Artificial intelligence is moving beyond chatbots and productivity assistants.
Organizations are increasingly deploying AI agents that can interact with databases, APIs, cloud platforms, business applications, customer systems, financial workflows, and other enterprise resources. These agents can make decisions and perform actions with limited human intervention.
That creates an important cybersecurity challenge.
Traditional security controls were largely designed around human users, applications, endpoints, networks, and known vulnerabilities. AI agents introduce another layer of risk because they can interpret instructions, access sensitive resources, interact with other systems, and take actions dynamically.
The recent $25 million Series A funding raised by Rein Security highlights the growing market for security technologies designed specifically for this environment. The funding brings Rein Security’s total funding to $35 million and comes as enterprises increasingly deploy AI agents with broader access to business systems and data.
The larger lesson extends beyond one cybersecurity company.
AI agents need security controls that can understand and govern what they actually do at runtime.
Why AI Agent Security Is Different
Traditional software generally follows predefined logic.
An AI agent can behave differently depending on the instructions it receives, the information it retrieves, the tools available to it, and the decisions it makes during execution.
This creates a dynamic security environment.
An enterprise AI agent could potentially:
• Access confidential databases
• Retrieve customer information
• Call internal and external APIs
• Modify business records
• Initiate transactions
• Interact with other AI agents
• Process documents and emails
• Execute code or workflows
• Access cloud infrastructure
• Make customer-facing decisions
The security challenge therefore extends beyond protecting the underlying AI model.
Organizations must understand what the agent is doing, what resources it is accessing, why it is performing an action, and whether that action is consistent with its intended purpose.
Rein describes its platform as operating at the AI-native runtime, providing visibility into agent actions and applying controls while those actions are being executed.
Runtime Security Becomes an Important Security Layer
Security teams have traditionally relied on controls such as endpoint protection, firewalls, identity management, application security testing, vulnerability management, and cloud security.
These controls remain essential.
However, AI agents introduce a new question:
What happens after the agent is authorized to operate?
An agent may have legitimate access but still make an unsafe decision.
For example, an AI agent might receive malicious instructions through a document, email, web page, API response, or another application. If the agent interprets that content as an instruction rather than untrusted data, it could potentially perform an action outside its intended role.
This is one reason prompt injection and indirect prompt injection have become major areas of AI security research.
Runtime controls can provide an additional opportunity to identify suspicious behavior and stop unauthorized actions before they result in damage.
Prompt Injection Is No Longer Just a Chatbot Problem
Prompt injection becomes significantly more serious when AI systems have access to real enterprise systems.
A manipulated chatbot response may create misinformation.
A manipulated enterprise agent could potentially create a business impact.
Consider an AI agent connected to:
• Customer databases
• Payment systems
• HR platforms
• Internal documentation
• Source code repositories
• Cloud infrastructure
• CRM systems
• Financial applications
• Supply chain platforms
If malicious instructions influence the agent, the resulting risk depends on the permissions and tools available to that agent.
This means organizations need to secure not only the prompts and models, but also the entire action chain surrounding the AI system.
The Agent Supply Chain Creates Additional Risk
AI agents are rarely built from a single component.
Modern agentic applications can depend on:
• Foundation models
• Open source libraries
• Agent frameworks
• APIs
• Plugins and tools
• Third party services
• Data sources
• Vector databases
• Cloud infrastructure
• Authentication systems
• External agents
Each dependency introduces another potential attack surface.
A vulnerable library, compromised dependency, malicious tool, or manipulated data source can influence the behavior of an AI application.
This makes software supply chain security particularly important for organizations developing custom AI agents.
Security teams should maintain visibility into the components used by AI applications and evaluate those components throughout the software development lifecycle.
Visibility Must Extend Beyond the Prompt
One of the most important concepts emerging in AI security is the difference between observing what an agent was asked to do and understanding what it actually did.
An AI agent might receive a harmless request but subsequently:
• Access an unexpected database
• Call an unauthorized API
• Retrieve sensitive information
• Send data to an external service
• Execute an unexpected function
• Interact with another agent
• Modify a business record
Monitoring only the initial prompt would not necessarily reveal the complete security event.
Organizations therefore need deeper observability across the agent’s execution path.
This includes monitoring:
• Agent identity
• User identity
• Tool usage
• API calls
• Database access
• Data movement
• Network connections
• Code execution
• Agent-to-agent communication
• Privilege changes
• External destinations
• Business outcomes
The objective is to connect technical activity with business context.
Least Privilege Must Apply to AI Agents
AI agents should not automatically receive the same access as the human employees who use them.
An employee may have access to a large amount of information, but an AI agent supporting a specific workflow may only need a small portion of that access.
Organizations should therefore apply least privilege based on:
• Business purpose
• User role
• Agent function
• Data sensitivity
• Required tools
• Application permissions
• Regulatory requirements
• Transaction limits
For example, an AI customer service agent may need access to customer support records but should not automatically have permission to access payroll information or security credentials.
Similarly, an AI financial assistant may need to analyze transactions but should not automatically receive unrestricted authority to initiate high-value transfers.
Runtime Guardrails Can Reduce the Blast Radius
Security controls do not always have to prevent an AI agent from operating.
Instead, organizations can establish boundaries around what an agent is permitted to do.
Examples include:
• Restricting access to approved applications
• Blocking unexpected API calls
• Limiting sensitive database queries
• Preventing unauthorized data transfers
• Restricting network destinations
• Requiring human approval for high-risk actions
• Monitoring unusual agent behavior
• Blocking deviations from established behavior patterns
These controls can provide security teams with additional time to investigate suspicious activity without necessarily shutting down legitimate AI workflows.
Real World Research Shows the Risk Is Emerging
The security concerns surrounding AI agents are not purely theoretical.
Rein has reported research involving an AI shopping agent belonging to a major US retailer, demonstrating how agent functionality could potentially be abused.
The company has also described an enterprise onboarding agent encountering a PDF containing hidden prompt injection instructions. According to Rein, its technology detected and blocked the resulting action before damage occurred.
These examples demonstrate an important security principle:
The risk associated with an AI agent is determined not only by what the agent knows, but also by what the agent is capable of doing.
An agent with access to sensitive systems and the ability to perform consequential actions requires significantly stronger controls than a system that only generates text.
AI Agents Are Becoming Part of Critical Business Processes
Organizations are increasingly exploring AI agents across multiple business functions.
Financial Services and Banking
Financial institutions can use AI agents for customer support, financial analysis, fraud detection, document processing, compliance workflows, and operational processes.
These environments require strong controls because agents may interact with financial records, customer information, payment systems, and regulated data.
Healthcare and Life Sciences
Healthcare organizations can use AI agents to support administrative workflows, patient services, research, documentation, and data analysis.
Security controls must protect sensitive health information and ensure that AI systems cannot access or disclose information beyond authorized purposes.
Retail and E-commerce
Retail organizations can deploy agents for customer service, product recommendations, order management, fraud detection, inventory workflows, and digital commerce.
Runtime monitoring can help identify suspicious transactions, unauthorized data access, and abnormal agent activity.
Manufacturing and Industrial Organizations
Manufacturers are increasingly adopting AI across supply chain management, production operations, engineering, maintenance, and business applications.
AI agents interacting with operational or production systems require particularly strong access controls and monitoring.
Energy and Critical Infrastructure
Energy companies and other critical infrastructure operators may eventually use AI agents to support monitoring, maintenance, incident analysis, and operational decision-making.
Because mistakes can have physical and operational consequences, these deployments require strong governance, segmentation, human oversight, and runtime protection.
Technology and SaaS Companies
Technology companies often build AI agents that interact with source code, cloud infrastructure, customer environments, APIs, and development platforms.
These organizations should integrate AI security into application security, DevSecOps, cloud security, and software supply chain programs.
Government and Public Sector
Government agencies may use AI agents to improve citizen services, document processing, analysis, and administrative workflows.
Strong identity controls, data governance, auditability, and compliance controls are essential when agents interact with sensitive government information.
What Organizations Should Do Now
Organizations preparing to deploy enterprise AI agents should begin establishing security controls before agents receive broad access.
1. Inventory AI Agents
Organizations should maintain visibility into internally developed, third party, and experimental AI agents.
2. Map Agent Permissions
Document which systems, APIs, databases, cloud services, and data sources each agent can access.
3. Apply Least Privilege
Give agents only the permissions necessary for their specific business functions.
4. Monitor Runtime Behavior
Security teams should monitor agent actions, tool calls, data access, API activity, and network communication.
5. Test Prompt Injection
AI security assessments should include direct and indirect prompt injection scenarios, malicious documents, poisoned data sources, and unauthorized tool use.
6. Establish Human Approval
High-risk actions such as financial transfers, privileged changes, sensitive data exports, or production modifications should require appropriate human oversight.
7. Secure the Agent Supply Chain
Organizations should assess models, frameworks, libraries, APIs, tools, plugins, and other dependencies used by AI agents.
8. Protect Agent Credentials
API keys, service credentials, tokens, and other secrets should be securely managed, rotated, monitored, and protected from exposure.
9. Maintain Audit Trails
Organizations should be able to reconstruct what an AI agent did, which user initiated the activity, which systems were accessed, and what outcome occurred.
10. Integrate AI Security Into Incident Response
Security operations teams should have procedures for investigating compromised agents, malicious prompts, unauthorized tool calls, data exposure, and agent-to-agent attacks.
AI Security Needs to Move From Posture to Prevention
Traditional vulnerability management remains an important part of cybersecurity.
However, AI agents create risks that may not always appear as conventional vulnerabilities.
An agent can be securely configured today and still behave unexpectedly tomorrow because its inputs, connected systems, tools, or operating environment have changed.
This makes continuous security important.
Organizations should combine:
• AI security testing
• Runtime monitoring
• Identity security
• Application security
• Cloud security
• Data protection
• Software supply chain security
• Threat detection
• Human oversight
• Incident response
Together, these controls create a more resilient security architecture for agentic AI.
Conclusion
Rein Security’s $25 million funding round is another indication that AI agent security is becoming a significant enterprise cybersecurity category. Rein announced the Series A on October 8, 2026, bringing its total funding to $35 million as organizations accelerate the deployment of AI agents across business systems and workflows.
But the broader lesson is more important than the funding itself.
AI agents are changing the relationship between software and enterprise systems. They are no longer simply generating information. Increasingly, they can retrieve data, call APIs, execute workflows, make decisions, and take actions on behalf of users and organizations.
That requires a different security mindset.
Organizations must treat AI agents as privileged digital identities with defined permissions, monitored behavior, controlled tools, secure dependencies, and measurable business boundaries.
Runtime security can become an important layer in this model because it focuses on what AI systems actually do while they are operating, not only how they were configured before deployment.
The organizations that combine AI innovation with strong governance, continuous monitoring, secure development, runtime controls, and human oversight will be better positioned to adopt agentic AI while protecting sensitive data, business operations, customers, and regulatory obligations.
About COE Security
COE Security partners with organizations in financial services, healthcare, retail, manufacturing, and government to secure AI-powered systems and ensure compliance.
Our offerings include:
• AI-enhanced threat detection and real-time monitoring
• Data governance aligned with GDPR, HIPAA, and PCI DSS
• Secure model validation to guard against adversarial attacks
• Customized training to embed AI security best practices
• Penetration Testing (Mobile, Web, AI, Product, IoT, Network & Cloud)
• Secure Software Development Consulting (SSDLC)
• Customized CyberSecurity Services
• Follow COE Security on LinkedIn for ongoing insights into safe, compliant AI adoption.
In addition, COE Security helps organizations secure enterprise AI agents through AI security assessments, agentic AI security testing, prompt injection assessments, AI application security reviews, runtime security evaluations, identity and access management reviews, API security testing, cloud security assessments, software supply chain assessments, data protection reviews, vulnerability management, threat monitoring, and penetration testing.
For financial services and banking, COE Security helps assess AI agents connected to financial applications, customer information, payment environments, fraud detection systems, APIs, and regulated data while strengthening access controls and monitoring.
For healthcare and life sciences, we help organizations evaluate AI systems that process sensitive information, strengthen data governance, assess AI integrations, test access controls, and support compliance aligned security programs.
For retail and e-commerce, we help secure customer-facing AI agents, digital commerce platforms, payment environments, APIs, cloud infrastructure, customer data, and automated business workflows.
For manufacturing and industrial organizations, we help assess AI applications connected to enterprise systems, cloud environments, supply chain platforms, operational technologies, and connected infrastructure.
For energy and critical infrastructure organizations, we help strengthen AI security, network segmentation, identity controls, monitoring, incident response readiness, and security testing for systems where unauthorized AI actions could create significant operational consequences.
For technology and SaaS companies, COE Security helps secure AI agents, APIs, cloud environments, source code, development platforms, third party dependencies, and software supply chains through security testing and secure development practices.
For government and public sector organizations, we help strengthen AI governance, data protection, identity security, cloud security, application security, monitoring, vulnerability management, and compliance programs.
COE Security also helps organizations establish responsible AI security programs that address emerging risks such as prompt injection, excessive agent permissions, insecure AI integrations, malicious tools, sensitive data exposure, unauthorized agent actions, AI supply chain risks, and AI driven cyber threats.
Our goal is to help organizations adopt AI securely while protecting sensitive information, strengthening cyber resilience, and maintaining compliance as AI agents become increasingly integrated into critical business processes.
Follow COE Security on LinkedIn for ongoing insights into safe, compliant AI adoption and to stay updated and cyber safe.
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