What is common between octopi and agentic AI? Both can do multiple tasks simultaneously through tentacles that extend in various directions. However, can AI grow back an arm it lost to a predator, as octopi can? This uncomfortable question needs to be asked as the technology is gaining widespread acceptance, and being an emerging technology, has an uncertain threat landscape. One of the clear and present dangers is overprivileges. Because the AI needs to ingest and analyze data from a variety of sources for reasoning, there exists a very thin line between just enough and overprivileges, and both bring their own set of hazards with them. An underprivileged system will not be able to fulfill its duties, and overprivileging will expose organizations to operational, compliance, and reputational risks.
One of the biggest reasons behind the privilege creep is how Agentic AI operates. It observes state across multiple systems, reasons about goals, plans sequences of actions, executes those actions using APIs and credentials, and adapts based on outcomes. An AI agent can read Jira tickets, analyze cloud telemetry, modify IAM policies, redeploy workloads, and notify stakeholders for the final decision. This ability can severely erase the functional gap between that system and a senior administrator with root access. Security teams understand how human privilege creep happens and can manage it through access reviews and off-boarding. This is obviously not the case with agentic AI. In this case, the creep is not just about permissions but about capabilities. It starts with read access to logs for troubleshooting. Then comes ticket creation, then read-only cloud access, then write permissions “just for remediation,” then deployment rights “for resilience.” Each step is rational in isolation. Collectively, they create an entity that can observe, decide, and act across the enterprise stack. Most organizations cannot clearly articulate the maximum authority their AI agent holds today, not in theory but in practice. That lack of clarity is a massive security problem.
Vendor Landscape: Securing Privileges
| Security Control Area | What This Category Addresses | Why It Matters for Agentic AI | Representative Vendors |
| Privileged Access Management (PAM) | High-risk access to infrastructure, cloud, and admin functions | Agentic AI increasingly performs admin-level actions without interactive sessions, bypassing traditional PAM assumptions | CyberArk; BeyondTrust; Delinea |
| Machine Identity & Secrets Management | Non-human credentials, tokens, keys, and certificates | Agentic AI relies on multiple machine identities that quietly accumulate excessive privileges | Akeyless; HashiCorp; Keyfactor |
| Cloud Infrastructure Entitlement Management (CIEM) | Excessive and unintended cloud permissions | AI agents frequently operate across cloud services, making privilege aggregation hard to reason about | Wiz; Sonrai Security; Ermetic |
| Identity Governance & Administration (IGA) | Ownership, access reviews, and accountability | AI agents do not fit cleanly into user or service-account models, breaking governance workflows | SailPoint; Saviynt |
| Cloud-Native Application Protection Platforms (CNAPP) | Runtime risk, permissions, and configuration drift | Agentic AI can amplify the blast radius of a single misjudged action across environments | Palo Alto Networks (Prisma Cloud); Microsoft (Defender for Cloud); Lacework |
| API Security | Abuse of APIs using valid credentials | Agentic AI executes actions primarily through APIs, making misuse look “normal” | Salt Security; Traceable; Cequence |
| Security Policy & Authorization Engines | Fine-grained, context-aware authorization | Needed to constrain what an AI agent is allowed to do, not just who it is | Open Policy Agent; Styra |
| AI Governance & Risk Management (Emerging) | Oversight of autonomous decision-making | Privilege misuse by aligned but misguided AI is a governance failure, not a breach | IBM (AI governance tooling); Microsoft (Responsible AI frameworks) |
Another, and probably the biggest security concern, about agentic AI privileges is not compromise, but misuse through misalignment. A human administrator can understand unwritten aspects like organizational norms, risk tolerance, and informal boundaries. An AI agent does not. It uses the privileges to execute tasks based on objectives and constraints as it understands them, using the privileges. It can still create a disaster while still acting within its scope if those privileges include the ability to modify access controls, rotate credentials, disable services, or redeploy infrastructure. This is a type of threat that current models have no answer for. There is no vector like a malicious insider or an external attacker, and no obvious vulnerability. Instead, there is a high-privilege actor making decisions that are locally rational but globally unsafe. From an incident response perspective, this is deeply problematic. Logs will show authorized actions performed using legitimate credentials. The system behaved as designed. The question of whether it should have been allowed to act that way is a governance failure, not a technical one.
Sanket Kadam, Senior Security Analyst at QKS Group, explains, “Agentic AI privilege risk must be governed through identity, not perimeter controls. By embedding runtime authorization, clear attribution, and lifecycle governance for every agent, CISOs can convert unmanaged automation into accountable, auditable operations while constraining blast radius through least-privilege execution.”
Another danger arising out of the cross-system privilege aggregation is of a magnified blast radius. Agentic AI bridges silos by correlating identity data, infrastructure telemetry, application behavior, and security signals. To do this effectively, it is granted access to each domain. That cross-domain visibility, combined with execution rights, creates an authority profile that no single human role typically holds. In effect, the agent becomes a convergence point for privileges that were intentionally separated to reduce risk. Thus, if an AI agent makes an incorrect decision, the impact is not limited to one system. It can propagate changes across environments at machine speed. A misjudged access revocation can lock out critical services. A poorly reasoned configuration change can expose sensitive assets. A flawed remediation can disable compensating controls. The speed and scale at which these actions occur far exceed those of most human administrators, making detection and rollback significantly harder.
Identity and access management practices are not currently equipped to handle this class of actor. Service accounts are treated as non-interactive and low risk. Privileged access management tools focus on human sessions. Just-in-time access assumes an explicit request and approval flow. Agentic AI operates continuously and implicitly. It does not “log in” in a way that triggers traditional controls, and its actions are often indistinguishable from those of trusted automation. This leaves a visibility gap precisely where privilege risk is highest.
These factors underline the need for CISOs to reframe how they think about AI privileges. The relevant question is not whether the agent is intelligent or reliable, but whether its privilege set is defensible under worst-case assumptions. What happens if the agent’s reasoning is wrong? What happens if it encounters an unexpected state? What happens if it optimizes for availability at the expense of security? These are privilege-driven failure modes, not model quality issues.
The uncomfortable reality is that many enterprises have already created super-privileged AI actors without acknowledging them as such. This is not because security teams are careless, but because existing frameworks were not designed for autonomous, non-human administrators. Until that changes, agentic AI will continue to expand its authority quietly, one at a time. It is better to check your privileges. Because your PAM will not be able to give out a pan-pan call. It will be a straight Mayday situation.