Artificial Intelligence in Robotics

Artificial intelligence in robotics is the use of learning, perception, and decision systems to make machines behave with more autonomy than fixed automation allows. It combines software intelligence with mechanical execution so robots can interpret sensor data, adapt actions, and operate in changing environments with less manual reprogramming.

Expanded Definition

Artificial intelligence in robotics refers to the decision-making, perception, and adaptation layer that lets a robot respond to its environment without being hardcoded for every condition. In NHI and agentic systems, the term matters because the robot is not only a machine, it is also an execution platform for software agents, sensors, and identity-bound services.

Definitions vary across vendors when robotics is paired with autonomy, computer vision, or agentic AI, but the security distinction is consistent: AI changes how actions are selected, while robotics changes how those actions are carried out in the physical world. That means failures can move from data misuse to physical impact, operational disruption, or unsafe actuation. Identity, authorization, telemetry, and fallback behavior all become part of the security boundary, not just the model itself. For a standards-oriented identity baseline, NIST SP 800-63 Digital Identity Guidelines helps frame assurance, even though it was not written specifically for robots.

The most common misapplication is treating AI-enabled robotics as simple automation, which occurs when teams deploy adaptive systems without re-evaluating access, safety interlocks, and recovery procedures.

Examples and Use Cases

Implementing AI in robotics rigorously often introduces operational uncertainty, requiring organisations to weigh autonomy gains against the cost of tighter monitoring, testing, and identity controls.

• Warehouse robots that use computer vision to detect obstacles, reroute around blocked aisles, and request new task assignments from a central orchestration service.
• Inspection drones that classify anomalies on pipelines or rooftops, then escalate only the highest-confidence findings to human operators for review.
• Manufacturing arms that adjust grip strength or movement speed based on sensor feedback, while calling authenticated tool APIs for job parameters.
• Field robots that rely on edge inference to operate with intermittent connectivity, then sync state and logs when they reconnect to managed services.
• Safety-critical robots that require policy checks before motion commands are executed, especially when a learning system proposes a new route or sequence.

These patterns become more trustworthy when teams align robot access and service authentication with controls from NIST SP 800-63 Digital Identity Guidelines and apply strong secret handling practices.

NHIMG’s analysis of the Schneider Electric credentials breach shows how identity compromise can cascade across connected environments, which is especially relevant when robots inherit credentials through tooling, pipelines, or orchestration layers.

Why It Matters in NHI Security

AI-driven robots widen the NHI attack surface because they often depend on service accounts, API keys, device certificates, orchestration tokens, and model endpoints that must all be authenticated and governed. When those identities are overprivileged or poorly rotated, attackers can influence motion, exfiltrate telemetry, or pivot into adjacent operational systems. NHIMG reports that 80% of identity breaches involved compromised non-human identities such as service accounts and API keys, which is directly relevant when autonomous robots are provisioned with long-lived access.

This is also a Zero Trust problem, because robots cannot be assumed trustworthy simply because they are on an internal network. Command provenance, least privilege, and continuous verification matter as much as model accuracy. Security teams should also map the robot’s supporting services to NIST SP 800-53 Rev 5 Security and Privacy Controls so access, logging, and recovery responsibilities are explicit.

Organisations typically encounter the real consequences only after a robot behaves unexpectedly, at which point AI in robotics becomes operationally unavoidable to address.

Related resources from NHI Mgmt Group

• Artificial Intelligence Management System
• Artificial intelligence system program
• How should security teams use threat intelligence to reduce NHI risk?
• Why do NHIs change the way threat intelligence should be evaluated?