Foundation Building – Controlled Integration into Enterprise Tools
As organizations become aware of AI usage and its associated risks, they shift from reactive containment to proactive enablement. This phase focuses on formalizing AI adoption, where Security and IT teams actively support Copilots, AI assistants, and SaaS-integrated AI tools. These tools are no longer operating in the shadows. They are integrated into core workflows such as CRM systems, ticketing platforms, document suites, and development pipelines.
The goal of this stage is to enable AI securely by establishing clear access boundaries, assigning ownership, and embedding AI into identity, governance, and security frameworks.
Key Challenge: Overprivileged AI Tools and Unscoped Access
Many AI tools, especially Copilots, require access to organizational systems to provide value. Without strict controls, these integrations are often granted excessive permissions. Whether interacting with Salesforce, Jira, G-Suite, or Office 365, Copilots may be capable of reading, writing, or deleting data far beyond what’s necessary.
Example Risk: A Jira Copilot is authorized using an inherited admin token and begins auto-merging tickets, bypassing required human review and violating change management policy.
IAM Risk: Non-Human Identity Explosion Without Attribution
As AI tools become embedded in workflows, they increasingly operate under their own credentials or tokens. These Non-Human Identities (NHIs), like bots, Copilots, and scripts, are often provisioned without consistent IAM policy, tracking, or delegation mapping. This results in:
No clear ownership of AI identities
Difficulty distinguishing between actions taken by humans versus AI
Users and developers may use their own identities for agents
Gaps in audit trails and accountability
Outcome: Organizations cannot determine who did what; was it the employee, or the AI acting on their behalf.
Security Risk: Secrets in Scripts, Impersonation Without Guardrails
Many early AI integrations are built in ways that hardcode secrets or tokens into scripts, or authorize AI systems to act without proper authorization or scoping. This opens the door to lateral movement, privilege escalation, and insider misuse.
Critical Risk: AI Copilots executing sensitive actions using unrestricted service accounts, with no session logging or runtime policy enforcement.
Solution Focus: Enforce Structured Integration and Identity Governance
As AI moves into the mainstream of the enterprise, the focus shifts from containment to control. When multiple autonomous AI agents interact using generic or shared Non-Human Identities (NHIs), it becomes nearly impossible to trace the origin of a malicious or erroneous command. This lack of auditable trails hinders incident response, forensics, and recovery efforts. Organizations must establish guardrails that allow AI to thrive safely, transparently, and with accountability.
Integration and Access Management
Implement formal intake and review processes for Copilots and AI assistants
Require impersonation and delegation mapping between users and AI tools
Use role-based templates to define what data each AI integration can access
Choose a solution to discover, manage and secure NHIs
Secure Credential and Token Management
Prohibit hardcoded secrets and rotate API tokens regularly
Vault credentials for all AI integrations and scripts
Treat AI tools as first-class identities with unique scopes and lifecycle management
Governance and Oversight
Establish a provisioning playbook per AI tool, including scoping, logging, and ownership
Develop and enforce:
Copilot Integration Policy
Third-Party AI Policy, including vendor evaluation and data boundaries
Enable individual and organizational level reporting to audit and assess for anomalies
Strategic Outcome
This phase focuses on establishing a foundation for scalability. With consistent access controls, NHI governance, and formal tool onboarding, enterprises can harness the full productivity benefits of AI tools while maintaining visibility, traceability, and control. Structured integration enables AI to become a secure, scalable part of daily operations and prepares the organization for the next maturity phase: deploying proprietary models and internal AI infrastructure.
Foundation Building – Controlled Integration into Enterprise Tools
As organizations become aware of AI usage and its associated risks, they shift from reactive containment to proactive enablement. This phase focuses on formalizing AI adoption, where Security and IT teams actively support Copilots, AI assistants, and SaaS-integrated AI tools. These tools are no longer operating in the shadows. They are integrated into core workflows such as CRM systems, ticketing platforms, document suites, and development pipelines.
The goal of this stage is to enable AI securely by establishing clear access boundaries, assigning ownership, and embedding AI into identity, governance, and security frameworks.
Key Challenge: Overprivileged AI Tools and Unscoped Access
Many AI tools, especially Copilots, require access to organizational systems to provide value. Without strict controls, these integrations are often granted excessive permissions. Whether interacting with Salesforce, Jira, G-Suite, or Office 365, Copilots may be capable of reading, writing, or deleting data far beyond what’s necessary.
Example Risk: A Jira Copilot is authorized using an inherited admin token and begins auto-merging tickets, bypassing required human review and violating change management policy.
IAM Risk: Non-Human Identity Explosion Without Attribution
As AI tools become embedded in workflows, they increasingly operate under their own credentials or tokens. These Non-Human Identities (NHIs), like bots, Copilots, and scripts, are often provisioned without consistent IAM policy, tracking, or delegation mapping. This results in:
No clear ownership of AI identities
Difficulty distinguishing between actions taken by humans versus AI
Users and developers may use their own identities for agents
Gaps in audit trails and accountability
Outcome: Organizations cannot determine who did what; was it the employee, or the AI acting on their behalf.
Security Risk: Secrets in Scripts, Impersonation Without Guardrails
Many early AI integrations are built in ways that hardcode secrets or tokens into scripts, or authorize AI systems to act without proper authorization or scoping. This opens the door to lateral movement, privilege escalation, and insider misuse.
Critical Risk: AI Copilots executing sensitive actions using unrestricted service accounts, with no session logging or runtime policy enforcement.
Solution Focus: Enforce Structured Integration and Identity Governance
As AI moves into the mainstream of the enterprise, the focus shifts from containment to control. When multiple autonomous AI agents interact using generic or shared Non-Human Identities (NHIs), it becomes nearly impossible to trace the origin of a malicious or erroneous command. This lack of auditable trails hinders incident response, forensics, and recovery efforts. Organizations must establish guardrails that allow AI to thrive safely, transparently, and with accountability.
Integration and Access Management
Implement formal intake and review processes for Copilots and AI assistants
Require impersonation and delegation mapping between users and AI tools
Use role-based templates to define what data each AI integration can access
Choose a solution to discover, manage and secure NHIs
Secure Credential and Token Management
Prohibit hardcoded secrets and rotate API tokens regularly
Vault credentials for all AI integrations and scripts
Treat AI tools as first-class identities with unique scopes and lifecycle management
Governance and Oversight
Establish a provisioning playbook per AI tool, including scoping, logging, and ownership
Develop and enforce:
Copilot Integration Policy
Third-Party AI Policy, including vendor evaluation and data boundaries
Enable individual and organizational level reporting to audit and assess for anomalies
Strategic Outcome
This phase focuses on establishing a foundation for scalability. With consistent access controls, NHI governance, and formal tool onboarding, enterprises can harness the full productivity benefits of AI tools while maintaining visibility, traceability, and control. Structured integration enables AI to become a secure, scalable part of daily operations and prepares the organization for the next maturity phase: deploying proprietary models and internal AI infrastructure.
Foundation Building – Controlled Integration into Enterprise Tools
As organizations become aware of AI usage and its associated risks, they shift from reactive containment to proactive enablement. This phase focuses on formalizing AI adoption, where Security and IT teams actively support Copilots, AI assistants, and SaaS-integrated AI tools. These tools are no longer operating in the shadows. They are integrated into core workflows such as CRM systems, ticketing platforms, document suites, and development pipelines.
The goal of this stage is to enable AI securely by establishing clear access boundaries, assigning ownership, and embedding AI into identity, governance, and security frameworks.
Key Challenge: Overprivileged AI Tools and Unscoped Access
Many AI tools, especially Copilots, require access to organizational systems to provide value. Without strict controls, these integrations are often granted excessive permissions. Whether interacting with Salesforce, Jira, G-Suite, or Office 365, Copilots may be capable of reading, writing, or deleting data far beyond what’s necessary.
Example Risk: A Jira Copilot is authorized using an inherited admin token and begins auto-merging tickets, bypassing required human review and violating change management policy.
IAM Risk: Non-Human Identity Explosion Without Attribution
As AI tools become embedded in workflows, they increasingly operate under their own credentials or tokens. These Non-Human Identities (NHIs), like bots, Copilots, and scripts, are often provisioned without consistent IAM policy, tracking, or delegation mapping. This results in:
No clear ownership of AI identities
Difficulty distinguishing between actions taken by humans versus AI
Users and developers may use their own identities for agents
Gaps in audit trails and accountability
Outcome: Organizations cannot determine who did what; was it the employee, or the AI acting on their behalf.
Security Risk: Secrets in Scripts, Impersonation Without Guardrails
Many early AI integrations are built in ways that hardcode secrets or tokens into scripts, or authorize AI systems to act without proper authorization or scoping. This opens the door to lateral movement, privilege escalation, and insider misuse.
Critical Risk: AI Copilots executing sensitive actions using unrestricted service accounts, with no session logging or runtime policy enforcement.
Solution Focus: Enforce Structured Integration and Identity Governance
As AI moves into the mainstream of the enterprise, the focus shifts from containment to control. When multiple autonomous AI agents interact using generic or shared Non-Human Identities (NHIs), it becomes nearly impossible to trace the origin of a malicious or erroneous command. This lack of auditable trails hinders incident response, forensics, and recovery efforts. Organizations must establish guardrails that allow AI to thrive safely, transparently, and with accountability.
Integration and Access Management
Implement formal intake and review processes for Copilots and AI assistants
Require impersonation and delegation mapping between users and AI tools
Use role-based templates to define what data each AI integration can access
Choose a solution to discover, manage and secure NHIs
Secure Credential and Token Management
Prohibit hardcoded secrets and rotate API tokens regularly
Vault credentials for all AI integrations and scripts
Treat AI tools as first-class identities with unique scopes and lifecycle management
Governance and Oversight
Establish a provisioning playbook per AI tool, including scoping, logging, and ownership
Develop and enforce:
Copilot Integration Policy
Third-Party AI Policy, including vendor evaluation and data boundaries
Enable individual and organizational level reporting to audit and assess for anomalies
Strategic Outcome
This phase focuses on establishing a foundation for scalability. With consistent access controls, NHI governance, and formal tool onboarding, enterprises can harness the full productivity benefits of AI tools while maintaining visibility, traceability, and control. Structured integration enables AI to become a secure, scalable part of daily operations and prepares the organization for the next maturity phase: deploying proprietary models and internal AI infrastructure.