DISC InfoSec blogAI Agents Archives

Jun 30 2025

Why AI agents could be the next insider threat

Category: AI,Risk Assessment,Security Risk Assessment — disc7 @ 5:11 pm

1. Invisible, Over‑Privileged Agents
Help Net Security highlights how AI agents—autonomous software acting on behalf of users—are increasingly embedded in enterprise systems without proper oversight. They often receive excessive permissions, operate unnoticed, and remain outside traditional identity governance controls

2. Critical Risks in Healthcare
Arun Shrestha from BeyondID emphasizes the healthcare sector’s vulnerability. AI agents there handle Protected Health Information (PHI) and system access, increasing risks to patient privacy, safety, and regulatory compliance (e.g., HIPAA)

3. Identity Blind Spots
Research shows many firms lack clarity about which AI agents have access to critical systems. AI agents can impersonate users or take unauthorized actions—yet these “non‑human identities” are seldom treated as significant security threats.

4. Growing Threat from Impersonation
TechRepublic’s data indicates only roughly 30% of US organizations map AI agent access, and 37% express concern over agents posing as users. In healthcare, up to 61% report experiencing attacks involving AI agents

5. Five Mitigation Steps
Shrestha outlines five key defenses: (1) inventory AI agents, (2) enforce least privilege, (3) monitor their actions, (4) integrate them into identity governance processes, and (5) establish human oversight—ensuring no agent operates unchecked.

6. Broader Context
This video builds on earlier insights about securing agentic AI, such as monitoring, prompt‑injection protection, and privilege scoping. The core call: treat AI agents like any high-risk insider.

📝 Feedback (7th paragraph):
This adeptly brings attention to a critical and often overlooked risk: AI agents as non‑human insiders. The healthcare case strengthens the urgency, yet adding quantitative data—such as what percentage of enterprises currently enforce least privilege on agents—would provide stronger impact. Explaining how to align these steps with existing frameworks like ISO 27001 or NIST would add practical value. Overall, it raises awareness and offers actionable controls, but would benefit from deeper technical guidance and benchmarks to empower concrete implementation.

Source Help Net security: Why AI agents could be the next insider threat

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Tags: AI Agents, Insider Threat

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Jun 09 2025

Securing Enterprise AI Agents: Managing Access, Identity, and Sensitive Data

Category: AI — disc7 @ 11:29 pm

1. Deploying AI agents in enterprise environments comes with a range of security and safety concerns, particularly when the agents are customized for internal use. These concerns must be addressed thoroughly before allowing such agents to operate in production systems.

2. Take the example of an HR agent handling employee requests. If it has broad access to an HR database, it risks exposing sensitive information — not just for the requesting employee but potentially for others as well. This scenario highlights the importance of data isolation and strict access protocols.

3. To prevent such risks, enterprises must implement fine-grained access controls (FGACs) and role-based access controls (RBACs). These mechanisms ensure that agents only access the data necessary for their specific role, in alignment with security best practices like the principle of least privilege.

4. It’s also essential to follow proper protocols for handling personally identifiable information (PII). This includes compliance with PII transfer regulations and adopting an identity fabric to manage digital identities and enforce secure interactions across systems.

5. In environments where multiple agents interact, secure communication protocols become critical. These protocols must prevent data leaks during inter-agent collaboration and ensure encrypted transmission of sensitive data, in accordance with regulatory standards.

6. Feedback:
This passage effectively outlines the critical need for layered security when deploying AI agents in enterprise contexts. However, it could benefit from specific examples of implementation strategies or frameworks already in use (e.g., Zero Trust Architecture or identity and access management platforms). Additionally, highlighting the consequences of failing to address these concerns (e.g., data breaches, compliance violations) would make the risks more tangible for decision-makers.

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