CDO, CPO, IAPP, Information Management and Governance, Information protection, Privacy

Looking Ahead: The New Operating Model for Business

COVID-19 has had a horribly disruptive effect on almost all people and aspects of society.  This paper starts a dialog around an admittedly tiny aspect of that and a view to the future.  It in no way should be seen to marginalize or trivialize the pain and suffering endured by the millions of people directly impacted by the pandemic.

On May 1st, CNBC published this article that discusses how some businesses are re-evaluating their need for physical office space in light of their experience with a majority of their workforce working remotely.

The rapid shift to work-from-home has served as a catalyst for change.  Many years ago, when video conferencing first became available, companies started to invest in equipment that was office-bound, hoping to reduce business travel. That never happened because the technology was temperamental, brands didn’t interoperate very well, there were never enough facilities, and the equipment required expensive point-to-point T1 lines.

Since then, there were advances in the technology along many orientations, including high speed internet to homes, corporate adoption of laptops, smartphones, and importantly, audio conferencing.  This enabled a shift toward work-from-home, and corporate shared office space – “hoteling” (universally adopted by consulting firms and hated by employees), smaller offices/cubicles sold euphemistically as “open concept” workspaces.  But many were still reluctant to use video (Dilbert summed it up well with a series of comics depicting people “working from home” taking video calls wearing their bathrobes).  Workers were far more comfortable with audio conferencing than video, but it still did a lot to get companies and workers more used to remote working.

The needle moved further toward remote workforce with the dramatic increase in off-shoring, leverage of contractors which in itself lessened the feeling of permanence of employment, and perhaps contributed to workers feeling more comfortable as individual contributors working from anywhere.  Paradoxically, there was a simultaneous shift toward urban living, as the number of young people wanting to drive or commute went down, which one might have thought would shift them back to offices.

Powerful Disruptor

All these shifts were gradual, and the net result was tidal shifts in the work model.  Leave it to nature to provide a dramatic disruption, which has resulted in remote working suddenly accounting for 95+% of non-essential workers.  The points raised in the CNBC article are not at all surprising, given how the experts are bracing for periodic reemergence of Corona, but are also supported by:

  • The high cost of commercial real estate and the need to manage costs
  • The remarkable advances in technology enabling remote working
  • The quality of life impact of time-wasting commutes

A shift to predominantly remote working has immediate benefits, including the opportunity to hire the most qualified workers without regard to their physical location, which helps address challenges businesses have faced hiring the right talent.  It also has consequences, such as the inevitable glut of empty office space.  The sudden reduction in the concentration of office workers has a significant impact to businesses relying on them – restaurants, shops, laundry, shoe-shine, even metropolitan transportation – as large portions of their customers stop coming.


In the past, there have been dramatic disruption to business leading to the shrinkage or elimination of entire industries.  Yet over time, business comes charging back.  Before Corona, unemployment was at record lows, and companies were clamoring for skilled workers.  This is after gloomy predictions of unemployment after waves of off-shoring everything from manufacturing to call centers to highly skilled workers.

What has to happen for remote working to become as effective as working from a managed location?

Physical space: Many people don’t have home offices and take over the dining room table instead.  This isn’t sustainable, since asking people to shift from a company managed location to home involves a level of disruption and the only financial beneficiary is the employer.   Wouldn’t it make more sense for the employer to provide each employee a remodeling budget (funded by savings resulting from reduced commercial real estate costs)?  Small contractors could build-out home offices based on guidelines or specifications defined by the employer.

Technology infrastructure: When someone works in an office, the employer provides a laptop and a portfolio of business applications, but also the infrastructure to provide access to those applications – physical connectivity, wi-fi, deskside support.  They establish standards that they are able to support in a cost-effective fashion.  This needs to be replicated in some fashion at home, at least for a portion of the workforce.  It’s not realistic to expect the worker to solve all their home technology issues and not impact their efficiency.  Solution?  A ramp-up of home technology service-providers (e.g., Geek Squad) who set up and support home offices.

Improved wireless: There is a race underway to roll out 5G infrastructure and public wi-fi 6 that promise high-speed performance that rivals (or beats) home-based/cable internet access.  This may be a boon for remote workers and their employers because it simplifies the support model by eliminating the so-called “last mile” connectivity to the individual house in favor of a more controlled infrastructure using transmitters on towers in public spaces.

Comforts and conveniences: As people get used to working remotely, their appetite for convenience goods and services will likely return.  This means the retail services that had been located near office buildings will cater to home-based workers.  To be sure, it won’t look the same, given that the density of customers is different.  There will be more home delivery or curbside service.  Will it be the same in terms of volume?  Probably in an overall sense, but the concentration will differ.  But it seems reasonable that the businesses that can cater to distributed remote workers will benefit.

Challenges – Privacy and Data Protection – a tiny slice

There is no doubt that as with any fundamental disrupter, there will be challenges to be met before we move to equilibrium – the so called “new normal”.  Among many others, information protection and privacy faces challenges.  Some years ago, a colleague authored a prescient paper entitled “Privacy in a Pandemic” that explored the reasonable tradeoffs to be made when balancing individual rights against the needs of society, famously captured by Spock as he sacrificed himself believing “the needs of the many outweigh the needs of the few… or the one”.  But the new equilibrium has implications for privacy and data protection in a more corporate setting.  While privacy regulation accommodate these priorities, privacy and data protection programs will have to re-calibrate their risk assessments and place new weight on risks made more prominent by the shift away from office-based workers, to one where the line between personal life and professional activity is blurred to the point where you can hardly tell the difference.  Clear desk policies went from being a constant real and philosophical debate to now being completely unenforceable, and therefore mostly moot.  Implementing sound technical controls that don’t disproportionately interfere with the ability to work will take time, and likely require new technology deployments.

Understanding purpose: A key enabler to pivoting data privacy will be a mature data governance program.  Making assumptions around higher level enterprise controls is no longer safe.  Instead, knowing the nature and location of data is far more important in order to protect while enabling use.  Providing more discrete permissions around the use of data will help lessen the risk of loss and unauthorized disclosure.  Understanding the purpose behind proposed use of data will enable assigning more discrete permissions.  Since preserving privacy is a lot more than just ensuring protection, the philosophy of understanding purpose also helps ensure appropriate use of data.

Fundamentals: Implementing new controls will take time and carries the risk of creating more frustration and confusion that benefit until the edges are smoothed out.  Privacy leaders should step back and consider the full breadth of their programs, leveraging all techniques to manage risk while avoiding unnecessary disruption.   An effective awareness program, for example, can go a long way to encouraging people to make safe decisions when handling data.


COVID-19 has created havoc in unprecedented ways, and has affected the lives of billions of people.  The human toll cannot be measured, and the suffering by so many should not be swept aside.  Experts are working through the optimal medical strategies while economists are still trying to model the short, medium and long term impacts to business.  Entire books will be written and college classes will be structured around the Coronavirus pandemic.  This paper has taken a very narrow slice of that and will hopefully start an open-minded dialog around how to help enable the future operating model for business.  The dialog can and will continue in months and years to come.

CCPA, CPO, GDPR, IAPP, Information Management and Governance, Information protection, Privacy, Risk management

Why do we have such a hard time understanding, assessing and managing risk?


Risk is a real concept that manifests across life.   Within a business context, risk management is a valuable tool to help improve the probability of success.  This paper explores the role of a risk manager, and is applicable across the board – whether business processes, technology, security, privacy, information or enterprise.  The reader can easily extrapolate the ideas to any aspect of life.

Definition and Reporting

Definition of risk: The probability or threat of quantifiable damage, injury, liability, loss, or any other negative occurrence that is caused by external or internal vulnerabilities, and that may be avoided through preemptive action.

The key word is “probability” – the likelihood that the event will occur.  In some instances, that can be calculated empirically, if all inputs and effects are known, where triggers can be identified – even if random (roll of the dice).  Other times, probability can be estimated based on historical data around similar conditions (50% chance of rain).

Other times, especially in business settings, there are more variables than can be practically tracked and quantified.  In those settings, Risk Managers use judgment to assess the risk of an event occurring.  The risks are usually classified in a 3 or 5 point scale – say, red, yellow, green or severe, major, moderate, minor and insignificant.   And the more knowledgeable the Risk Manager, the more insightful their assessment of risk, but it still remains a probability.


Communicating risk gets complicated when we start factoring in risk mitigating strategies – avoid, reduce, transfer, accept—and reduction techniques – controls, TOD/TOE, residual risk, control risk, etc.

Even within the mitigating strategies there are grey areas – avoiding has consequences (lost opportunities), acceptance doesn’t mean the adverse event will occur, reduction doesn’t mean eliminate.

While some leaders claim they are comfortable navigating uncertainty, there is no question that business hates risk: markets react to uncertainty, and “punish” companies that operate with too many unknowns, and reward those that demonstrate clarity.

People publish dashboards and discuss numbers of controls, as though they were currency – more controls must be better – even though one good (strong) control could replace many poor (weak) controls.  Even auditors are reluctant to rely on process controls and would rather verify every transaction instead (assuming they could).

So what’s the issue?

To some extent, we, as Risk Managers, are the issue.  When asked about risk, we articulate it in our own language:

Risk Manager to Client (or internal business stakeholder): “there is a risk that such-and-such could happen that has these consequences”

Client: “how likely?”

RM: “moderate”

Client: (thinks: “huh?”) “what can we do about it”

RM: “implement x-y-z control”

Client: “will that make it go away”

RM: “implementing this control will reduce the risk, but it leaves a residual risk”

Client: (thinks: “huh?”) “Is that a ‘yes’?  Why wouldn’t you just do it?  And what’s that mean?”

RM: “here – sign this ‘residual risk acceptance document’”

Client: “ok – done”.  (thinks: “thank god that’s over!”) Back to business as usual.

Let’s face, this exchange isn’t very helpful.  The Client clearly doesn’t understand the risk as a potential impact to his/her business, and the “residual risk acceptance document” is a rubber-stamp.

Who owns the risk?  Risk Managers say that their business process stakeholders own the risk, and the Risk Manager’s role is to explain the risk, options for control, and residual risk.  However, it’s fair to say that the business process stakeholders often doesn’t truly accept their role, or if they did, they would engage in a more meaningful dialog.  And the residual risk acceptance document effectively nullifies the dialog.

If the controls are effective, or for whatever reason, the risk fails to manifest, then what?  How often does the client step back and acknowledge that RM did their job and issues were avoided?  Or does the client question why the risk management exercise was undertaken?  On the other hand, if an adverse event takes place, despite controls, does the client look at RM as though they failed?  The cynical reader would point out that if the on-going processes of managing risk management were part of core operations, then you wouldn’t see a spike in RM funding after an event takes place; you might see some refinement or realignment, but not a huge uptick in funding…

An alternative approach

So the challenge is how to meaningfully communicate risk to leadership in a way that puts risk in a business context.

First, one must keep clear: generally speaking, risk can’t be eliminated if the business wants to undertake the activity that introduces the risk.  That said, the Risk Manager can keep the following in mind as these points might promote meaningful communication:

  1. Articulate the risk in familiar business terms (“speak English!”). Explain what would have to happen to trigger the risk.  If you describe a chicken-little event without explaining the triggers, you might get dismissed.
  2. Be realistic when describing the risk and the likelihood. The likelihood should include realistic related events.
  3. Propose options for mitigating the risk, including avoid-reduce-transfer-accept. Bring a reasonable amount of research to present viable options, and be able to articulate the residual risk.
  4. Understand appetite for risk at an appropriate level. A mid-level manager may have a different appetite for risk than the CEO.
  5. Consider what kinds of risks needs to be escalated and to what level: Don’t present a risk to a CEO in a “Enterprise Risk Management” setting that should be addressed by a mid-level manager.
  6. Be realistic in evaluating the consequences of the risk. Walk the stakeholder through understanding the various consequential outcomes to help determine an appropriate mitigating strategy.
  7. Make clear who owns the risk. Get rid of “risk acceptance” documents – if a risk is significant enough to warrant action, it should be pursued.  Risk Acceptance documents are an attempt to shift/assign responsibility, and if they are needed, then they will also be ignored in the post-mortem.
  8. Acknowledge that business environments are dynamic, and events rarely unfold negative risks occur. People intervene.  Processes engage.  The outcome is rarely what was predicted when the risk was recorded.  And the more catastrophic the risk, the more it morphs as it unfolds.

Many of these considerations apply in the post-mortem stage.  One of the big challenges in the risk management community is one of appropriate hindsight.  When evaluating changes to make in risk management in light of an event, it’s important to remember what was known and considered at the time risks were assessed.

The overarching themes in this article is that risk managers need to be realistic when articulating risks, consequences and controls.  Risk managers must recognize they need to bridge the communications gap to their stakeholders by describing risks in business terms that will resonate.

Risk is a fact of life in every aspect of business.  Bad stuff happens, and risk management is not risk “elimination”.  Risk managers play a critical role, and by thoughtfully supporting their stakeholders, they can help business accelerate forward.




Organizational Placement of Privacy

Question for the community: where should a Chief Privacy Officer (or more broadly, the privacy function)?  Some alternatives include:

  1. Counsel’s office: Since privacy is a legal matter, it stands to reason that compliance would benefit from being embedded with the general counsel.  On the other hand, counsel is often positioned as a separate function to demonstrate objectivity and independence from operations.  Moreover, since lawyers are trained to look at situations through a legal-risk lens, they are sometime less able to “get to YES” and truly embed privacy in operations.  Operations folks may look at their Legal colleagues in general as someone providing “sign-off” and that perception might extend to privacy compliance.
  2. Risk Management & Compliance: again, the alignment has some logic, since privacy provides a set of requirements that overlaid on operational processes, and one should manage the risk of non-compliance.  However, similar to assigning privacy to the Counsel’s office, Risk and Compliance are often organizationally separate to maintain objectivity and independence.  As a result, there will likely be challenges in embedding privacy into operational processes to achieve Privacy/Data Protection by Design.
  3. Office of the Chief Data Officer: The CDO is tasked with understanding the full breadth of data for purposes of deriving value and helping the organization leverage data in existing and new initiatives.  As a result of developing and maintaining the inventory of an organization’s data, the CDO is in a natural position to assess the applicability of privacy requirements and embed privacy requirements in business processes.  The challenges include that the CDO may be perceived has having a conflict of interests by owning privacy compliance as well as data leverage goals (in much the same way as a CIO has a conflict of interests by owning the CISO function).  Another challenge is that CDOs don’t always own all data in the organization, instead focusing on the data to be leveraged or monetization.  This leaves key gaps – such as employee data.
  4. Office of the CIO or CISO: The CISO is tasked with protecting data and is often looked to when there are data incidents.  As a result, the CISO has operational processes as it relates to embedding security requirements as well as monitoring/responding to issues, so adding privacy requirements would seem like a logical extension.  Moreover, the CIO and CISO are very well versed at implementing tools and extensions, which will be required for an effective program.  Privacy professionals will be quick to point out that privacy requirements extend well beyond security, and compliance requires a different level of understanding of the nature of data and how it’s used; a privacy breach may exist where no “traditional” security breach has occurred.  Moreover, privacy requirements apply to information and processes across an organization – not just those within scope of the CIO.  You could have an entire privacy awareness curriculum that never mentions technology, instead focusing on how people handle information. 
  5. Operations (COO): Having privacy report of the COO can make sense, depending on the organization.   Whereas privacy has been around for many years, the passage of landmark privacy legislation – with significant consequences for non-compliance – has very quickly elevated its importance in organizations, making it a Board-level or C-suite priority in some cases.  Having it report to the COO gives it prominence and positions it as aligning with the entire company.  This helps enable the implementation of privacy processes as embedded components in business process.  If done right, the result is a less disruptive but more effective program.   The downside is that unless the organization is a very data-focused company, privacy may get lost among the COO’s other priorities, and may be the target of political struggles.

To be sure, any of these models can work, if provided with the appropriate leadership, support and oversight.  Moreover, the culture of the company and the nature of their business can also influence an appropriate structure.

Privacy is at a crossroads.  One the one hand, the emerging interest and concern from consumers (and therefore legislators) puts pressure on companies to acknowledge their responsibilities handling personal information properly.  On the other hand, since privacy has been around for a while and is conceptually familiar to executives, is there a level of privacy fatigue being felt?  As a result, are companies less motivated to address the risks, instead adopting a wait-and-see attitude?

CCPA, CPO, GDPR, IAPP, Information Management and Governance, Information protection, Privacy

Does Privacy Need Disrupting?

Executive Summary

When it comes to the use of data in a business context, there are a few absolute truths: (1) business will continue to gather and process more and more information about people to meet their goals. (2) We will continue to see larger and more far-reaching data events involving personal information.  And (3) regulators will continue to respond with increasingly complex requirements around the handling of personal information.

This paper reflects on the trajectory data-use is taking within the business environment, and explores some challenges the privacy profession is facing trying to keep pace.  The combination points to the inevitability of catastrophic data incidents.

But like so many other industries, modern technology may hold the answer to managing the risk.  This paper goes on to discuss that through the measured deployment of disruptive technologies, the privacy profession may find a way to support the acceleration of data use in the business, while managing risk and pursuing compliance.


The thing about black swans is that they are both predictable and unpredictable – you know they are going to happen, you just can’t anticipate when and the form they will take.  In the period of one week in December, over 600 million records containing PII (Personally Identifiable Information) were breached. For perspective, that’s more than every man, woman and child living in the US, UK, Canada, Australia and Russia combined.  

With the increasing volume of PII being collected and processed by organizations around the world, it was inevitable that something like this was going to happen.  Moreover, it will happen again – and bigger – from triggers and vulnerabilities on which the risk community is not focusing. And no global organization wants to be named in a headline that talks about hundreds of millions of records being compromised.  

About data

We live in an age where information is emerging is a truly leverage-able resource for companies around the world, enabled by the incredible pace of change in technology and analytics capabilities.  The opportunities to improve customer experience are growing exponentially. To be sure, customers now measure their own satisfaction – and loyalty – based on capabilities offered by service-providers that were not even possible a few short years ago.  And companies are doubling-down investment to outpace their competition, or in many cases – in the face of disruptive startups – ensure their very survival.

Much of the data at the heart of the most promising innovations is in some way tied to individuals — whether traditional PII or PHI or new data around people’s movements, tastes and behaviors, spun off from IoT sensors, new analytics technologies or apps used by individual consumers where they are knowingly or inadvertently contributing data.

We also see that as some companies push the boundaries, or in the aftermath of high profile data incidents, lawmakers are reacting by implementing far-reaching legislation to protect the rights of individuals.  Complying with those is a challenge and imperative for all organizations but especially forward-looking global organizations, as they navigate uncharted waters and as regulations emanating from different jurisdictions overlap and conflict.  

Given the pace and trajectory of developments in technology and data, and the scale and frequency of data events, it’s reasonable to predict that there will be more breaches in the future – both larger in scale and more impactful.   Moreover, the increasing number and complexity of regulatory requirements – many triggered in the aftermath of data breaches – will place increasing burden on businesses, increasing internal tension between those developing new and innovative products and services, and those tasked with managing risk and ensuring compliance.  Finally, the potential ramifications of a breach, including the very significant fines, lost business or damage to the brand, can have lasting negative consequences to any organization.

Risk and privacy activity today

Today, risk management and privacy are heavily manual.  Risk management and privacy groups are relatively compartmentalized, often viewed as necessary but imposing layers of bureaucracy, addressed late in the process and after the business requirements are met; risk and privacy requirements are often viewed as disruptive and costly.   

Whereas “Privacy by Design” seems like an obvious enabler, and has been a holy grail of sorts, passionately embraced by privacy practitioners, it’s often down-played (or ignored) by business development groups.  

The basic process around risk and privacy include the following:

  1. Privacy Policies that reflect requirements — whether legal, contractual, ethical, professional or industry parameters.  This establishes the inward- and outward-facing posture and serves as the foundation and basis that drives every meaningful aspect of the program.
  2. Process documentation: business processes that handle PII are documented and analyzed to identify risk and to ensure that controls mitigate the risk and align with policy requirements.  
  3. Data and application inventories: as a supplement to process documentation, knowing what data is on hand and what applications process it is important to help ensure that appropriate controls are in place
  4. Trigger points within processes – IT or business processes – around changes or data events requiring action; certain activities such as developing or changing an application that stores or processes PII should trigger a Privacy Impact Assessment to determine what risks exist and what controls are needed.
  5. Consultations and approvals where SME’s respond to inquiries and use research and professional judgment to provide recommendations.
  6. Risk assessments take place periodically to determine what’s changed and whether controls are aligned with risks to PII.
  7. Controls are tested periodically to ensure they are functioning as intended
  8. Control weaknesses or failures are documented in findings reports requiring action by control owners

The process is largely manual

The key point in providing this list is to highlight the fact that all of these are manually intensive and are at best supplemented or enabled by tools such as GRC applications.  And while the enabling tools and applications help, these processes are only linearly scalable – meaning, increases in the number of in-scope processes and applications require a proportional increase in resources — people — to accomplish the risk and compliance activity.   Moreover, while the most effective privacy programs distribute the activity across the business constituents, and can gain some leverage and economies of scale, the costs fundamentally increase fairly linearly.

Most organizations face challenges in trying to increase their bench of Privacy SMEs, since they require in-depth understanding of their organizations, as well as privacy expertise, and need to exercise consistent and similar judgment.  So maintaining consistent quality around advice provided by SMEs is a risk and challenge in itself.

So in summary, the technology, data, business and regulatory environment is evolving rapidly, getting more complex, and more critical for the continuing success of the organization.  Traditional privacy risk and compliance practices are heavily manual, reactive, burdensome and difficult to scale. In combination, it’s clear that costly and damaging issues will continue to arise, and the tension between the execution of business strategy, managing risk and maintaining compliance will become even more pronounced.

What is changing…

In order to become better embedded and get ahead of business developments that leverage data, the privacy function needs to understand how the business plans to gather, manipulate and store PII, and overlay the risk and compliance requirements for its treatment and handling – which should result in certain adjustments to the business strategy.  

The privacy team has to understand all aspects of information risk management (leveraging an auditor’s playbook) to judge sufficiency of control, and be able to interface with the business, IT, IT security, legal, audit and compliance stakeholders, as well as with regulators.  

An important dimension of this is to have a framework for accepting residual risk.  This framework has to resist the “group-think” temptation to be either blinded by competitive pressure or the promise of fantastic profits, or lured into the “risk elimination” mode.  Instead, it should allow for the analysis of risk, mitigating effect of controls, and a transparent mechanism to accept residual risk that escalates upwards through leadership, depending on the overall risk/benefit balance.

But as discussed above, data “events” are bound to happen — whether breaches, losses or abuses — and privacy professionals too often are reactive.

Fundamental and disruptive change – leveraging Artificial Intelligence

Business, technology and data science will continue to accelerate, events will happen and regulations with come into effect.  The result is an increasing tension between opposing forces, where the resistant compliance side of the equation will almost always lose.

It’s time to take a fresh look at the model.  Increasingly, companies are recognizing the disruptive effect that data and analytics (including AI) will have on their business – the very action that increases the risk of privacy events discussed in this paper.

Privacy compliance can benefit from disruption.  

Ultimately, many aspects of privacy compliance will benefit from the disruptive use of AI and cognitive algorithms.   Given that privacy compliance combines documentation, analysis and judgment, there are opportunities to design and train algorithms to assist analysis, which will increase the timeliness and reach of the program.


First and foremost is the recognition that intelligent automation and leveraging AI is a journey – not a destination – and benefit is gained incrementally.  Focus begins on the more basic and mechanical aspects of the program, allowing more analyst time to focus on more sophisticated and complicated issues.

The privacy activities are then broken into categories which helps to drive priorities:

  • Routine daily tasks that need to be monitored for compliance, and where certain events trigger action, and
  • Change involving new applications, data, business ventures or data use cases
  • New requirements, such as new regulations, risk factors or data use restrictions

As the process matures, more aspects of the program can be automated, leading to a state where increasingly sophisticated tasks are processed automatically and the SME is engaged at certain thresholds where, say, more judgement or specific approval is needed.  If properly implemented, the algorithms are trained methodically (“crawl, walk, run”) and logged to ensure consistency.

Example activities that are candidates for automation:

  1. Process review comparing to policy – using an algorithm to determine whether a proposed process might violate a privacy policy
  2. Access monitoring – data stores containing information pertaining to people can be monitored for access and AI can analyze access for anomalies, and trigger responses
  3. Data access requests – routine operational transactions, such as requesting access to certain data, can be vetted and handled through Intelligent Automation
  4. Transaction monitoring – AI sensors can be tuned to monitor a wide range of structured and unstructured transactions guarding against inadvertent use of private information
  5. Privacy event analysis/DLP – Data Loss Prevention (DLP) sensors can capture thousands of potential events on a daily basis.  AI can be used to risk-assess the events based on a variety of rules, and flag those exceeding a predetermined risk threshold for further investigation.  
  6. Control analysis and testing – privacy programs often include a periodic testing cycle.  AI can be used to evaluate the results of testing to assess severity
  7. Data discovery and inventory – All organizations have large volumes a unstructured data stored (and often forgotten) on network file servers.  AI can be used to traverse the file stores and build meta-data tables around the data, and can be tuned to identify sensitive data, helping to ensure compliance
  8. Data psudonymization – AI can be used to implement psudonymization techniques on a large scale, and can test whether the data can be re-identified.
  9. Contract review – often times additional specific data handling terms are embedded in contracts with large clients.  AI can be used to extract those terms and correlate them to specific data in the environment to help comply with the client’s requirements.
  10. Regulation review – AI can be used to highlight applicable sections of regulation based on ingested company policy documentation, which accelerates implementing compliance activity
  11. Risk analysis – Algorithms can be trained to detect data use-cases that are in conflict with policy.
  12. Residual risk assessment – Quantifying residual risk is very important for determining whether risks are sufficiently mitigated to meet corporate risk appetite, and whether a value proposition is still valid.  AI can help with the determination.
  13. Customer inquiries – Intelligent automation can be used to handle customer inquiries around where data is, requests for erasure or transfer.  This can be extremely burdensome for companies with large numbers of individual customers.


All these use cases are within the capabilities of existing technology, and the decision to pursue any combination is based on specific circumstances.  However, the overriding point is that they pave the way toward much more flexibility and scalability of a privacy program that is coming under increasing pressure to perform.  So the benefits are:

  • Greater flexibility
  • More scalability and leverage of resources
  • Lower risk of non-compliance
  • Less impact and burden to the business
  • Managed cost


At a high level, the risks are that the tools fail to detect or prevent an unauthorized use or disclosure of information pertaining to individuals.  This can be because the algorithms don’t work as intended or are not properly implemented. These are project and operational risks and should be managed through normal risk management processes.

But by keeping in mind the current state and the trajectory business is on, the reality is that leveraging Intelligent Automation and Artificial Intelligence makes sense.  It’s going to happen.


When it comes to the use of data in a business context, there are a few absolute truths: (1) business will continue to gather and process more and more information about people to meet their goals. (2) We will continue to see larger and more far-reaching data events involving personal information.  And (3) regulators will continue to respond with increasingly complex requirements around the handling of personal information.

Many industries are being disrupted by the creative and innovative use of data.  The privacy profession — increasingly in the spotlight, yet dependent on manual processes — is quickly becoming a good candidate for reinvention.  People will benefit, as it will open avenues for business to provide new products and services designed to make their lives better, while at the same time lowering the risk to them for participating.

CDO, CPO, GDPR, IAPP, Information Management and Governance, Information protection, Privacy

Data Regulation is the New Reality

On October 28th, the BBC’s Chris Baraniuk reported that recently, Tim Cook was in Brussels to address the International Conference of Data Protection and Privacy Commissioners.  In his remarks, Mr. Cook, referring to the misuse of “deeply personal” data, said data was being “weaponized against us with military efficiency”.  The BBC went on to report that Mr. Cook said “We shouldn’t sugar-coat the consequences,” and “This is surveillance.”

The speech reaffirmed Apple’s strong defense of user privacy rights, in contrast to competitors business model of driving advertising revenue by analyzing people online habits.   “The trade in personal data served only to enrich the companies that collect it, he added.”

Mr Cook also praised the EU’s new data protection regulation, the General Data Protection Regulation (GDPR), and went on to say that other countries “including my own,” should follow the EU’s lead toward protecting personal data.

To be sure, economics are at the heart of the concern.  Given the string of events that have occured, where data is mishandled or exposed, companies are at risk of losing customer and stakeholder trust, and without that trust, it not clear how they can drive or thrive in the data economy.  So coming together in support of a GDPR-like framework makes sense; it raises the conversation to a global level, and can result in a safer and more efficient environment in which to conduct business. Companies that embrace the framework will have more flexibility and resilience, while those firms paying it lip-service will eventually themselves be at the center of their own data crisis.

And therein lies the rub.  Compliance is not the same as data protection, especially when the regulation is principles-based and not prescriptive.  If the objective in implementing a framework is to comply with a regulation, one is tempted to overlay one’s current operating model with the requirements of the regulation and address gaps.  While it may reduce the risk of data incident, it will probably do so by coincidence.

On the other hand, if companies went about handling information with the strongest possible ethics, where they routinely assessed and addressed risk, recognized and avoided moral hazards, then the incidence of breach and miss-use would naturally be much lower.

The obvious competitive issue is that the second scenario is more expensive and less flexible.  Moreover, if a minority of companies took this approach, they would be less prosperous, and economic darwinism would cause them to go extinct.  And suppose there were a major data “catastrophe”, and the market environment were made up of a combination of those that embrace a high degree of data ethics and those that don’t, the market may not recognize and reward the higher resilience of the highly-ethical-but-less-profitable.  Instead, what we’ve seen is the mainstream market reacts with shock and incredulity and – at the prompting of regulators – implement newly-penned frameworks meant to avoid future occurrences of those events.

While one might argue that black swan events are by definition unforeseeable, one can equally argue that waiting for them to strike before implementing any sort of protection strategy simply opens the door to more black swans, and when events do occur, they result in unnecessarily high impact.

Apple’s Tim Cook makes clear his view that status quo is unacceptable.  As an unquestioned highly credentialed leader and insider in the technology (and information) world, his use of words like “weaponized” and “this is surveillance” should not be taken lightly — he knows what he’s talking about.  No doubt more so than the rest of us.

So as with most things, a thoughtful balance must be considered.  The dilemma is how to balance among the following:

  1. The rapid growth the volume to data aggregated by companies, across the board from business-to-consumers, to business-to-business companies, and ranging from companies providing services to those providing goods — and the increasing overlap.

  2. As a subset of that, the increase in the volume of personal data stored online, and the ability to gather even more data about individuals – habits, interests, locations, views and opinions.

  3. The rapid evolution in the science of data analytics, and the ramp-up of technology able to manipulate and compute data on a mammoth scale.

  4. Coupled with this, is the increasing ability to combine and analyze datasets in ways that allow for the creation of new and credible data and conclusions.

  5. As the size and richness of the datasets grow, so do the consequences of an event (whether a breach, misuse, abuse or exposure).  These range from a sense of creepiness when personal data is exposed, all the way to the very real consequences of insidious manipulation of our views and opinions.

In short, how can companies derive benefit from data, while managing the risks?  Neither momentum is letting up — the momentum around utilizing the expanding datasets, or the momentum around data events and subsequent responses from regulators.

One realistic way is to embrace and build a culture around managing all aspects of data, in lock step.  This is built into a data management program, led by a Chief Data Officer, comprised of three interdependent functions:

  1. Data Leverage, focussed on enabling the business use of information,

  2. Data Protection and Compliance, focussed on addressing risk resulting from data leverage, in terms of misuse, loss or non-compliance with obligations

  3. Data Quality, ensuring that the data being used retains its accuracy and integrity

This model is coupled with appropriate oversight, in the form of:

  1. A steering group with senior stakeholders from across the company,

  2. Direct oversight by CEO or COO,

  3. Connectivity into other key functions, including the CIO, CISO, HR and Legal,

  4. Active oversight by the Board of Directors, to support business initiatives and agree with risk mitigations plans.

A standing filter for any and all data initiatives needs to be ethics, and a consideration for the consequences of the genie getting out of the lamp.  Is the company willing to handle the outcome, if an initiative goes wrong? What is the risk, how is it managed, and are the right people accepting the residual risk?

The discussion is reaching a fever pitch with leaders of the most influential technology companies adding their voices to the conversation.  Any company wanting to join the data economy should consider doing so with an appropriate data management framework. This will position them to accelerate as new opportunities present themselves, while being able to manage events as they occur and accommodate compliance requirements that arise.