Identity and access management controls who can access which data and systems by verifying identities, assigning permissions, and keeping audit logs.
Traditional audit trails often fail to track AI interactions, making it challenging to log what data was accessed, by whom, and how it was synthesized.
Privilege creep, shadow identities, and AI oversharing are exacerbated as GenAI becomes integrated into enterprise workflows.
GenAI challenges traditional Identity and access management models by introducing AI agents that perform autonomous tasks and operate outside conventional controls.
Modern Identity and access management requires adaptive controls such as just-in-time access, sensitivity-driven role scopes, behavioral authentication, AI prompt logging, and usage-based risk scoring.
Identity and access management (IAM) is no longer considered just a backend compliance function but also a strategic defense layer. It focuses on three core pillars: authentication, authorization, and auditing. These mechanisms cover who can access what, under which conditions, and how that access is recorded and reviewed.
The first pillar, Authentication, ensures that the identity of a user, device, or application is verified before access is granted. For this purpose, passwords, biometric verification, multi-factor authentication (MFA), and cryptographic keys are commonly used. In the GenAI era, however, traditional authentication is under pressure. As AI agents start performing tasks on behalf of users, the credentials may be shared or integrated into workflows as direct login events. In early 2024, McKinsey found that 65% of organizations were already utilizing generative AI, nearly double the rate from the previous year.
Authorization deals with defining what resources an authenticated identity can access. It involves assigning roles, policies, and permissions. In traditional systems, access was often binary and role-based. However, the GenAI shift complicates this model. AI tools, especially those integrated into productivity, can access corporate data without triggering traditional authorization barriers. Unlike human users, these models don’t just request individual files; they can semantically query, infer, and generate composite answers based on multiple documents.
Auditing, the third pillar, ensures that every access event is logged and traceable. It is essential for incident response and continuous risk assessment. In enterprise IAM, logs capture discrete user actions, such as opening a file, editing a record, and access being denied. But AI agents operate differently. They don’t just access one file; they traverse vectors, embed results, and aggregate summaries from data repositories. These interactions may not be fully captured by conventional auditing mechanisms used in legacy IAM systems.
Many IAM threats stem from overextended access, hidden machine identities, and GenAI's tendency to reveal more than intended, each challenging the principle of least privilege in new ways.
Organizations often assign roles broadly to speed up provisioning. Over time, these roles accumulate extra permissions that are no longer needed. These outdated privileges create risk, as malicious insiders or compromised accounts can access far more systems than intended. IAM best practices explain that privilege creep occurs when new permissions are added but old permissions are never removed, such that users retain access beyond their duties. In a 2024 survey, only 46% of surveyed IT leaders considered their enterprise IAM platforms “very” or “highly effective” for handling user access provisioning, lifecycle, and termination processes. This reflects a confidence gap in IAM systems’ ability to manage user privileges at scale, especially during onboarding, role changes, and offboarding.
The modern infrastructure uses countless machine and API identities. These identities are often uncontrolled and unlogged, representing shadow identities. They range from bot accounts to hardcoded API tokens. When these credentials are shared via code repositories, the audit trail disappears. Academic studies found that public GitHub repos had over 6 million exposed secrets in 2021, double the previous year. Those tokens grant unchecked entry unless monitored. GitHub warns developers, “Never hardcode authentication credentials like tokens, keys, or app‑related secrets into your code.”
Generative AI tools introduce a new data risk. They operate with broad read privileges and no context-aware restrictions. A developer using GitHub Copilot or an enterprise version of Microsoft Copilot might request code snippets or summaries. If sensitive data exists in code or other documentation (e.g., board minutes), AI may reveal it. This is AI oversharing, where a tool generates sensitive output because it aggregates data across sites of permissible access.
GenAI tools utilize semantic search to locate relevant information across various data repositories. They bypass traditional keyword-based access paths. This means an AI agent could access content stored in silos without triggering standard IAM controls. Combining semantic search with RAG requires tight access controls to ensure AI only uses pre-approved sources. Without such guardrails, GenAI can surface sensitive content across silos, even if no single file is fully exposed.
Unlike users, LLMs infer and synthesize information by combining data from many files. LLMs operate at the semantic layer, composing answers from fragments rather than logging direct file access, leaving security teams blind to how sensitive data is exposed. Studies have shown that a significant share of enterprise AI prompts include sensitive or compliance-relevant content. Because these interactions are processed outside traditional enterprise IAM controls, inference often occurs without visibility or logging, leaving auditing blind spots.
Prompt chaining, where one AI response feeds into the next, can elevate access in ways IAM didn’t intend. A low-level prompt might fetch benign data. The following prompt asks the AI to derive deeper insights. This multi-stage inference can expose privileged insights that were never explicitly requested or granted. Research confirms that chained prompts increase model depth and abstraction. In effect, attackers could manipulate innocuous prompts to mine sensitive assets, sidestepping IAM-defined privileges.
Aggregated AI outputs create tangled data lineage issues. Regulators, such as GDPR, require organizations to maintain logs of “who accessed what, when, and how.” Yet when AI interpolates snippets from multiple documents, auditors cannot trace exactly whose identity “viewed” each piece of the data. OWASP identifies prompt injection as a top vulnerability in LLMs. Without detailed AI prompt logging and data lineage, demonstrating compliance becomes nearly impossible, even if IAM shows no misconfiguration.
As digital infrastructure grows increasingly complex, identity is becoming the new security perimeter for GenAI. Establishing clear IAM practices is critical to prevent unintended access, data exposure, and governance breakdowns.
Static role assignments quickly accumulate permissions that users no longer need. Moving to dynamic group membership and just-in-time privilege reduces this risk. Relation-Based Access Control (ReBAC) assigns permissions based on dynamic context, such as team membership or current project role. This model enables tighter enforcement of least-privilege principles and ensures stale access is revoked automatically. As IAM matures, adopting context-sensitive models like ReBAC helps enterprises handle fluid roles and GenAI applications.
Labeling data by sensitivity (public, internal, secret) is common. However, AI requires these labels to be integrated into its authorization logic. When a GenAI prompt is issued, the system should filter source material using these labels. If "secret" is outside the user’s scope, the AI must not ingest or include it in responses. This alignment between data classification and access enforcement closes one of the most significant gaps in GenAI-era IAM.
Authentication isn’t a one-off. For AI interactions, systems must analyze device posture, location, and behavior before each access is made. This Active Authentication model can identify anomalies, such as a developer fetching board-level data without MFA or from an unusual IP address. Enabling conditional access helps prevent AI from being used as an attack vector when credentials are compromised.
To trace “who saw what,” every AI prompt and response must be logged with metadata: user identity, timestamp, prompt text, response content, and source lineage. These logs become part of the IAM audit trail. They support compliance and enable the detection of policy violations and anomalous access patterns. Without such logging, AI becomes an invisible backchannel for data leaks. However, integrating prompt and response logging into existing infrastructure is not a frictionless process. Enterprises often rely on SIEM platforms, which are optimized for conventional logs, not AI interactions. Capturing full context, including input prompts, generated outputs, and data lineage, requires schema extensions and possibly new ingestion pipelines.
Human review of permissions doesn't scale. Automated systems should evaluate who has access to what, how often they use it, and what level of risk it presents. Users with access but no usage for months should be flagged. Those who frequently interact with sensitive data must be reviewed more often. Combining usage telemetry with risk metrics ensures timely revocation of stale rights and focuses review cycles on high-risk roles.
Knostic enhances IAM by interpreting how data is accessed and used continuously, not just by its original label. It creates a dynamic knowledge graph that considers organizational risk, user roles, and document relationships to surface hidden exposures, such as internal documents being surfaced through AI prompts.
The platform converts red-team-style threat testing into routine practice by simulating prompts across tools like Copilot and Glean. These automated tests help identify when AI interactions subvert IAM controls through inference-based privilege escalation.
Knostic also provides audit verification, logging every retrieved document, policy evaluation, and AI output with context. This supports compliance audits under GDPR, HIPAA, and the EU AI Act, adding crucial explainability to otherwise opaque generative systems.
In addition, rather than relying on real-time enforcement, the platform feeds violation data into governance systems to enhance IAM policies, sensitivity labels, and conditional AI access control. Over time, this feedback loop sharpens identity governance to reflect AI behavior, reducing risk while preserving productivity.
Explore more about how Knostic strengthens your existing stack at knostic.ai/roles/iam.
To see how Knostic integrates with Microsoft 365, Copilot, and other enterprise systems to secure AI content, download the full Knostic LLM Data Governance White Paper. It offers practical architectures and compliance frameworks to help you operationalize responsible GenAI deployment.
IAM controls access to source files. DLP stops outbound data sharing at the file or message level. But neither governs how AI assembles answers.
Track reductions in AI oversharing incidents, tighter alignment between user roles and content inferred through GenAI tools, and less manual intervention during access reviews due to better automation and real-time policy enforcement.
Quarterly reviews are a minimum. But Knostic enables event-driven micro-certifications triggered by anomalies, such as frequent access to sensitive summaries by non-executive users. This ensures that access governance adapts in real-time to the behaviors of AI.