AI is now embedded in tools like Microsoft Teams, Zoom, and modern assistants like Anthropic Claude, OpenAI, and Gemini, and usage has moved from pervasive to omnipresent:
As humans interact with AI — and AI agents interact with each other — a new class of communications has emerged: aiComms. These interactions create new behavior patterns with new risk, compliance, and governance challenges. In fact, 88% of organizations already report governance and security challenges adopting AI, including new risks like 53% of AI agents exceeding intended permissions and 47% or organizations experiencing an AI Agent-related security incident.
Guardrails are critical, but alone aren’t enough. Organizations need a purpose-built AI interaction governance layer that normalizes AI communications across AI tools and unifies guardrail alert review; adds contextual investigation for SIEM and observability tools; and continuously monitors human, AI, and guardrail behavior, adding a forensic analysis lens to assess and improve risk detections.
A growing mix of AI platforms and diverse guardrail tools each produce their own content formats, interaction types, and detection alerts, at high volumes with significant depth of content, with no unified view to navigate the content and context across all of them.
The surging volume of AI interaction data overwhelms SIEMs and SOC tools, making it increasingly difficult to investigate alerts and triage risks that actually need attention.
AI interactions between humans, agents, and systems represent an entirely new frontier with no established baseline from which to detect anomalies. While point-in-time detections for known risks such as PCI exposure, jailbreaking, and prompt injection are essential, they are no longer sufficient on their own. New and subtler risks are continuously emerging: covert data inference that inadvertently circumvents guardrails, corrupted outputs where incorrect data is embedded within accurate responses, unsafe data introduced by users into AI systems, and many more.
Guardrail Alerts and AI Content Types Challenge Analysts
Interactions Surpass Guardrail Understanding of Compliance Risks Over Time
AIComms and interaction content from custodians on legal hold are already subject to eDiscovery and legal hold requirements, yet most organizations struggle to fully capture content from AI assistants and summaries.
Retention Compliance and technology teams struggle to manage aiComms data collection and retention rules in a flexible, documented, policy-enforced, and auditable manner. That includes making defensible risk-based decisions on what AI communications are retained and which ones are not in a consistent way. That is critical to maintaining compliance balanced with data protection. In fact, Gartner notes that “Over-retention amplifies cost, risk, and noise, flooding pipelines with low-value artifacts and widening breach and sensitive data exposure.”
Organizations need defensible policy alerts, rules, and disclaimers in communications in real-time, and in the retained records that let users know the presence of AI agents and content, provide rules of usage, set expectations of behavior, and can be proven to have been provided when communication activity is reviewed.
Compliance teams need to detect compliance risks within AI interactions, from promissory and implied performance language to MNPI and beyond, regardless of whether it is the human pushing the guardrails or AI providing the content. Beyond that detection, they increasingly need to find and correlate when incorrect AI interaction content appears in traditional comms channels, such as an email that was sent externally that could trigger, or already has triggered, a compliance incident for the organization.
Teams must also detect emerging and evolving risk behaviors that develop over time, whether from humans adapting how they communicate with AI, or from AI tools adapting to solve problems and generate content. Both sides of this interaction create new risk behaviors at scale, whether well-intentioned or not. Only continuous monitoring with forensic-level analysis can keep pace with these risks as they evolve, enabling teams to identify, alert on, and remediate them effectively.
Theta Lake provides a unified collection, workflow, and investigation layer for AI interactions and content that transforms an organization’s ability to govern AI Communications, enabling organizations to meet evolving regulatory expectations, manage risk across the organization, and future-proof their compliance and security strategy in the era of AI.
Standardized format and normalization across all AI platforms and guardrail tools, with unified investigation views, contextual timeline views, automated multi-party review workflows, and risk summarization across all interactions over time.
Bidirectional alert integrations with SIEM and SOC tools, including SSO and private links with RBAC directly into contextual investigation views.
ISO 42001 and CSA STAR AI Level II certified, safe, and transparent built-in classifiers for detecting AI Interaction risks across Unethical Summary Steering, File Sharing with AI assistants, Indirect Prompt Injection in attachments, Shadow AI, and more. These detections can be combined with highly parametrized compound detection rules, custom classifier, and risk summarization for continuous monitoring.
Certified classifiers identify compliance and conduct risks (e.g., Unethical Summary Steering, sensitive info sharing, jailbreaking, prompt injection), with Gartner-recognized Compound Detection rules for bespoke AI interaction monitoring.
Triage and prioritize Guardrails alerts to cut through noise and reduce alert fatigue, while bringing your own custom classification tools and models to apply against aiComms and workflows.
Full suite of endpoints to automate workflows, extract interactions and enrichment data, integrate custom classification models, and connect with AI guardrail, observability, and security tools.
Investigate Behavior Patterns Over Time.
Review Detections in Specific Interactions.
Automatically collect and apply legal hold to AI summaries and related AI communication content for custodians in legal matters.
Customers can selectively collect and apply dynamic archiving retention rules to decide which aiComms records to keep for how long and in what region, as well as what to explicitly not retain. Includes native audit trail standard compliance as well as WORM with 17a-4 attestation options and the ability to store in any and multiple locations to meet any state or national data sovereignty requirement.
Despite the perception that many AI interactions are “internal,” the reality is that aiComms content is often used in traditional communications such as email and sent externally. Theta Lake can identify risks in AI interaction content before it is used in external communication channels, while also finding where it has been used in traditional communication channels to take corrective action.
Real-time Policy Notifications that insert customizable group and role-based notifications and disclaimers into Microsoft Teams chat and conversations that provide training, links to policies, and disclaimers for AI usage across AI assistants. All notifications become part of the audit history and evidence proof of policy.
Investigate Behavior Patterns Over Time
99% of organizations are expanding their use of AI this year. Yet, 88% cite challenges with governance and security. Join speakers and industry luminaries from SIFMA, RingCentral, Zoom, Webex by Cisco, Metrigy Analyst Group, and Theta Lake as they discuss the impacts on risk of AI in regulated organizations and the need to reimagine security and compliance in an AI-powered workplace. The series kicks off June 16, 2026, 11 am EST.
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