A.A. Litan LLC
Effective Date: March 7, 2026
Policy Owner: Founder & Chief Governance Officer
At A.A. Litan LLC, we believe that the power of advanced automation must be balanced with absolute safety and ethical rigor. This policy governs the use of Large Language Models (LLMs), autonomous AI agents, and automated decision-support systems across our flagship platforms: Cynoculist, DASTA-AI, Aether, Trade_Up, Artic-Pro, and WordofGod.
Our approach is led by our founder, who holds industry-recognized certifications in the responsible, safe, ethical, and secure adoption of AI, ensuring that every line of code and every model deployment is filtered through a lens of high-integrity governance.
We do not deploy technology in a vacuum. Our internal risk management lifecycle is strictly mapped to the NIST AI Risk Management Framework (AI RMF).
We maintain a culture of risk awareness where AI is never "black box" technology.
We proactively identify risks associated with third-party model dependencies.
We conduct rigorous evaluations and "red-teaming" on third-party models, such as those provided by OpenAI, to test for hallucinations, prompt injections, and bias.
We prioritize end-user safety through two operating modes: (1) assistive AI, where outputs are decision-support reviewed by humans before action (DASTA-AI, GRC platform), and (2) supervised autonomy, where AI agents act independently on routine decisions within configurable thresholds while escalating confirmed threats to human analysts (Aether). Every autonomous decision is logged with a full, human-readable audit trail.
The security of customer data is our primary differentiator. We have engineered a proprietary mechanism within Cynoculist, Trade_Up, Artic-Pro, WordofGod, and DASTA-AI that acts as a secure gateway between our users and the LLM. For Aether, a distinct data minimization principle applies: only network indicators (IP addresses, domains, file hashes) extracted from security alerts are transmitted to third-party enrichment services. No user personal data, alert narrative text, or cloud account credentials are transmitted to these services.
Before any request reaches a third-party provider, our system automatically scrubs and de-identifies sensitive data, including PII, credentials, and proprietary technical strings.
For Artic-Pro, browser-native transcription keeps biometric voice data local to your device. No audio files are collected or stored by our servers.
We utilize enterprise-grade API configurations ensuring that customer data is never used by third-party providers to train, retrain, or improve their models. Your data remains your own.
Our Artic-Pro platform is guided by the "Improve Clarity, Not Identity" mandate:
We provide inclusive feedback that remains charitable to all linguistic backgrounds.
We conduct periodic audits to ensure coaching remains objective and reduces model bias.
We maintain a strict boundary between public-facing tools and our internal infrastructure:
Used strictly for processing de-identified technical summaries under our secure API protocols.
All internal model implementations are subject to a formal approval process. No internal model is deployed without an assessment of its safety parameters and alignment with our ethical standards.
We believe in purposeful automation: AI should act autonomously only where speed is safety-critical and within well-defined, configurable boundaries. Human judgment governs all consequential decisions.
No critical security finding, "CyberScore," or executive risk narrative produced by DASTA-AI or the GRC platform is delivered without a Human-in-the-Loop review step. All AI outputs from these platforms are decision-support instruments, not autonomous actions.
Aether operates under a supervised autonomy model. Routine triage (classification, false-positive suppression, and benign verdict closure) is performed autonomously by the AI pipeline within operator-configured thresholds. All confirmed true positives are escalated to designated human analysts with full context and a recommended action before any case is closed. No autonomous action is taken on escalated threats without analyst acknowledgment.
Every autonomous decision made by Aether is logged with a human-readable audit trail including the verdict, the evidence considered, and the confidence score. This trail is available to all authorized users and is designed to satisfy SOC 2, ISO 27001, and NIST audit evidence requirements.
Aether is our most autonomy-intensive product. As an autonomous SOC platform that makes real-time triage decisions on security alerts, it requires dedicated governance controls beyond our standard AI practices.
Operators define escalation thresholds (severity level, confidence score, alert type) that determine which alerts are handled autonomously and which are escalated for human review. No alerts are silently discarded; every verdict is visible in the case management interface.
Every AI triage decision is logged with a timestamp, the evidence considered (enrichment results, MITRE ATT&CK technique mapping, severity score), the verdict rendered, and the routing action taken. This audit trail cannot be deleted by analysts or team leads and is accessible to authorized auditors and compliance officers.
Analysts can mark escalated alerts as false positives within the platform. This feedback is incorporated into per-tenant triage behavior to reduce future noise. All feedback actions are logged and attributable to the analyst who submitted them.
Aether does not take remediation actions (e.g., blocking IPs, quarantining resources, modifying firewall rules) autonomously. All triage verdicts are advisory. Remediation requires explicit human authorization. This boundary is a deliberate governance constraint and is not configurable by operators.
Only isolated network indicators (IP addresses, domains, file hashes) are transmitted to external enrichment services. Alert narrative content, user identities, resource names, and account credentials are never transmitted externally. Enrichment queries are rate-limited and monitored.
The threat landscape for automated systems is constantly evolving. A.A. Litan LLC commits to:
We regularly stress-test our implementations against the OWASP Top 10 for Large Language Models.
We conduct periodic "evals" to ensure that the third-party models we consume continue to meet our high standards for accuracy and safety.
We monitor for "model drift" and bias to ensure our outputs remain fair, objective, and helpful.
This policy is a living document. As the regulatory environment changes, our founder's certified expertise ensures that A.A. Litan LLC remains at the forefront of safe and secure technology adoption.
We are committed to transparency and will continue to improve our processes to ensure we deploy technology safely, securely, ethically, and responsibly.
Inquiries regarding our AI Governance and Risk Assessment can be directed to:
This Responsible AI Policy was last updated on June 9, 2026