Responsible AI
Audit, security, red-teaming, and governance, in one practice
Responsible AI is the fourth pillar of Phygitalytics (Physical × Digital × Analytics × Responsible AI): the governance layer that keeps a GenAI system accountable in production, not just in a policy document. This page pulls together everything on AI audit, AI security, red-teaming, evaluation tooling, governance, and training in one place.
AI Model Intelligence & Assurance
Know what your AI can do, where it fails, and whether it is ready for production
Phygitalytics provides AI Model Intelligence & Assurance to help enterprises evaluate, compare, secure, and confidently deploy AI models and agents. We go beyond standard benchmarks by testing AI against domain-specific positive, negative, boundary, and adversarial scenarios—evaluating reasoning, accuracy, hallucination, security, value stability, tool usage, agent behavior, and runtime containment. From model selection and RAG evaluation to AI red teaming, agent security, and continuous assurance, we help organizations understand what their AI can do, where it fails, how it behaves under pressure, and whether it is ready for production.
Standardized Evaluation Pipeline
Breadth · Comparability · Robust metrics
Step 1
Generating test cases
InBehaviorsAttackOutTest casesStep 2
Generating completions
InTest casesModel + DefenseOutCompletionsStep 3
Evaluating completions
InBehaviors+CompletionsClassifierLLM-based / Hash-basedOutSuccess rate
AI audit · co-authored research
Most AI audit frameworks stop at principles. STARED goes to the function level.
AuditOne · AI Auditing Litepaper · v1.1, 2024
AI adoption is outrunning oversight: 83% of ML professionals say it's hard to spot bias in their own systems, 77% of businesses reported an AI-related breach in the prior year (HiddenLayer, 2024), and more than 80% of AI projects fail, twice the rate of non-AI IT projects (RAND, 2024). Existing frameworks (NIST, ISO 42001, GDPR, OECD) set useful principles but rarely reach the technical depth needed to see how a model actually behaves in production.
AuditOne's STARED framework closes that gap, auditing AI across the data, model, and output layers for Security, Technical Assessment, Regulatory Compliance, Ethics, and Data Governance. I co-authored it alongside Tobias S. Potthoff, Hubert Jackowski, and Gayathri Devi B R.
AI Auditing Litepaper: the STARED Framework
PDF · 14 pages · 2.4 MB
AI security
The real-world risks that show up in production
Hands-on AI security workshops cover prompt injection, data leakage, and hallucination control, drawn from work including a recent AI security workshop delivered for one of the largest healthcare organizations in the US.
Prompt injection
Direct and indirect injection through user input, retrieved documents, and tool outputs, tested before it shows up in production.
Data leakage
PII and proprietary data exposure through prompts, context windows, logs, and model outputs.
Hallucination control
Grounding, citation, and confidence checks so an ungrounded answer gets caught before a customer sees it.
The same AI security, governance, and observability challenges apply whether the AI touches passenger and operational systems, engineering teams need AI risk training, or an existing AI-driven workflow needs observability and control added after the fact.
Exploring similar risks across your platforms? Let's walk through how this could be structured for your team.
Book a free call →Red-teaming
Attack the system before a user, an auditor, or an attacker does
Red-teaming built into the delivery pipeline, not bolted on before an audit: adversarial prompt testing, bias and fairness probes, and automated attack simulation run against the actual system, not a generic benchmark.
Adversarial prompt testing
Structured jailbreak and injection attempts run against the actual system prompt and guardrails, not a generic benchmark.
Bias & fairness probes
Targeted testing across protected attributes and edge-case demographics to surface skew before an auditor or regulator does.
Automated attack simulation
PyRIT-driven adversarial simulation layered on top of manual red-teaming for coverage at scale.
Findings, not just a report
Every finding maps to a fix: a guardrail, a prompt change, or a monitoring rule, so the exercise changes the system, not just the risk register.
Tools & evaluation
The Responsible AI toolchain
Model and RAG evaluation, guardrail benchmarking, and red-teaming, run with the same tools used to ship production GenAI systems.
Giskard
Vulnerability scanning and bias/robustness testing for ML and LLM models.
PyRIT
Microsoft's Python Risk Identification Toolkit for automated red-teaming of generative AI systems.
Promptfoo
Prompt and RAG evaluation, regression testing, and red-teaming for LLM applications.
Guardrails-AI
Structured output validation and runtime guardrails against hallucination and unsafe content.
LLM Guard
Input/output scanning for prompt injection, PII leakage, and toxic content in production.
Governance · global recognition
Input published in the UN Global Dialogue on AI Governance
Phygitalytics' submission (Asia & Pacific, Private Sector, Respondent 665) was published among the written submissions to the UN Global Dialogue on AI Governance, established under General Assembly Resolution 79/325.
“Capability commoditises. Control compounds. Governance is a runtime, not a policy document: guardrails, observability, evaluation pipelines, and tested kill switches, auditable in code, not just in committee minutes.”
Priorities highlighted: safe, secure & trustworthy AI · transparency, accountability & human oversight · interoperability of governance approaches · AI capacity-building.
Read the Phygitalytics submission (PDF) →Courses
Learn it directly: Responsible AI & AI security on Udemy
Self-paced courses distilled from 178+ live training sessions and hands-on advisory engagements, covering the same audit, security, and governance ground as this page.
GenAI and Responsible AI for Leaders
Strategic Leadership in the AI Era: Security, Risk, and Innovation
Rating: 4.6 out of 5 (3 ratings)
View on Udemy →GenAI and AI Security – Frameworks and Best Practices
Driving Enterprise GenAI Adoption: Tools, Frameworks, and Real-World Case Studies from Industry Leaders
Rating: 4.3 out of 5 (1,246 learners)
View on Udemy →Bring audit-grade Responsible AI to your build
AI audit, security, red-teaming, evaluation, and governance, scoped to your team and sector.