Cybersecurity is evolving as organisations increasingly rely on cloud infrastructure, automation, application programming interfaces (APIs), and artificial intelligence. While traditional security strategies have often focused on protecting human users, modern digital environments also depend on machine identities, service accounts, software applications, automated workflows, and AI agents. These entities can access systems, exchange information, and perform tasks with varying levels of authority, creating new challenges for identity security.
TIDR, short for Tiger Identity Detection and Response, is a cybersecurity platform focused on non-human identity (NHI) and AI agent security. Its area of focus reflects a growing need to understand how software-based identities operate, what resources they can access, and how identity-related risks can be identified and addressed.
As businesses adopt more interconnected technologies, protecting identities is no longer limited to managing employee accounts and passwords. Organisations must also consider the credentials, permissions, tokens, and access relationships used by machines and automated systems.
TIDR sits within this emerging area of cybersecurity, where identity detection and response are increasingly relevant to managing the risks associated with non-human identities and AI-driven operations.
What Is TIDR?
Tiger Identity Detection and Response (TIDR) is a cybersecurity platform designed around the security of non-human identities and AI agents.
Non-human identities are digital identities used by software, infrastructure, applications, services, and automated processes rather than individual people. They allow systems to authenticate themselves, communicate with other services, and perform authorised operations.
AI agents introduce additional considerations because they may use tools, access business data, interact with external systems, and carry out multi-step tasks based on instructions or goals.
TIDR’s stated focus on these areas places it within the broader field of identity security. The objective of this field is to help organisations understand and manage the identities that operate across their digital environments.
Specific capabilities, integrations, detection methods, and response functions should be confirmed against the platform’s actual product documentation. However, the wider security challenges associated with NHIs and AI agents explain why this category of technology is becoming increasingly important.
Understanding Non-Human Identity Security
Non-human identity security focuses on protecting the digital identities that applications, services, machines, and automated processes use to access systems and resources.
These identities are essential to modern IT operations. A cloud application may need permission to retrieve information from a database, while an automated deployment service may require access to infrastructure management tools.
Although these identities support legitimate business activities, they can also introduce security risks when their credentials or permissions are not managed appropriately.
What Are Non-Human Identities?
Non-human identities can include several types of digital identities:
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Service accounts: Accounts used by applications or services to perform automated tasks.
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API credentials: Tokens or keys that allow software applications to communicate with other systems.
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Workload identities: Identities assigned to applications, containers, virtual machines, and other computing workloads.
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Machine identities: Digital identities used by devices, systems, and automated processes.
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Automation accounts: Accounts used by scripts, scheduled jobs, and operational workflows.
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AI agent identities: Identities associated with AI-powered systems that access tools, information, or software services.
Each identity may have its own authentication methods, permissions, lifecycle, and security requirements.
As organisations expand their use of cloud services and automation, the number of non-human identities can increase substantially. Managing them effectively requires visibility into how they are created, used, and authorised.
Why Non-Human Identities Matter
Non-human identities frequently operate without direct human supervision. They may run continuously, communicate with multiple services, or execute tasks automatically.
If an identity has excessive permissions or its credentials are exposed, an attacker may be able to misuse it to access sensitive information or perform unauthorised actions.
For example, a compromised service account with broad database permissions could potentially expose information beyond the application it was originally designed to support.
Similarly, an API token that remains valid after a project ends may create an unnecessary access pathway.
These risks make identity inventory, access governance, credential management, and monitoring important elements of modern cybersecurity.
The Growing Importance of AI Agent Security
AI agents are changing how organisations automate tasks and interact with digital systems. Unlike a basic script that follows a fixed sequence of instructions, an AI agent may interpret information, select tools, and execute multiple actions as part of a broader objective.
Depending on its design, an agent may access documents, query databases, interact with APIs, or initiate workflows through connected applications.
These capabilities can improve productivity, but they also create new security considerations.
Access to Business Resources
AI agents may require access to business systems to complete their assigned tasks. That access can include documents, databases, communication tools, cloud resources, or operational applications.
Security teams need to understand what an agent can access and whether those permissions are appropriate for its intended purpose.
An agent designed to summarise internal documents, for instance, should not automatically receive unrestricted access to every company system.
Excessive Permissions
Excessive permissions occur when an identity has more access than it needs to perform its intended function.
For AI agents, this problem can become particularly significant when an agent can invoke multiple tools or execute actions across connected systems.
Limiting permissions to the minimum necessary level helps reduce the potential impact of errors, misuse, or compromise.
Unintended Actions
AI agents may encounter ambiguous instructions, misleading information, or unexpected system responses. If they have authority to take consequential actions, these situations can create operational and security risks.
Appropriate safeguards may include restricting sensitive actions, requiring human approval for high-impact operations, and maintaining records of important activities.
Identity security forms one part of this broader protection strategy because an agent’s permitted actions are closely connected to its identity and access rights.
Protecting Credentials and Tokens
AI agents may use credentials or access tokens to interact with external services and internal systems.
If these credentials are exposed through insecure storage, logs, configuration files, or other weaknesses, an unauthorised party may be able to impersonate the agent or access its resources.
Secure credential storage, limited token lifetimes where appropriate, controlled permissions, and regular review are important measures for reducing these risks.
How Identity Detection and Response Supports Cybersecurity
Identity detection and response refers broadly to identifying suspicious identity-related activity and taking steps to investigate or contain potential threats.
In an environment containing many machine identities and AI agents, security teams need to understand not only which identities exist but also how they behave and what they are authorised to do.
Identity Visibility
Organisations need an accurate inventory of identities operating across their environments.
Without sufficient visibility, unused credentials, forgotten service accounts, and unknown automated processes may remain active without appropriate oversight.
Identity visibility can help security teams understand the relationships between identities, applications, permissions, and protected resources.
For a platform focused on NHI security, this is an important part of the problem space that TIDR addresses.
Detecting Suspicious Activity
Identity-related security incidents may involve unusual authentication attempts, unexpected access patterns, misuse of credentials, or activity that does not align with an identity’s normal purpose.
Detection approaches can examine available logs, authentication events, permissions, and other relevant signals to identify potentially suspicious behaviour.
The effectiveness of detection depends on the quality of the available data, the accuracy of the rules or models used, and the ability to distinguish legitimate activity from malicious behaviour.
Investigating Identity Risks
When suspicious activity is detected, security teams need enough context to understand what happened and assess its significance.
Useful information may include the identity involved, the resources accessed, the permissions available, the sequence of events, and any changes made during the incident.
A clear understanding of identity relationships can help investigators determine whether an event represents an isolated issue or part of a broader compromise.
Responding to Threats
Response measures depend on the nature and severity of a security incident.
Possible actions in identity security environments include revoking exposed credentials, disabling compromised accounts, reducing excessive permissions, invalidating access tokens, or restricting an identity’s access while an investigation continues.
Automated response can help security teams react quickly, but it should be implemented carefully. Disabling a critical service identity without understanding its dependencies may disrupt legitimate business operations.
The appropriate response should balance containment, operational continuity, and the need to prevent further unauthorised activity.
Key Security Risks Associated With Non-Human Identities
Understanding common identity risks helps explain the need for specialised security approaches.
Credential Exposure
Passwords, API keys, certificates, and tokens may be exposed through insecure storage, accidental disclosure, compromised systems, or poorly controlled development environments.
An exposed credential can allow an attacker to impersonate a legitimate identity.
Organisations should protect secrets, rotate credentials when necessary, and revoke compromised access promptly.
Overprivileged Access
An identity with broad permissions can create a larger attack surface than one restricted to a specific task.
Excessive privileges may allow a compromised identity to access sensitive systems, change configurations, or move between connected resources.
Applying the principle of least privilege helps limit these opportunities.
Unmanaged or Forgotten Identities
Digital identities may remain active after an application is retired, a project ends, or an automation workflow changes.
These identities can be overlooked if organisations lack a consistent inventory and lifecycle management process.
Regular reviews can help identify identities that are unused, unnecessary, or no longer appropriately configured.
Identity Sprawl
Identity sprawl occurs when organisations accumulate large numbers of accounts, tokens, service identities, and other credentials across different systems.
Managing this complexity becomes more difficult when identity information is distributed across multiple cloud providers, applications, and operational teams.
Centralised visibility and consistent governance practices can help reduce gaps in oversight.
Risks From Connected AI Tools
An AI agent may interact with several tools and services, each with its own access controls.
If the agent is given broad permissions or can pass untrusted information into sensitive workflows, the consequences of a mistake or manipulation may extend beyond the agent itself.
Security measures should therefore consider both the agent’s identity and the permissions of the tools it can invoke.
Benefits of a Focused NHI and AI Agent Security Approach
A security approach designed around non-human identities and AI agents can help organisations address risks that may be missed by controls focused primarily on human users.
Better Understanding of Identity Exposure
A clear view of identities, credentials, and permissions helps organisations understand where access-related weaknesses may exist.
This can support prioritisation by highlighting identities with sensitive access, unnecessary privileges, or inadequate oversight.
Improved Access Governance
Consistent identity governance helps ensure that access is appropriate to an identity’s intended purpose.
For machine identities, this may involve assigning clear ownership, documenting dependencies, reviewing permissions, and removing access when it is no longer required.
For AI agents, it also means defining the boundaries of permitted actions and the resources an agent can access.
Support for Incident Response
Identity-focused investigation can help security teams understand how a compromised credential or suspicious identity may affect other systems.
This context can support more targeted containment and reduce the chance of overlooking related access pathways.
Greater Oversight of Automated Systems
As organisations deploy more automation and AI, understanding which identities perform actions on their behalf becomes increasingly important.
Clear ownership, appropriate permissions, and reliable activity records can help make automated operations more accountable.
These benefits depend on the quality of the underlying security processes and the actual capabilities of the tools being used.
Best Practices for Securing Non-Human Identities and AI Agents
Organisations can strengthen their identity security posture through a combination of technical controls, governance, and ongoing monitoring.
Maintain an Identity Inventory
Document non-human identities across applications, infrastructure, and automation systems.
Where possible, record each identity’s owner, purpose, permissions, dependencies, and lifecycle status.
Apply Least Privilege
Grant each identity only the access required to perform its assigned function.
Review permissions regularly, particularly when applications change or AI agents gain access to additional tools.
Protect Credentials
Use appropriate secrets-management systems and avoid embedding sensitive credentials directly in source code or configuration files.
Establish procedures for credential rotation, revocation, and incident handling.
Monitor Identity Activity
Collect relevant authentication and access logs to help identify unusual behaviour.
Monitoring should reflect the identity’s role and operational context, since legitimate activity can vary significantly between applications and services.
Establish AI Agent Guardrails
Define what an AI agent is permitted to do, which tools it can access, and when human approval is required.
Sensitive actions should have suitable safeguards, and permissions should be reviewed as the agent’s capabilities evolve.
Test Detection and Response Procedures
Organisations should periodically assess whether their monitoring and incident response processes work as intended.
Testing can help identify missing logs, excessive response permissions, and operational dependencies that might complicate incident containment.
The Future of Non-Human Identity and AI Agent Security
The growing use of cloud services, distributed applications, and AI-powered automation is expanding the role of machine identities in modern organisations.
As more digital systems act autonomously or communicate with one another, identity security will increasingly need to account for non-human entities alongside employees and other human users.
Security teams will need practical ways to discover identities, assess their permissions, understand their relationships, and respond to suspicious activity.
AI agents make this challenge more complex because they can connect identity access with decisions, tool usage, and multi-step actions. Effective protection therefore requires a combination of identity governance, access controls, activity monitoring, application security, and appropriate human oversight.
TIDR’s focus on Tiger Identity Detection and Response for non-human identity and AI agent security reflects this emerging area of cybersecurity. Its relevance lies in the broader need to protect the digital identities that enable automated systems to function.
The effectiveness of any specific platform depends on its verified features, integrations, detection capabilities, and operational fit within an organisation’s existing security environment.
Conclusion
TIDR, or Tiger Identity Detection and Response, is a cybersecurity platform focused on non-human identity and AI agent security. This area of security addresses the challenges associated with machine identities, service accounts, application credentials, and AI agents that access resources or perform tasks across digital environments.
As organisations expand their use of automation and artificial intelligence, identity-related risks become increasingly important. Exposed credentials, excessive permissions, unmanaged accounts, and poorly controlled AI agent access can create opportunities for unauthorised activity.
A strong security approach combines identity visibility, least-privilege access, credential protection, activity monitoring, and well-defined response procedures. For AI agents, it also requires clear operational boundaries and appropriate safeguards around sensitive actions.
By focusing on the security of non-human identities and AI agents, TIDR belongs to a growing category of cybersecurity solutions addressing the changing relationship between identity, automation, and digital access.