Today’s global economy runs on digital data, but the United States still has no single comprehensive national consumer privacy law as of July 2026, leaving companies to govern information through a mix of state privacy statutes, sector-specific federal rules, and global compliance obligations. That absence is not merely a legal curiosity. It has become an operational tax on every company trying to use cloud platforms, AI systems, predictive analytics, and customer data at scale. The argument made by Iron Mountain general counsel Michelle Altamura in TechRadar Pro is therefore bigger than privacy compliance: data governance is now a resilience strategy.
For years, American companies treated privacy as a matter for lawyers, auditors, and breach-response teams. The business collected the data, product teams found ways to use it, and compliance departments tried to make the paperwork line up afterward. That model was tolerable when data lived in relatively stable systems of record and the worst governance failure was an embarrassing consent banner or a delayed deletion request.
It is much less tolerable in 2026. Data now moves through SaaS platforms, customer analytics suites, AI training pipelines, outsourced processors, cloud data lakes, employee productivity tools, and automated decision systems. A company can no longer pretend that privacy is a perimeter issue, because there is no stable perimeter around the modern information estate.
Altamura’s core point is that organizations should stop waiting for a perfect federal privacy law and instead manage data to the most rigorous standard they face. That “highest common denominator” approach sounds conservative, but in practice it is often the only scalable one. A multinational business cannot realistically run one privacy model for California, another for Colorado, another for Europe, another for health data, and another for financial records without creating precisely the complexity that attackers, auditors, and litigators exploit.
The irony is that America’s fragmented privacy regime has forced serious companies toward a more mature posture than a weak national law might have required. The best-run organizations are not asking, “What is the least we can do in this jurisdiction?” They are asking, “What control framework lets us operate globally without re-engineering trust state by state?”
Models and analytics systems do not merely store bad information. They amplify it. Poorly classified records, stale customer profiles, duplicate files, unsecured exports, and forgotten repositories can all become inputs into automated systems that produce confident, wrong, or legally sensitive outputs. In that sense, data quality is no longer just an efficiency concern. It is a control surface.
That is why Altamura’s emphasis on redundant, obsolete, and trivial data matters. The industry shorthand, ROT, can sound like a records-management nuisance, but it is really a resilience problem. Every unnecessary dataset increases storage cost, search noise, discovery exposure, breach impact, and the odds that an AI system will draw from material nobody has validated in years.
The lesson for Windows-heavy enterprises is especially blunt. Most corporate environments still contain decades of SharePoint sites, file shares, Exchange archives, OneDrive folders, exported spreadsheets, Teams chats, SQL databases, and endpoint remnants. Before an organization can responsibly plug Microsoft 365 Copilot, Azure AI, or any analytics platform into that estate, it must know what data exists, who owns it, how long it should live, and whether it should be used at all.
If users believe a service is careless, invasive, or opaque, they withhold information, provide false details, decline permissions, abandon accounts, or move to competitors. That makes the company’s data worse, which makes its personalization, analytics, fraud detection, and AI outputs worse. Privacy failure therefore degrades not just reputation, but the quality of the business’s own intelligence.
This is where “privacy by design” stops being a slogan. A company that embeds governance into product development, vendor management, retention schedules, access control, and AI deployment is not slowing innovation. It is protecting the integrity of the information innovation depends on.
The companies that understand this will treat trust as infrastructure. They will measure whether data is accurate, permissioned, current, minimized, classified, and explainable before using it to automate decisions. They will also recognize that customers, employees, and partners increasingly judge organizations by how responsibly they handle information, not merely by whether they avoid fines.
Businesses want uniformity because uniformity reduces compliance cost and operational uncertainty. Privacy advocates and some state officials resist broad preemption because a weak federal law could flatten stronger protections already enacted in states such as California. Both positions have merit, which is why the stalemate persists.
For executives, the practical consequence is clear: waiting for Washington is not a strategy. A federal law may eventually arrive, but it may not resemble the current draft, it may face legal and political challenges, and it may still leave sector-specific obligations intact. HIPAA, GLBA, children’s privacy rules, state breach notification laws, contract requirements, international transfer rules, and industry standards are not going away simply because Congress passes a baseline privacy statute.
This is why governance has moved upward. Boards and executive teams increasingly see data as a balance-sheet asset that can become a liability overnight. The same customer dataset that powers personalization can become a breach notification nightmare. The same document archive that supports institutional memory can become a discovery burden. The same AI initiative that promises productivity can expose sensitive data if access controls and retention policies are weak.
When product teams cannot tell whether a data use is allowed across markets, they slow down. When engineers must build bespoke workflows for every jurisdiction, platforms become brittle. When legal review becomes the final checkpoint for every analytics project, experimentation narrows to what can survive approval queues. A business does not need to be fined to lose value from weak governance; it can simply become too slow to compete.
This is especially visible in AI adoption. The companies moving fastest are not necessarily the ones with the fewest rules. They are the ones with the clearest internal rules. If a developer knows which data is approved for model grounding, which repositories are off limits, which records contain regulated information, and which retention policies apply, experimentation can proceed safely. If nobody knows, every proof of concept becomes a governance incident waiting to happen.
A high-standard governance model can therefore accelerate innovation by reducing ambiguity. It creates reusable controls, reusable classifications, reusable review patterns, and reusable trust. That is less glamorous than a product demo, but it is how large organizations turn technology from a pilot into a production capability.
What has changed is that privacy, security, and data quality are converging. A privacy team may care about minimization because the law requires it. A security team may care because it reduces the blast radius of compromise. A data science team may care because cleaner datasets produce better models. A CFO may care because storage, e-discovery, and cyber insurance costs are real. Different incentives now point toward the same conclusion: keep the right data, protect it properly, and delete what no longer earns its risk.
That convergence is where resilience emerges. Resilience is not just the ability to restore from backup or survive ransomware, though both remain essential. It is the ability to keep operating when laws change, vendors fail, customers ask hard questions, regulators investigate, attackers probe, and new technologies demand cleaner inputs.
Governance turns those shocks from existential surprises into managed events. A company with mapped data flows, defensible retention, strong access controls, and clear accountability can respond. A company with petabytes of unknown material scattered across platforms can only search, panic, and hope.
That makes governance an architecture story as much as a legal one. If your Microsoft 365 tenant has sprawling guest access, inconsistent SharePoint permissions, unlabeled sensitive documents, uncontrolled Power BI exports, and no lifecycle policy for Teams data, the problem is not merely that privacy paperwork is incomplete. The problem is that the business does not know how information moves through its own nervous system.
The same applies on premises. Legacy file servers, departmental databases, old application shares, PST archives, and unmanaged endpoints often contain the data organizations least understand and most fear losing control over. Cloud migration can make that better, but only if migration is paired with classification and cleanup. Otherwise, the company simply relocates disorder to a newer billing model.
The practical path is not glamorous. Inventory the data. Classify it. Assign owners. Reduce ROT. Tighten permissions. Log access. Test deletion workflows. Review vendors. Align retention to business need and legal duty. Then, and only then, let AI and analytics systems consume the data with confidence.
Governance-before-automation is a harder sell because it sounds slower. It asks executives to fund classification, cleanup, policy design, and information architecture before the productivity gains are fully visible. But the alternative is worse: deploying systems that make faster decisions from data the organization cannot defend.
The mature position is not anti-AI or anti-analytics. It is the opposite. If data is the fuel for modern business, governance is the refinery. Raw accumulation is not strategy. A company that hoards data without governing it is not becoming more intelligent; it is becoming more exposed.
Altamura’s argument lands because it reframes privacy from a constraint into a condition of durable innovation. That framing is increasingly difficult to dispute. The organizations that treat data governance as a board-level discipline will move faster not because they ignore risk, but because they have already engineered around it.
The Privacy Patchwork Has Become an Operating Model
For years, American companies treated privacy as a matter for lawyers, auditors, and breach-response teams. The business collected the data, product teams found ways to use it, and compliance departments tried to make the paperwork line up afterward. That model was tolerable when data lived in relatively stable systems of record and the worst governance failure was an embarrassing consent banner or a delayed deletion request.It is much less tolerable in 2026. Data now moves through SaaS platforms, customer analytics suites, AI training pipelines, outsourced processors, cloud data lakes, employee productivity tools, and automated decision systems. A company can no longer pretend that privacy is a perimeter issue, because there is no stable perimeter around the modern information estate.
Altamura’s core point is that organizations should stop waiting for a perfect federal privacy law and instead manage data to the most rigorous standard they face. That “highest common denominator” approach sounds conservative, but in practice it is often the only scalable one. A multinational business cannot realistically run one privacy model for California, another for Colorado, another for Europe, another for health data, and another for financial records without creating precisely the complexity that attackers, auditors, and litigators exploit.
The irony is that America’s fragmented privacy regime has forced serious companies toward a more mature posture than a weak national law might have required. The best-run organizations are not asking, “What is the least we can do in this jurisdiction?” They are asking, “What control framework lets us operate globally without re-engineering trust state by state?”
AI Turns Bad Data Hygiene Into Business Risk
The arrival of generative AI has made data governance feel newly urgent, but the underlying problem is older than ChatGPT, Copilot, or enterprise retrieval-augmented generation. Businesses have long tolerated messy data because storage was cheap, governance work was dull, and every department believed its archive might someday become useful. AI has changed the cost of that neglect.Models and analytics systems do not merely store bad information. They amplify it. Poorly classified records, stale customer profiles, duplicate files, unsecured exports, and forgotten repositories can all become inputs into automated systems that produce confident, wrong, or legally sensitive outputs. In that sense, data quality is no longer just an efficiency concern. It is a control surface.
That is why Altamura’s emphasis on redundant, obsolete, and trivial data matters. The industry shorthand, ROT, can sound like a records-management nuisance, but it is really a resilience problem. Every unnecessary dataset increases storage cost, search noise, discovery exposure, breach impact, and the odds that an AI system will draw from material nobody has validated in years.
The lesson for Windows-heavy enterprises is especially blunt. Most corporate environments still contain decades of SharePoint sites, file shares, Exchange archives, OneDrive folders, exported spreadsheets, Teams chats, SQL databases, and endpoint remnants. Before an organization can responsibly plug Microsoft 365 Copilot, Azure AI, or any analytics platform into that estate, it must know what data exists, who owns it, how long it should live, and whether it should be used at all.
Trust Is Now Part of the Data Pipeline
The privacy conversation often frames consumers as passive subjects whose data is collected, processed, sold, secured, or breached. That misses the feedback loop. People change their behavior when they do not trust a system.If users believe a service is careless, invasive, or opaque, they withhold information, provide false details, decline permissions, abandon accounts, or move to competitors. That makes the company’s data worse, which makes its personalization, analytics, fraud detection, and AI outputs worse. Privacy failure therefore degrades not just reputation, but the quality of the business’s own intelligence.
This is where “privacy by design” stops being a slogan. A company that embeds governance into product development, vendor management, retention schedules, access control, and AI deployment is not slowing innovation. It is protecting the integrity of the information innovation depends on.
The companies that understand this will treat trust as infrastructure. They will measure whether data is accurate, permissioned, current, minimized, classified, and explainable before using it to automate decisions. They will also recognize that customers, employees, and partners increasingly judge organizations by how responsibly they handle information, not merely by whether they avoid fines.
Federal Inaction Has Pushed Governance Into the Boardroom
The United States has tried repeatedly to move toward federal privacy legislation, and Congress returned to the issue again in 2026 with proposals including the SECURE Data Act. Axios and other outlets have reported that the latest push again runs into the old fault line: whether a federal law should preempt stronger state rules. That fight is not procedural. It is the central political argument over American privacy.Businesses want uniformity because uniformity reduces compliance cost and operational uncertainty. Privacy advocates and some state officials resist broad preemption because a weak federal law could flatten stronger protections already enacted in states such as California. Both positions have merit, which is why the stalemate persists.
For executives, the practical consequence is clear: waiting for Washington is not a strategy. A federal law may eventually arrive, but it may not resemble the current draft, it may face legal and political challenges, and it may still leave sector-specific obligations intact. HIPAA, GLBA, children’s privacy rules, state breach notification laws, contract requirements, international transfer rules, and industry standards are not going away simply because Congress passes a baseline privacy statute.
This is why governance has moved upward. Boards and executive teams increasingly see data as a balance-sheet asset that can become a liability overnight. The same customer dataset that powers personalization can become a breach notification nightmare. The same document archive that supports institutional memory can become a discovery burden. The same AI initiative that promises productivity can expose sensitive data if access controls and retention policies are weak.
The Real Cost of Fragmentation Is Innovation Drag
Compliance teams often describe fragmentation in terms of legal obligations: different rights, thresholds, deadlines, exemptions, and enforcement mechanisms. That is accurate, but incomplete. The deeper cost is innovation drag.When product teams cannot tell whether a data use is allowed across markets, they slow down. When engineers must build bespoke workflows for every jurisdiction, platforms become brittle. When legal review becomes the final checkpoint for every analytics project, experimentation narrows to what can survive approval queues. A business does not need to be fined to lose value from weak governance; it can simply become too slow to compete.
This is especially visible in AI adoption. The companies moving fastest are not necessarily the ones with the fewest rules. They are the ones with the clearest internal rules. If a developer knows which data is approved for model grounding, which repositories are off limits, which records contain regulated information, and which retention policies apply, experimentation can proceed safely. If nobody knows, every proof of concept becomes a governance incident waiting to happen.
A high-standard governance model can therefore accelerate innovation by reducing ambiguity. It creates reusable controls, reusable classifications, reusable review patterns, and reusable trust. That is less glamorous than a product demo, but it is how large organizations turn technology from a pilot into a production capability.
Security Teams Have Always Known This Was About Exposure
Security professionals do not need much convincing that less unmanaged data means less risk. Every forgotten archive is a potential breach multiplier. Every over-permissioned file share is a lateral movement opportunity. Every stale identity with access to sensitive data is an incident report in embryo.What has changed is that privacy, security, and data quality are converging. A privacy team may care about minimization because the law requires it. A security team may care because it reduces the blast radius of compromise. A data science team may care because cleaner datasets produce better models. A CFO may care because storage, e-discovery, and cyber insurance costs are real. Different incentives now point toward the same conclusion: keep the right data, protect it properly, and delete what no longer earns its risk.
That convergence is where resilience emerges. Resilience is not just the ability to restore from backup or survive ransomware, though both remain essential. It is the ability to keep operating when laws change, vendors fail, customers ask hard questions, regulators investigate, attackers probe, and new technologies demand cleaner inputs.
Governance turns those shocks from existential surprises into managed events. A company with mapped data flows, defensible retention, strong access controls, and clear accountability can respond. A company with petabytes of unknown material scattered across platforms can only search, panic, and hope.
Windows Shops Should Read This as an Architecture Story
For WindowsForum readers, the governance debate can sound abstract until it collides with the systems administrators actually run. In a Microsoft-centric environment, data governance is expressed through identity, endpoint management, retention labels, sensitivity labels, audit logs, data loss prevention policies, conditional access, backup design, e-discovery, and privileged access management.That makes governance an architecture story as much as a legal one. If your Microsoft 365 tenant has sprawling guest access, inconsistent SharePoint permissions, unlabeled sensitive documents, uncontrolled Power BI exports, and no lifecycle policy for Teams data, the problem is not merely that privacy paperwork is incomplete. The problem is that the business does not know how information moves through its own nervous system.
The same applies on premises. Legacy file servers, departmental databases, old application shares, PST archives, and unmanaged endpoints often contain the data organizations least understand and most fear losing control over. Cloud migration can make that better, but only if migration is paired with classification and cleanup. Otherwise, the company simply relocates disorder to a newer billing model.
The practical path is not glamorous. Inventory the data. Classify it. Assign owners. Reduce ROT. Tighten permissions. Log access. Test deletion workflows. Review vendors. Align retention to business need and legal duty. Then, and only then, let AI and analytics systems consume the data with confidence.
The Companies That Win Will Govern Before They Automate
The temptation in every technology wave is to automate first and govern later. That is how organizations ended up with shadow IT, SaaS sprawl, orphaned cloud resources, and collaboration platforms full of sensitive files nobody owns. AI raises the stakes because it makes those mistakes easier to scale.Governance-before-automation is a harder sell because it sounds slower. It asks executives to fund classification, cleanup, policy design, and information architecture before the productivity gains are fully visible. But the alternative is worse: deploying systems that make faster decisions from data the organization cannot defend.
The mature position is not anti-AI or anti-analytics. It is the opposite. If data is the fuel for modern business, governance is the refinery. Raw accumulation is not strategy. A company that hoards data without governing it is not becoming more intelligent; it is becoming more exposed.
Altamura’s argument lands because it reframes privacy from a constraint into a condition of durable innovation. That framing is increasingly difficult to dispute. The organizations that treat data governance as a board-level discipline will move faster not because they ignore risk, but because they have already engineered around it.
The Practical Lesson Hidden Inside the Privacy Debate
The privacy debate can be ideological, but the operational lessons are concrete. Businesses do not need to wait for a perfect statute to improve their posture, and IT teams do not need to wait for legal departments to discover obvious data risks.- Organizations should govern data to the strictest standard they materially face, rather than building brittle state-by-state or region-by-region exceptions.
- Redundant, obsolete, and trivial data should be treated as a security, cost, and AI-quality problem, not merely a storage nuisance.
- Privacy, security, records management, and AI governance should share operating controls instead of functioning as disconnected programs.
- Microsoft-centric environments should treat identity, permissions, labeling, retention, auditability, and data loss prevention as core governance infrastructure.
- Federal privacy legislation may reduce some uncertainty, but it will not eliminate the need for mature internal data governance.
- Companies that can prove responsible stewardship of information will have an advantage with customers, regulators, partners, insurers, and their own employees.
References
- Primary source: TechRadar
Published: 2026-07-06T08:49:08.155376
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