agent least privilege access: Lets you define an agent’s job in plain English to automatically generate an “Agent Persona” with only the tools and permissions it needs, enforcing strict boundaries on what each agent can access and execute.
  • End-to-end authentication and authorization: Integrates with OAuth 2.1-compliant identity infrastructure and provides built-in token lifecycle management to deliver identity-based access while preventing unauthorized AI agent access.
  • Monitoring and visibility: Provides real-time visibility into AI-API traffic with full audit logging, tracking agent and user behavior, which applications are accessed, and what API calls agents are making.
  • Sensitive data protection: Applies DLP scanning to agent requests and MCP server responses to monitor, redact, and block sensitive data exposure across more than 100 out-of-the-box detection types.
  • Built-in security guardrails: Eliminates risks from rogue MCP servers with a trusted server registry, and applies automated tool risk scoring, rate limiting, and context-aware policies to block prompt injection and business logic abuse.

To see how the Cequence AI Gateway can help you safely enable and govern agentic AI in your organization, learn more about the Cequence AI Gateway.

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