AI and Technology Due Diligence Consulting
Cyberonix is retained for two related but distinct decisions: whether a specific AI system or program is fit for deployment, and whether a software or cloud target is fit for acquisition, investment, or enterprise commitment. The practice spans system-level AI risk assessment, organization-level AI governance, M&A technology due diligence, and focused software and cloud due diligence: different questions, but each one resting on the same evidentiary discipline applied to artifacts the in-house team cannot evaluate impartially. Clients include technology and corporate leadership (Chief Technology Officers, Chief AI Officers, VPs of Engineering), board directors and audit committees, private-equity and venture-capital principals, strategic acquirers, and enterprise customers and partners evaluating a software or cloud commitment. The practice is distinct from Cyberonix’s executive technology advising in that it is risk-bound or transaction-bound rather than an ongoing leadership advisory, and distinct from the firm’s litigation work in deliverable form: written analysis for leadership and investors rather than expert reports for the court. Cyberonix’s AI and technology due diligence engagements rest on the same analytical discipline that underwrites the firm’s litigation record.
Why Cyberonix for AI and Technology Due Diligence
Both AI risk and transaction-stage technology decisions require an outside view that is technically deep enough to read the artifacts (the model, the codebase, the architecture, the operational telemetry), independent enough to be relied on by a board or an investor, and disciplined enough to produce a record that holds up under later scrutiny. The in-house team is technically deep but cannot be independent of the system or practice it is being asked to evaluate. Traditional management consulting is independent but, as a rule, not technically deep enough to read model behavior, audit MLOps infrastructure, or reason about an engineering practice from the artifacts. Cyberonix occupies the gap.
Engagements draw on the same evidentiary standard the firm applies in its litigation work: methodology documented to withstand sophisticated scrutiny, findings tied to the artifacts they rest on, and conclusions traceable to the underlying evidence. The standard is applied to a leadership and investor audience rather than to counsel and the court. What changes is the audience and the form of the deliverable; the analytical discipline does not.
Areas of Practice
Cyberonix’s AI and technology due diligence practice covers four areas of work.
AI Bias, Fairness, and Risk Assessment
System-level evaluation of a specific AI system or model. The work covers fairness metrics and disparate-impact analysis, robustness and adversarial testing, alignment and safety risk, and regulatory exposure under the EU AI Act, the NIST AI Risk Management Framework, and sector-specific frameworks. Used before deployment to support a deploy or do-not-deploy decision, and on an ongoing basis as part of an audit cycle once the system is in production.
AI Systems Governance and Readiness
Organization-level evaluation of AI deployment posture. The work covers AI policy and governance, lifecycle and MLOps maturity, third-party AI tool review, incident response, and regulatory readiness, evaluated as a program rather than system by system. Used by boards, audit committees, and Chief AI Officers establishing a new AI program or maturing an existing one against the obligations a sophisticated audit or regulator would surface.
M&A Technology Due Diligence
Transaction-context technology diligence on acquisition targets, run alongside legal, financial, and commercial diligence streams. The work covers engineering practice, codebase, architecture, scalability, security, technical debt, IP posture, and integration risk. Findings are calibrated to the decisions the transaction turns on: buy or no-buy, valuation, deal terms, and the post-close integration plan.
Software and Cloud Due Diligence
Focused technical diligence on a software or cloud product’s architecture, scalability, reliability, security, and operational posture. Used inside an M&A transaction and equally outside it: vendor evaluation by an enterprise customer, partnership commitment, audit support, and pre-investment review by an investor whose technical question is bounded to a single product rather than a full target.
How We Work
An engagement begins with a scoping conversation that defines the question and identifies the artifacts to be examined. Cyberonix then conducts a targeted review of the relevant material: model weights and evaluation data, codebase, architecture documents, version control history, build and continuous-integration infrastructure, runtime and operational telemetry, third-party assessment records, and regulatory documentation. Members of the target’s or client’s team are interviewed where doing so would sharpen the analysis rather than as a matter of process.
The deliverable is written analysis for leadership and investors, not expert reports or sworn testimony. Engagement length ranges from short focused reads (a single AI system, a single product, a single transaction question) through multi-month diligence spanning a full target or a complete AI program, with interim deliverables and decision points scoped at the start. Where a matter later proceeds to litigation, the analytical record the engagement produced can be carried forward by the firm’s litigation practice rather than redone from the artifacts.
Our Experts
The team is the same group of senior consultants who staff the firm’s litigation work. Each holds a faculty appointment at a research university in the United States, with industry depth spanning AI systems, software architecture, distributed systems, software security, and cloud infrastructure. The team’s standing in the field is reflected in recognitions including IEEE Fellow status, ACM Distinguished Member status, and named professorships at leading research institutions. Engagements are staffed so the lead consultant’s research record and industry background align with the technology under review, with salaried consultants supporting and extending that work.