Technology Insights
Analysis and practical guides on technology.
150 articles, checklists, and guides on technology law, written by the attorneys at MC Law. Every piece is general information, not legal advice—for a specific matter, contact the firm.
Running a Platform That Hosts User Content: A Practical Guide
Running a platform that hosts user content is an operational discipline with a legal layer, and most platform liability comes from the platform's own conduct rather than from anything a user posted. This guide covers what to build: terms that match the process actually run, a moderation policy that scales, the copyright infrastructure the intellectual property carve-out requires, notice-and-action mechanisms for other categories, a subpoena response process, transparency data, and the design review that addresses the product-defect claims now driving platform litigation. It includes a build sequence for a new platform, a remediation sequence for an existing one, and the errors that reliably create exposure. A worked scenario follows a takedown demand from intake through resolution and litigation posture. It also covers what the terms of service must say for the defenses to work.
Section 230 and Platform Liability: What the Statute Actually Says and Where It Stops
Section 230 is the most consequential and most misdescribed statute in American technology law. This article explains what it actually provides: two distinct immunities, one for hosting third-party content and one for good-faith moderation, along with four express exceptions that are doing increasing work. It covers the elements courts apply, the material contribution test that determines when a platform becomes a content provider itself, and the product design theories plaintiffs use to plead around the statute. It addresses the Supreme Court's recent decisions, what they decided and what they conspicuously did not, and the First Amendment framework that constrains state regulation of moderation. It closes with practical guidance for platforms that want the protection to hold.
Lookalike Domain Response Checklist: A Practical Checklist
This checklist turns a lookalike domain incident into phased tasks that can be worked from a printed page. It opens with preparation and five-minute triage, then covers the money, evidence capture, containment, communications, and same-day abuse reports. Later phases address escalation to registries and ICANN, insurer and counterparty steps, the choice among the URS, the UDRP, and federal court, and the papers needed for each. A separate phase covers a registrant who was wrongly reported, and a final phase covers lessons learned and defensive work. Each task names the authority or source behind it, with notes on practice and red flags where mistakes tend to happen.
AI Licensing Toolkit: Data Provenance Records, License Terms, and Indemnity Clauses
This toolkit collects the working documents of an artificial intelligence licensing practice: the provenance manifest that makes every other protection possible, the clause library covering training rights, output ownership, indemnity conditions and model survival, the model and corpus inventory that keeps compliance current, and the intake and diligence materials that route deals correctly. Each template is annotated with its purpose, its owner, and the specific failure it prevents. The clauses are drafted from both sides so a negotiator can see the position being taken and the position likely to be taken back. Each template carries drafting notes on the allocation of training-data risk and output ownership. Adapt everything to the model, the data, and the deployment.
AI Licensing Diligence Checklist: A Practical Checklist
Artificial intelligence diligence fails when it asks for representations instead of documents, and when it treats a three-layer asset as a single license. This checklist separates the data, model, and output layers and runs each through the questions that produce usable answers. It covers provenance verification, chain of title, personal information and regulated data, model license restrictions that engineering teams routinely miss, indemnity conditions tested against the actual deployment, and the termination provisions that determine whether a trained model survives. Separate sections cover transaction diligence, vendor intake, and the ongoing inventory that keeps the answers current.
Negotiating an AI Data or Model License: A Practical Guide
Negotiating for artificial intelligence assets fails in predictable ways: the parties license a single undifferentiated thing, the licensor warrants rights it cannot document, and nobody addresses what happens to a trained model when the data license ends. This guide walks through the negotiation in the order that surfaces those problems early. It covers scoping the three layers, running diligence that produces documents rather than assurances, drafting the grant so training, fine-tuning, and evaluation are named separately, negotiating indemnity conditions rather than indemnity grants, and resolving termination before signature. It includes positions for each side, a term sheet, and the escalation points worth walking away over.
Licensing Data and Models for Artificial Intelligence: Training Rights, Outputs, and Indemnities
Licensing for artificial intelligence looks like software licensing and is not, because the asset has three layers that behave differently: the data, the model, and the outputs. This article separates them and explains what each layer's owner can and cannot grant. It covers the rights actually needed to train lawfully, the difference between an open weight release and open source, why output ownership provisions are more contract than copyright, and the indemnity structures that allocate the risks nobody can eliminate. It also addresses provenance documentation, the contractual layer that operates independently of copyright, and the practical questions of retention, deletion, and what happens to a model when the license to its training data ends.
Domain Acquisition Toolkit: Outreach Scripts, Purchase Agreements, and Post-Closing Records
This toolkit collects the working papers for buying a domain name from its current holder, each introduced by purpose, owner, and timing and followed by drafting notes tied to authority. It includes a decision record and intent memo, outreach scripts and answers to the awkward questions, a broker engagement letter, and a seller provenance questionnaire. It also includes a domain purchase agreement with title, no-lien, and no-dispute representations, escrow instructions, and a transfer closing run sheet that accounts for ICANN's current 60-day lock and its adopted reforms. Post-closing papers cover the security record, an internal license from a holding entity, and a closing file index. Every template uses bracketed placeholders and can be adapted to a small purchase or a large one.
Vendor Cybersecurity Diligence Checklist: A Practical Checklist
Most significant data breaches now arrive through a vendor, and most vendor security reviews are a questionnaire nobody reads attached to a contract nobody negotiated. The controls that actually matter are a small set: knowing what data the vendor receives, tiering the review to the risk, obtaining independent assurance rather than self-attestation, and negotiating a handful of contract terms that determine who bears the loss. This checklist covers the sequence: inventory and tiering, the diligence appropriate to each tier, the technical and organizational questions worth asking, the subprocessor and data location analysis, the contract provisions that allocate breach cost and preserve audit and exit rights, and the ongoing monitoring and offboarding that most programs skip entirely. It closes with the ongoing monitoring and offboarding steps that most programs skip, including the deletion certification that determines whether the data actually goes away.
The CAN-SPAM Act: A Comprehensive Guide for Businesses and Marketers
The CAN-SPAM Act of 2003 (15 U.S.C. 7701-7713) and the FTC's implementing rule (16 C.F.R. Part 316) set the federal ground rules for commercial email, regulating all commercial messages rather than only bulk spam. This guide explains the statute's scope through the "primary purpose" test that distinguishes commercial messages from exempt transactional or relationship messages, walks through the seven core compliance requirements, and details who counts as an "initiator" versus a "sender" and how liability is shared across advertisers, vendors, and affiliates. It covers enforcement by the FTC, sector regulators, state attorneys general, and internet service providers, including the inflation-adjusted civil penalty of roughly $51,744 or more per offending email and the criminal penalties under 18 U.S.C. 1037 for fraud-based conduct. The article addresses the absence of any private right of action, the statute's broad preemption of state email laws under 15 U.S.C. 7707, and how CAN-SPAM interacts with the TCPA for text messages and with stricter international regimes such as the GDPR and Canada's CASL. It closes with a practical compliance checklist and an FAQ for businesses and marketers.
Cybersecurity Regulatory Compliance Checklist: A Practical Checklist
This checklist turns the overlapping cybersecurity rules that apply to American businesses into concrete, checkable tasks. It starts with applicability and scope, then works through governance, the written risk assessment, the written program and policies, core technical controls, vendor oversight, training and testing, and incident response with every regulatory reporting clock. A regime-by-regime section covers the FTC Safeguards Rule, NYDFS Part 500, the SEC's disclosure rules and Regulation S-P, HIPAA and the Health Breach Notification Rule, defense contractor requirements, state laws, insurance, telecommunications, and critical infrastructure. It closes with board reporting and certifications, examination readiness, an annual compliance calendar, common mistakes, an FAQ, and practical takeaways. Each item cites the governing rule so that compliance teams, counsel, auditors, and examiners can trace it to its source.
Failed Software Implementations: Contract, Fraud, and Warranty Claims When an IT Project Goes Wrong
When an ERP, CRM, or other enterprise software project runs years late and millions over budget, the customer and the integrator usually end up arguing about the same short list of questions: which body of law governs, whether the breach was material, what the warranties and remedy limits actually say, and whether a fraud claim can escape the liability cap. This article explains each question in plain English and then gives the practitioner detail, using the cases that shaped the field, including Advent Systems v. Unisys, State of Indiana v. IBM, Metropolitan Life v. Noble Lowndes, the Hertz v. Accenture rulings, and the economic loss cases from Michigan, Wisconsin, California, and Florida. It walks through the anatomy of a failed project from the sales pitch to the termination letter and shows which documents at each stage become evidence. It covers the UCC predominant purpose test, express and implied warranties and their disclaimers, failure of essential purpose, the direct-versus-consequential damages fight over re-implementation costs, gross negligence and willful misconduct carve-outs, integration and non-reliance clauses, and the rarely successful theory of IT professional negligence. It also addresses exit issues, including transition assistance, source code escrow, data return, and ownership of custom code, along with damages proof, project records, experts, limitations periods, and arbitration. An extended worked example, common mistakes, an FAQ, and practical takeaways for both customers and vendors close the article.