Pranav Bhave
Cloud security × AI-assurance research. I build research software that exposes
what an AI-assurance claim establishes, what it assumes, and where it must stop.
In ninety seconds
Penn State CS ’26 (Cybersecurity minor), AWS CCP + AI
Practitioner. My research question: what do composed AI guardrails actually guarantee
when their joint behavior was never measured? I maintain
the Missing Column Census — twenty public
guardrail evaluations in a bounded, single-reviewer inventory; five provide
heterogeneous joint-evidence artifacts — four print a composition result and two release aligned per-item outcomes, with one artifact doing both —
and the mathematics beneath it,
CC-Framework, which
answers with Fréchet–Hoeffding bounds and claim governance (the
five-minute case study).
At Penn State's S2 Lab I build
Ghost-Ark, a
verifier for the provenance limits of AI-governance receipts. Every claim on my site
carries its evidence or says it can't — the
evidence ledger has the bindings.
Research
Aug — Dec 2025 · Pennsylvania State University
Independent research — LLM safety guardrail composition (IST 496)
Supervised by Dr. Peng Liu. Reinforcement, interference, and dependence-driven failure in
composed LLM safety systems using probabilistic bounds and copula-family reasoning.
CC-Framework is the primary deliverable, with theorem notes, research memos, and
architecture documentation.
Jan — May 2025 · Pennsylvania State University
Research assistant — logic & verification tooling
Python CNF-conversion tooling for SAT-solving and logical-verification workflows;
evaluation of LLM-assisted formalization of natural-language logic — translation
reliability, ambiguity handling, verification-readiness.
Selected projects
2026 · Python, evidence engineering, primary-source research
The Missing Column Census — a falsifiable field intervention
- A source-bound, bounded single-reviewer inventory of public guardrail evaluations:
inclusion wording locked in repository history, 20 artifacts examined against primary
sources, every row carrying passages, classification, and correction history. Corrected
2026-08-30 while retaining the 2026-08-27 cutoff: 14 document a shared item set and
common event definition (the stricter ladder is 14 / 12 / 0); 5 provide heterogeneous
joint-evidence artifacts — four print a composition result and two release aligned
per-item outcomes, with one artifact doing both.
- The headline is recomputed from the census file in CI and registered as claim
MC-001 with a fixed falsifier — the marketing claim is itself an executable,
correctable object. Ships with a tested reference implementation of the proposed
Minimum Joint Guardrail Disclosure.
2025 — present · Python, statistics, reproducibility
CC-Framework — dependence-aware AI assurance
- Treats composed-guardrail failure as partial identification: computes sharp
Fréchet–Hoeffding bounds when rail marginals are known but joint dependence is not.
- Wraps results in claim envelopes, evidence roles, Merkle-logged receipts, and decay
semantics; maintains an explicit claim-boundary manifest and non-claims.
In development · S2 Lab, Penn State · AWS, TypeScript
Ghost-Ark — provenance limits of AI-governance receipts
- Verifier and measurement harness testing how far receipts identify executions —
canonicalization kernels, collision corpora, and mechanically enforced non-claims.
Early stage · AWS Nitro Enclaves
Assay — enclave-attested media processing (private)
- Investigating which provenance claims a verifier can and cannot derive from Nitro
Enclave attestation.
2026 · React/Vite · security ML
Ghost Visualizer · GCE · security-ML experiments
- Seven-scene visual essay on why isolated guardrail scores mislead; computes bounds,
endpoint witnesses, and receipts from deterministic rows.
- Intrusion-detection and ML-backdoor evaluation experiments: attack-success-rate and
false-negative-risk analysis.
Education & certifications
May 2026 · University Park, PA
Pennsylvania State University — B.S. Computer Science, minor in Cybersecurity
Coursework: computer security, operating systems, data structures, algorithms,
statistical inference, theory of computation.
Certified · in progress
AWS Certified Cloud Practitioner · AWS Certified AI Practitioner
SAA-C03 and Security — Specialty in progress. Verification IDs available on request.
Skills
- Cloud / AWS
- IAM, S3, VPC, EC2, CloudWatch, Lambda, API Gateway, AWS Budgets, Bedrock-oriented architecture, Terraform basics, Nitro Enclaves (research)
- AI / ML security
- Guardrail composition, safety-stack evaluation, threat modeling, privacy auditing, intrusion detection, backdoor evaluation, ASR analysis
- Programming
- Python, Java, C/C++, SQL, JavaScript/TypeScript; NumPy, Pandas, Scikit-learn, TensorFlow
- Practice
- Reproducible experiments, claim governance, technical writing, Git, Linux, React, Node.js
Download this résumé as a
PDF — printed from this page, so it says exactly what the page says. A phone number is
deliberately omitted from both; email is the contact route. Biographical and project-history
claims here are owner-attested unless linked to a public artifact; see the
evidence ledger for what that means.