Lantern achieves SOC2 Type 1 compliance in 8 weeks

Lantern, the AI-native ERP software company behind LanternBRP, announced on July 17, 2026, that it has achieved SOC2 Type 1 compliance. The certification was completed in just 8 weeks without a dedicated security department or formal compliance team, validating the platform's enterprise-grade security for mid-market and multi-entity businesses.

SOC2 is a security framework developed by the American Institute of Certified Public Accountants (AICPA) that evaluates how companies manage customer data based on principles like security, availability, and confidentiality. For software vendors, this compliance is a baseline expectation for procurement.

Audit Process and Controls

The certification process evaluated 198 individual tests and controls spanning the platform and the company. Lantern utilized the Secureframe compliance automation platform to manage the process. The audit was conducted by Prescient Security.

Audit Component	Details
Duration	8 weeks
Tests and Controls	198
Management Platform	Secureframe
Auditor	Prescient Security

Operational Efficiency

Lantern completed the audit without a dedicated security engineer, relying on existing operational discipline within its engineering and development teams. The company attributed the speed of the audit to having the majority of controls already in place, rather than building them specifically for the audit. Roughly 4 weeks were dedicated to initial setup and control mapping, followed by 4 weeks of auditing.

"We didn't aim to break a speed record, our goal from the start was to do things the right way. The practices that ensure a smooth audit, strong access controls, effective monitoring, and disciplined change management, are the same ones that build customer trust," said Ady Das, Co-CEO/CIO of Lantern.

Future Compliance Goals

The SOC2 Type 1 certification adds to Lantern's enterprise credentials as it scales its modular business resource planning platform across sectors such as manufacturing, field services, and agriculture. The company stated it is now headed towards Type 2 compliance.

Lantern Pharma Inc. has launched ZetaOmics, a computational-biology module within its withZeta.ai platform that functions as an autonomous "Computational Biologist." The new tool is designed to perform end-to-end bioinformatics and multi-omic analysis across any cancer type, with a specific focus on rare and pediatric cancers that often lack dedicated computational resources. By accessing a database of more than 615,000 curated cancer samples across eleven data sources, ZetaOmics aims to accelerate research by autonomously designing experiments and building rigorous cohorts in minutes rather than weeks.

First introduced as part of the withZeta.ai development roadmap unveiled on May 7, 2026, ZetaOmics is now entering an early-access program for select academic and industry bioinformatics teams as well as Lantern Pharma collaborators. This phased rollout is intended to validate the module against demanding research workflows and establish reference relationships ahead of broader commercial availability. The platform distinguishes itself by embedding domain intelligence directly into its tools, allowing it to reason about experimental design and prevent statistical pitfalls that can lead to false discoveries, rather than simply automating existing software routines.

Technical Capabilities

The ZetaOmics module incorporates fourteen distinct tools equipped with embedded domain intelligence to ensure rigorous analysis. These tools enable capabilities ranging from federated search across vast datasets to cohort building and survival modeling. The system is designed to recognize flaws in experimental design—such as incompatible data pipelines or confounding variables—and decline to run analyses that would produce invalid results, instead suggesting corrections.

Module	What Sets It Apart	Autonomous Capability
Discovery	Ask in plain English	Federated search across 615K+ samples and 11 sources; auto-resolves cancer-type naming and routes only to datasets that hold the modality you need.
Gene Intelligence	Genome to proteome in one query	Links gene, transcript, and protein across Ensembl, UniProt, OMIM, and Orphanet, with pre-computed co-expression networks and rare-disease associations.
Batch Guardian	Stops false discoveries before they start	Profiles cohorts for hidden confounders and mixed pipelines before any analysis runs, and auto-matches anatomically correct normal controls.
Cohort Builder	Describe it; get an analysis-ready group or cluster	Turns plain-language filters into sample groups with diagnostic feedback — if a filter finds nothing, it returns the valid values that do exist.
Expression	The right normalization, chosen for you	Auto-selects normalization by task, scores curated biological signatures, resolves canonical pathways, and validates RNA against protein evidence.
Mutations	Not all variants are equal	Consequence-aware stratification (true loss-of-function vs. benign missense) and copy-number analysis with druggability context; live cBioPortal access.
Drug Response	The full statistical story	Resolves drug synonyms across ChEMBL/PubChem, checks data coverage before comparing, and reports effect size, confidence intervals, power, and FDR — not just p-values.
Differential Expression	Rigor enforced automatically	Adaptively selects edgeR, DESeq2, or limma-trend by sample size, refuses cross-pipeline and cross-specimen comparisons by default, and returns pathway enrichment with caveats surfaced.
Survival	Beyond a median split	Covariate-adjusted Cox modeling with hazard ratios, confidence intervals, and proportional-hazards diagnostics — flagging unadjusted associations as potentially confounded.
Stratification	Test any molecular hypothesis	Splits cohorts by expression, gene ratios, curated signatures, clinical metadata, or drug/CRISPR response into analysis-ready groups for downstream testing.
Protein Triage	Is the target actually druggable?	Ranks druggable, tumor-restricted targets against normal-tissue baselines — and for rare cancers with no prognostic panel, pivots automatically to plasma-based biomarker discovery.
Analysis Sandbox	Bespoke tests, no code required	Describe a custom analysis in plain language; the agent generates and runs sandboxed code against stored results, routing to guarded purpose-built tools whenever they apply.
Visualization	Publication-quality, every time	Thirteen presets (volcano, Kaplan-Meier, drug waterfall, dependency bars, concordance, PCA/UMAP and others) rendered deterministically so every figure matches the reported numbers.
Investigation Memory	Never rebuild from scratch	Stores, versions, and traces every result across turns, so complex multi-step workflows checkpoint their work and stack more analysis than any single run could survive.

Strategic Implications

Panna Sharma, President and Chief Executive Officer of Lantern Pharma and Founder of withZeta.ai, stated that the platform encodes the judgment of expert bioinformaticians to make scientific rigor the default for researchers. The company expects ZetaOmics to drive value through three primary avenues: expanding into new markets by serving all cancer types, creating new collaboration and partnership opportunities through the early-access program, and generating new subscription revenue potential. The module is positioned to strengthen withZeta.ai as both a productivity engine for Lantern Pharma’s internal drug-discovery pipeline and a standalone commercial platform for the global oncology research community.