What are the biggest AI cybersecurity risks facing enterprises?
AI cybersecurity risks arise from the interaction of models, applications, infrastructure, users, and external services. The OWASP Top 10 for Large Language Model Applications identifies important attack categories, including prompt injection, sensitive information disclosure, supply chain vulnerabilities, and excessive agency.
Enterprise security teams should prioritize visibility and controls across the following risks.
Shadow AI and unmanaged infrastructure
Shadow AI occurs when employees or business units use AI tools without appropriate security oversight. Unmanaged applications can introduce unknown data flows, insecure configurations, and third-party dependencies. Security teams may also overlook AI-related services exposed to the internet.
This challenge extends beyond employee use of AI applications. AI development environments, model-serving infrastructure, and integrations can expand an organization's attack surface when deployed without established security controls.
According to Bitsight's 2026 State of the Underground report, exposed AI-related services surged 360% in 2025, highlighting how rapidly AI infrastructure is expanding the external attack surface.
Bitsight's cyber risk intelligence approach emphasizes continuous visibility across the extended attack surface, including unknown assets, vulnerabilities, and external dependencies. These capabilities can help security teams investigate AI-related infrastructure exposures that conventional asset inventories may overlook.
Prompt injection and AI manipulation
Prompt injection occurs when attackers embed malicious instructions in content processed by AI systems.
These instructions may appear in documents, websites, emails, or other external data sources. Depending on an application's permissions, successful attacks could expose confidential information or trigger unauthorized actions.
Organizations should treat external inputs as untrusted, enforce authorization boundaries, and restrict sensitive actions.
Sensitive data exposure
AI applications may expose confidential information through prompts, connected data sources, generated outputs, or improperly configured integrations.
For example, employees might submit proprietary information to external AI services without understanding their data handling policies.
Data classification, access restrictions, encryption, and approved-use policies help reduce these risks.
AI supply chain vulnerabilities
Enterprise AI systems depend on external models, APIs, cloud infrastructure, software components, and service providers. A compromised dependency can introduce vulnerabilities into connected business applications. Third-party risk management should therefore include AI providers and their relevant security dependencies.
Bitsight's September 2026 announcement on supply chain cyber risk intelligence highlights the growing need to connect external exposure monitoring, threat intelligence, and vendor remediation workflows as AI accelerates the threat landscape.
Although this research and positioning address broader supply chain cybersecurity risks rather than AI-specific incident rates, they reinforce the importance of monitoring dependencies across AI-enabled enterprise environments.
Insecure AI infrastructure and excessive agent permissions
Exposed model endpoints, vulnerable software, weak authentication, and misconfigured cloud services can create opportunities for attackers. Autonomous AI agents introduce additional risks when they can access sensitive systems or execute actions without sufficient restrictions.
Security teams should apply least-privilege access, monitor agent activity, secure supporting infrastructure, and require approval for high-impact operations.
How do you implement an AI risk management framework in an enterprise?
Implementing an AI risk management framework requires translating governance principles into operational security processes. Enterprise AI risk management best practices emphasize clear ownership, continuous visibility, consistent security requirements, and integration with existing cybersecurity programs.
1. Establish AI governance and security policies
Assign accountability for AI cybersecurity risk and define responsibilities across security, IT, GRC, procurement, and business teams.
Create an AI risk management policy addressing acceptable use, data handling, approval requirements, security controls, and incident escalation.
Policies should also define when deployments require additional review, such as when AI agents receive privileged access or applications process regulated information.
2. Discover and inventory AI systems
Identify AI applications, models, infrastructure, agents, and external services operating across the enterprise.
Combine application inventories, procurement records, cloud visibility, and attack surface discovery to identify managed and unmanaged deployments.
Document system ownership, sensitive data access, and relevant dependencies.
3. Define AI risk categories and security controls
Establish a consistent risk taxonomy covering unauthorized access, data exposure, prompt injection, infrastructure vulnerabilities, supply chain compromise, and autonomous agent behavior.
Apply baseline security requirements across AI systems, with additional controls for sensitive or business-critical deployments.
4. Integrate AI risks into existing cybersecurity workflows
Connect AI security findings to established vulnerability management, incident response, third-party risk management, and GRC processes.
For example, vulnerabilities affecting AI-hosting infrastructure should enter existing remediation workflows with clear ownership and deadlines.
5. Continuously monitor and improve
Monitor AI-related assets, permissions, vulnerabilities, and third-party exposures as environments evolve.
Track remediation progress, validate security controls, and update policies when new threats or significant deployment changes emerge.
This approach makes AI risk management an ongoing operational discipline rather than a one-time compliance exercise.
How can CISOs integrate AI risk management into existing cybersecurity programs?
CISOs can integrate AI risk management by extending established security governance, detection, remediation, and reporting processes to AI-related technologies.
Effective implementation requires coordination between teams with different operational responsibilities.
Team | AI risk management responsibilities |
CISOs and security leadership | Define AI security strategy, risk tolerance, and executive oversight |
SOC and SecOps | Monitor AI-related threats, investigate suspicious activity, and coordinate incident response |
Vulnerability management | Identify and remediate vulnerabilities affecting AI infrastructure |
GRC | Maintain AI policies, risk registers, control requirements, and compliance evidence |
Third-party risk management | Evaluate AI providers and monitor supplier security exposures |
IT and application security | Secure AI deployments, enforce access controls, and validate configurations |
Integrating AI security into SOC operations
SOC teams should incorporate AI applications and infrastructure into existing detection and response workflows.
Monitoring priorities may include unusual API activity, suspicious authentication attempts, unexpected data transfers, and unauthorized agent actions.
For example, if an AI agent begins accessing sensitive repositories outside its normal operating pattern, SOC analysts should be able to investigate the activity using application logs, identity telemetry, and relevant security alerts. Response procedures may include restricting credentials, disabling integrations, or escalating the incident to the application owner.
AI-specific threats may require application-level telemetry that conventional network monitoring cannot provide.
Integrating AI governance into GRC programs
GRC teams should maintain centralized records of AI systems, risk owners, security requirements, exceptions, and remediation decisions.
For example, an AI application requesting access to confidential customer information may require additional security controls and documented approval before deployment. GRC teams can track the decision, record any accepted risks, and verify that required safeguards remain in place.
They should also coordinate with privacy and legal teams when AI deployments introduce regulatory obligations. Aligning AI governance with existing enterprise risk management processes helps security and business leaders make consistent, evidence-based decisions.
How should organizations assess and prioritize AI security risks?
Organizations should prioritize AI security risks according to potential business impact, exposure, threat activity, and existing safeguards. AI risk assessment informs these decisions, but represents only one component of the broader AI risk management framework.
Key prioritization considerations include:
- Exposure: Whether AI services are accessible from untrusted networks
- Data sensitivity: Whether systems process confidential or regulated information
- Privileges: Whether AI applications can execute actions or access critical systems
- Threat activity: Whether vulnerabilities are actively exploited
- Business impact: Whether compromise could disrupt essential operations
- Control effectiveness: Whether safeguards adequately reduce identified risks
For example, an internet-facing AI application with weak authentication and access to sensitive data generally warrants greater urgency than an isolated experimental model.
Security teams should translate these findings into remediation priorities, monitoring requirements, or formal risk acceptance decisions.
What cybersecurity controls should an AI risk management framework include?
An AI risk management framework should combine foundational cybersecurity protections with controls addressing AI-specific vulnerabilities.
Security Control | Role in AI Risk Mitigation | Target Operational Owner |
AI Asset Discovery | Identify managed/unmanaged AI services, shadow AI, and exposed infrastructure. | SecOps / Attack Surface Mgmt |
Identity & Access Management (IAM) | Restrict access to AI applications, fine-tuning data, and administrative interfaces. | IAM / AppSec |
Data Protection & DLP | Limit exposure of confidential data via prompts, logs, or model outputs. | Data Security / GRC |
Secure Infrastructure Configuration | Remediate vulnerabilities in AI hosting, APIs, cloud instances, and orchestration layers. | Vulnerability Mgmt / Cloud Sec |
Prompt Injection Defenses | Enforce authorization boundaries, apply least-privilege access, handle untrusted inputs securely, and validate sensitive actions | AppSec / Software Engineering |
Agent Permission Boundaries | Apply least privilege, scoped API tokens, and mandatory human-in-the-loop approvals. | AppSec / SOC |
Supply Chain Risk Management | Vetting third-party model providers, software dependencies, and hosting services. | TPRM / Procurement |
Logging & Behavioral Audit | Maintain comprehensive logs of prompts, agent interactions, and administrative changes. | SOC / SecOps |
AI-Specific Incident Response | Maintain playbooks for compromised agent workflows, data leakage, and prompt exploits. | Incident Response / SOC |
Continuous Security Testing | Conduct red teaming and vulnerability testing against AI-specific attack vectors. | Red Team / AppSec |
These controls should operate together as part of a layered security strategy.
For example, traditional access controls may prevent unauthorized users from accessing an AI application, but they do not necessarily prevent malicious instructions from influencing an authorized AI agent. Organizations should enforce authorization boundaries, apply least-privilege permissions, treat external inputs as untrusted, and validate sensitive actions before execution.
Security teams should test these controls before deployment and periodically validate their effectiveness as AI systems evolve.
How Bitsight supports enterprise AI cybersecurity risk management
Effective AI cybersecurity risk management requires visibility into external exposures, supporting infrastructure, and third-party dependencies.
Bitsight helps organizations understand their extended attack surface, contextualize vulnerabilities, prioritize remediation, and communicate cybersecurity risk.
Identify and prioritize external exposures
Bitsight Security Posture Management continuously maps externally observable assets and security exposures. By combining exposure information, threat intelligence, and business context, Bitsight helps security teams identify vulnerabilities, prioritize remediation, and measure security posture improvement.
These capabilities support AI risk management when AI applications or supporting infrastructure contribute to an organization's external attack surface.
Monitor third-party AI dependencies
Bitsight Third-Party Risk Management helps organizations assess vendors, monitor security posture, and respond to emerging third-party threats.
Bitsight Continuous Monitoring provides ongoing visibility into vendor security exposures, while Bitsight Cyber Threat Intelligence adds context about vulnerabilities, compromised credentials, and adversary activity.
These insights help security and GRC teams understand cybersecurity risks involving AI providers and their supporting ecosystems.
Strengthen SOC and GRC coordination
Bitsight provides contextualized cyber risk intelligence that supports both operational security decisions and enterprise governance. Security teams can use threat and exposure intelligence to prioritize investigation and remediation. GRC leaders can use security posture trends and reporting capabilities to communicate risk and track progress.
This shared intelligence helps organizations align technical remediation priorities with broader AI security governance objectives.
AI risk management framework FAQs
What is the difference between AI risk management and AI risk assessment?
AI risk assessment identifies and evaluates risks associated with specific AI systems or use cases. AI risk management encompasses the broader, ongoing activities of governance, risk mitigation, security monitoring, incident response, and continuous improvement.
Is the NIST AI Risk Management Framework mandatory?
NIST AI RMF 1.0 is voluntary and does not independently establish legal compliance obligations. However, organizations may face applicable AI, cybersecurity, privacy, or industry-specific requirements.
How does AI risk management differ from traditional cybersecurity risk management?
AI risk management extends traditional cybersecurity practices to address additional risks involving model behavior, manipulated inputs, training and inference data, autonomous agents, and AI-specific dependencies.
How often should organizations review their AI risk management framework?
Organizations should review their framework periodically and following significant changes, such as new AI deployments, expanded permissions, critical vulnerabilities, security incidents, or regulatory developments.
What metrics should CISOs use to measure AI risk management effectiveness?
Useful metrics include AI inventory coverage, security control compliance, unresolved critical exposures, remediation time, incident response performance, and third-party security posture trends.
Strengthen enterprise AI security with continuous risk management
An effective AI risk management framework helps organizations establish accountability, mitigate emerging threats, and maintain security oversight throughout the AI lifecycle.
For CISOs, SOC leaders, and GRC teams, integrating AI governance into existing cybersecurity programs provides a practical foundation for managing expanding attack surfaces, third-party dependencies, and AI-specific vulnerabilities.
Explore how Bitsight Security Posture Management helps organizations identify external exposures, prioritize remediation, and continuously measure cybersecurity posture.
