The rush for AI technology is in full swing. However, without a focus on identity-first security, every implementation risks becoming vulnerable. Many organizations treat native AI like a standard web application, but it actually functions more like a junior employee who has root access and no supervision.
From Hype to High Stakes
Generative AI has progressed past the initial hype. Companies are now:
- Utilizing LLM copilots to speed up software development
- Streamlining customer service processes with AI agents
- Incorporating AI into financial operations and decision-making
Whether they are using open-source models or connecting to platforms like OpenAI or Anthropic, the focus is on achieving speed and scalability. However, many teams overlook an important point:
Each access point to an LLM or website represents a new identity edge. Additionally, every integration introduces potential risks unless identity and device posture are properly managed.
The AI Build vs. Buy Dilemma refers to the decision organizations face when determining whether to develop their own artificial intelligence solutions in-house or purchase existing ones from vendors. This choice involves weighing factors such as cost, time, expertise, and the specific needs of the business.
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Many businesses encounter a crucial choice:
Build: Develop in-house agents customized for their specific systems and workflows.
Buy: Utilize commercial AI tools and SaaS solutions.
The threat landscape remains indifferent to the path you select.
Custom-built agents can increase internal vulnerabilities, particularly if access control and identity segmentation are not properly enforced during operation. Third-party tools are frequently misused or accessed by unauthorized individuals, often corporate users using personal accounts, where governance issues arise. Securing AI is less about the algorithms themselves and more about who (or what device) is interacting with them and the permissions that interaction grants.
What’s at Stake?
AI agents are capable of acting on behalf of humans and accessing data as a human would. They are often integrated into essential business systems, such as:
- Source code repositories
- Finance and payroll applications
- Email accounts
- CRM and ERP systems
- Customer support records and case histories
Once a user or device is compromised, the AI agent can serve as a rapid backdoor to sensitive information. These systems hold significant privileges, and AI can enhance an attacker’s access.
Common AI-Related Threats:
- Identity-based attacks like credential stuffing or session hijacking aimed at LLM APIs
- Misconfigured agents with excessive permissions and lacking scoped role-based access control (RBAC)
- Weak session integrity where compromised or insecure devices request privileged actions through LLMs
How to Safeguard Enterprise AI Access:
To mitigate AI access risks while fostering innovation, you should implement:
- Phishing-resistant MFA for every user and device accessing LLMs or agent APIs
- Detailed RBAC aligned with business roles—developers shouldn’t have access to finance models
- Ongoing device trust enforcement, utilizing signals from EDR, MDM, and ZTNA
AI access control must transition from a one-time login verification to a real-time policy engine that adapts to current identity and device risks.
