AgentIQ turns BigID’s rich data graph into an execution surface. Instead of issuing tickets or one-off scripts, teams can ask the agent to investigate a risk, rank it by business impact, and carry out policy-compliant fixes—revoking access, changing retention, quarantining files, or orchestrating DSR fulfillment. The timing lines up with two forces: (1) enterprises adopting LLM assistants as day-to-day interfaces, and (2) the need to tame shadow AI and expanding data estates across cloud, SaaS, and on-prem. By meeting users in their preferred agent surface and embedding approvals and logs, AgentIQ reduces friction without compromising traceability.

AgentIQ can be driven from BigID or external agents in Claude, ChatGPT, Gemini, and Copilot, plus internal enterprise agents. Actions are constrained by the requesting user’s permissions, and guardrails are enforced at the API and MCP layers—not just via prompt instructions—closing a common loophole for over-permissive agents. Every step is logged and attributable to a human. Enterprise controls such as BYOK, telemetry, and air-gap support are in scope. With 200+ integrations covering data at rest, in motion, and in use, AgentIQ’s end-to-end flows include: ranking internet-exposed sensitive data by business risk, revoking public access, mapping AI systems that touch regulated data, flagging policy violations, and removing stale or excessive entitlements.

Week 1: Define two high-yield intents (e.g., remove public access to PII-bearing objects; auto-close DSR across top repositories). Connect read-only integrations, import policies, and map ownership. Week 2: Enable dry-run mode with ChatOps approvals; wire change windows and ITSM tickets; set scoring rules by sensitivity, exposure, and activity. Week 3: Turn on limited-scope write actions for low-risk assets; validate evidence (who approved, when, and what changed) is auditor-ready. Week 4: Expand surface to external agents (Claude/Copilot) for request initiation; add runbooks for rollbacks. Track MTTR, false-positive remediation rate, and exception rates. Outcome: a repeatable pattern for closed-loop policy enforcement with measured risk.

Think of AgentIQ as the agentic counterpart to DSPM findings: it prioritizes with business context and executes changes safely. It does not replace SIEM/SOAR or EDR; rather, it plugs into those pipelines or operates in parallel for data-centric risks. DLP and data classification provide signals; IAM/PAM anchor entitlements; AgentIQ translates intent into constrained actions with audit evidence. The model-agnostic stance—running from Claude, ChatGPT, Gemini, or Copilot—reduces lock-in and lets enterprises standardize on their assistant of choice while keeping BigID as the enforcement and context substrate underneath.

Agent reliability still depends on high-quality data context and well-specified intents. Over-broad scopes or absent approvals can create disruptive changes. Treat initial use cases as controlled change-management events: segment blast radius, require approvals for writes, and keep dry-run on until metrics stabilize. Validate rate limits and backoffs across cloud/SaaS APIs. Ensure legal and audit teams sign off on evidence formats for completed actions and rollbacks. Finally, decide where human-in-the-loop is mandatory (e.g., entitlement revocation for privileged identities) versus safe to automate (e.g., removing public access from non-production buckets with PII).