Data Science was supposed to be the next big thing in IT (in the late 2010’s - before prices skyrocketed and the world starting doing double somersaults), but Data Science as a Hype Cycle and an IT practice had several things going against it from the start:

• It had the word “Science” built in and that’s pretty scary (given the anti-science climate that we now live in). Just think about all of the lunatics telling us this week that SpaceX is actually worth 2 trillion because Elon is going to put colonies on Mars (yes it’s really in their IPO prospectus). Now, anyone that knows anything about Science - yikes, begone ye awful word ! - knows that radiation would kill anyone travelling to Mars and if the trip didn’t kill them living on Mars would. But Scence cannot be allowed to get in the way of truly monumental BS.

• Data Science wasn’t connected with any specific products per se - well, any data tool might be connected to it theoretically, but who if anyone could point out a Data Science company - especially one on the NASDAQ or S & P?

• Because Data Nerds (as depicted by the AI-generated cartoon below) look too much like Bill Gates and no one cares if cool Robots massacre them all.

What is (or was) Data Science (DS) Anyway?

Let’s start with a definition of Data Science from Harvard - they know what they’re talking about, right?

The U.S. Census Bureau defines data science as “a field of study that uses scientific methods, processes, and systems to extract knowledge and insights from data.” So, this is a field that works with data that doesn’t fit neatly into rows and columns—and, in the end, derives relevant information from it.

Data science is inherently interdisciplinary as it combines expertise from statistics, computer science, mathematics, and domain-specific knowledge. This makes it incredibly versatile, with applications spanning healthcare, finance, marketing, and even environmental research. Harvard, What is Data Science page.

One of the reasons that Harvard has that page is that they’ve actually got a Master’s Degree in Data Science program, one of many in universities across the nation (and the world) - yet all of them are relatively new (Harvard’s started in 2018). I think that academia was probably faster spinning up those DS programs than they had been creating new curriculum around Cyber Security and other emerging tech practices.

Ok, so the definition of Data Science is somewhat ambiguous and it sort of begs the question as to what you’d actually be doing with a DS degree (which acronym sounds uncomfortably like a BS degree). This got me to wondering, what’s in that Harvard DS Master’s Degree program as it might be representative of what other colleges are doing and then I looked at some of the courses. I’ve included the descriptions from two courses in the program to help assess it…

Critical Thinking in Data Science

This course examines the wide-ranging impact data science has on the world and how to think critically about issues of fairness, privacy, ethics, and bias while building algorithms and predictive models that get deployed in the form of products, policy and scientific research. Topics will include algorithmic accountability and discriminatory algorithms, black box algorithms, data privacy and security, ethical frameworks; and experimental and product design. We will work through case studies in a variety of contexts including media, tech and sharing economy platforms; medicine and public health; data science for social good, and politics. We will look at the underlying machine learning algorithms, statistical models, code and data. Threads of history, philosophy, business models and strategy; and regulatory and policy issues will be woven throughout the course.

The course descriptions don’t include any syllabuses which makes it hard to figure out what they’re actually doing, although there does seem to be a lot of focus on algorithms. This summary description (w/o a syllabus) seems fairly ambiguous and wide-ranging to the point where it sounds like a giant survey course. But maybe, just maybe there are other courses in this DS degree that are more specific, right? Let’s look…

Advanced Practical Data Science (now we’re talking!)

The primary objective of this course is to understand how modern AI systems are built, deployed, and maintained in real-world settings. Beyond developing accurate models, the focus is on turning them into scalable, reliable applications. The course centers on Machine Learning Operations (MLOps) and incorporates modern approaches based on Large Language Models (LLMs), and agent-based systems. Students will learn how to design end-to-end AI workflows, including data pipelines, training, evaluation, deployment, and monitoring. We also introduce key ideas such as prompt design, retrieval-augmented generation (RAG), and how LLMs can interact with tools and APIs in more complex workflows. The course combines conceptual understanding with hands-on implementation, enabling students to build complete AI systems.

What?!? This is an AI course!!!

Now, we’ve got proof positive that AI has not only murdered Data Science, but it’s actually replaced it in its own home like some Doppelganger. Who’s going to be next in this tech rampage…

In Jordan Peele’s Us (2019), unsuspecting families are being killed and replaced by creepy doubles - sounds familiar.

Copyright 2026, Digital Perspectives

Add this current AI-fueled trend to the many decades of White Collar layoffs (and last year’s massive Government layoffs thanks to DOGE) and we end up with an ever decreasing pool of organizational knowledge more or less everywhere. And add to that massive hiring decreases for entry-level roles (e.g. new college graduates) again fueled by the AI-hype and we can see a fairly alarming outcome looming on the horizon. As older, more knowledgeable employees are systematically eliminated, the pipeline to replace them and have someone to pass their knowledge to is simply vanishing. We are looking at a very real possibility that in many organizations, all Knowledge Capital may simply vanish within the next decade.

In this scene from 1975’s Rollerball, the world’s brain, Zero “just misplaced the whole of the 13th Century…” Zero may have predicted the current phenomenon of (AI) Model Collapse 50 years ago.

Definitions

Knowledge Collapse - The deliberate elimination of Knowledge (White Collar) Workers combined with the inability or lack of motivation to capture their Knowledge Capital before they depart. As they are removed and the organizations fail to capture their knowledge, such enterprises become increasingly dependent on tools with inadequate capabilities and / or the remaining overworked Subject Matter Experts (SMEs). The eventual end state is Knowledge Collapse which in turn leads to failure of the organization/s in question as they no longer know how to function.

(Human) Knowledge Capital - The undocumented information / knowledge about how an organization and / or industry operates that cannot be thoroughly ‘trained into’ or otherwise replicated into software logic. This SME-level knowledge is often mission-critical in nature and is seldom ‘covered’ by system or process documentation and when it is represented within solution code, it’s typically hard to decipher. It’s probable that in most organizations, more than 50% of this Knowledge Capital is retained entirely within the personnel of that group.

Knowledge Capture - The deliberate effort to transfer knowledge from an Expert to another employee and / or knowledge solution. There are a variety of ways that this can occur, but for any of them to be effective, they must be part of some consistent process/es. Contrary to popular belief, employees training their AI replacements are not able to transfer knowledge in the same manner as humans do and much is still being lost.

Knowledge Management - A dedicated solution (typically as some form of software or SaaS) that helps to capture and make organizational knowledge available to enterprise users. Very few enterprises have such solutions however and those that do often fail to populate them properly.

What Can Be Done to Avoid or Otherwise Mitigate This Dilemma ?

The first step is to acknowledge that; 1) it actually is a problem and 2) that it is actually happening in most places. Right now, the majority of organizations are actually being rewarded for doing this (e.g. cutting people arbitrarily and without any contingencies for the associated knowledge loss. Last year’s DOGE cuts are a perfect example of this; no studies were conducted in advance and no efforts whatsoever were made to try to extract and retain the knowledge of the 100’s thousands of workers (many of them senior-level resources) that were cut in a very rapid fashion. The result was disruptions across most agencies involved and failure to achieve any of the previously anticipated cost benefits of such cuts (with the agencies often having to rehire people that they had just cut).

There are a few things that enterprises can do to avoid or mitigate this situation though and they include the following:

1. Don’t fire people arbitrarily without determining what the impacts may be of such dismissals.

2. Obtain or build usable and properly populated Knowledge Management solutions.

3. Ensure that the ‘pipeline’ of Knowledge Workers or SMEs is not severed. In other words, don’t expect that AI will magically understand or know your specific enterprise because it won’t - that’s not how the technology works. New workers are always needed to maintain continuity in organization over time.

4. Build Knowledge Capture into the daily routine of your workers and emphasize it in transition activities (hiring, promotion and firing).

5. Do not allow your organization to become overly dependent on software or AI solutions to automate critical business processes. It’s important to keep in mind here that the main value provided by many companies is the IP associated with how they do (and have done) their work. Handing all of that over to 3rd party providers is a recipe for replacement.

Copyright 2026, Digital Perspectives

Just The Facts

Time of Death for the IT Industry occurred sometime in late 2025 or early 2026. Here’s some of the details associated with the cause of death:

• A non-stop, merry-go-round of tech layoffs that began in 2023. While these are still ongoing, they passed a critical level just recently. That critical level determined just how ‘flooded’ the job market is and determines whether even looking for position anymore is worth the effort.

• During 2025, somewhere close to 350k US Government employees were fired as a result of Elon Musk’s & Trump’s DOGE experiment, (Trump just recently bragged about this on national TV), most of these folks were not ready for retirement and perhaps more than half were IT-related and Project Management roles. Thus, we got another 200k or so people who flooded the IT sector at the exact same time all of the tech layoffs were occurring.

• Somewhere in the neighborhood of $1 trillion in CapEx related to AI has occurred in just the last year or so - none of that money: zero - is going towards new or additional employees. In fact, the CapEx is being used as one of the excuses or reasons for the non-stop rounds of White Collar / IT layoffs. Worse than the amount that’s already been spent is the news that another $ trillion or so is on the books to be spent on AI CapEx this year going forward - virtually ensuring that there will continue to be zero money for hiring employees for the foreseeable future.

• Artificial Intelligence executives are also proclaiming now on nearly a daily basis that humans aren’t actually needed anymore and that AGI and Super-intelligence are already here - and other companies are buying into that BS despite all evidence to contrary - leading to even more layoffs.

• Rates and salaries for the few IT jobs that are still being offered are now down to mid-1990’s levels (this is evidenced through rates quoted by IT recruiters). This declining salary / rate trend applies for all AI roles as well (and honestly there aren’t actually any “AI roles” anyway - just legacy roles with “AI” tacked in front as a prefix).

• The official rate of IT Unemployment in the US is hovering around 6%, but that’s totally bogus - the true number is much higher. Also, the vast majority of supposed IT openings that are being listed are fake as well, (although this number did start going down dramatically in 2025). Unemployment Rates for recent IT / Computer Science graduates is significantly higher.

• The above point highlights that the relatively few AI-roles that do exist are actually earning less than ordinary IT roles were just a year or two ago contradicting the bizarre narrative that big tech is in a bidding war for AI talent (it appears as though this war only applies to about 5 people). In every other IT hype cycle over the past 35 years or so many new IT roles and jobs were created - that’s not happening with AI at all.

• The geniuses at Wall Street Journal (likely in the pockets of the AI Hype-masters) are now proclaiming that the US can’t win “The AI Race” against China without further screwing American IT workers by continuing the H1B program at its current level even though Tech employment is plummeting. (one of the myriad of ways that AI is now screwing everyone).

• YouTube is now flooded with channels and videos from IT Professionals describing how they were laid off in Zoom meetings, texts and emails - often with zero notice. These folks are starting new careers or beginning early retirement under the consensus acknowledgement that there are no absolutely no IT jobs.

Zombie IT

Yes, IT will continue to be around for some time, but it’s beginning to resemble a Zombie version of its former self and in many cases it may just end up as the caretakers for mediocre AI that continues to fail at almost every single task it’s given (requiring humans to clean up the mess somewhat like those drivers in the Philippines are driving Waymos that are supposed to be self-driving).

Information Technology as a sector existed before the IT Boom, it was called Management Information Sciences (MIS) then, and it was much smaller and quite different in nature from what sprang forth in the early 1990’s as distributed computing and the PC became ubiquitous in every organization. MIS supported a much smaller workforce and played a far less important role in the Enterprise than IT did for the past 35 years - it seems as though history is repeating itself at least in that regard. This atrophy in both industry size and importance is essentially ending an era within the American Economy where millions (and perhaps even tens of millions) of people were able to enter the Middle Class and afford to live in the nation’s ever-more expensive cities by choosing IT as a career path. That’s over now and it sure doesn’t look as though it’s coming back. By the time executives realize that the current generation of AI tools doesn’t actually do any of things that the tech lords say they do, a new generation of AI might that might actually function will likely emerge ending any potential bounce-back in IT employment. There is no happy ending for this story.

The question then becomes, who will want to (or be able to) stick around in IT within this new environment where:

• AI is valued higher than humans even when it sucks.

• Wages and salaries keep dropping.

• No one knows from one moment to the next whether they’ll have a job.

There are a growing number of folks that I know personally who have left the industry altogether over the past few years - I predict that this trend will turn into a flood beginning in 2026. I am genuinely sad to see this happen as I believe it’s going to have very serious consequences not just for all of the folks losing their careers, but for every company that loses talent that they can’t really replace and for an economy that will likely never recover from losing this many good paying jobs (with nothing on the horizon to replace them).

Copyright 2026, Digital Perspectives

AI & IT

The early to mid-1990’s represented the beginning of the current IT boom. Previous to that explosion of new roles and jobs, things had been pretty stable for what was then referred to as Management Information Systems (MIS). Nearly all of the work was done on Mainframe computers with relatively little access to systems outside the strict confines of IT departments. The Mainframe server rooms were the early version of what later became Data Centers (and is now the Cloud).

The underlying premise behind the majority of innovations associated with Information Technology (IT) has been focused around various forms of systems-based automation. For the past 50 plus years or so, each new wave of IT innovation has led both to greater automation as well as to the creation of new job opportunities and workplace roles associated with those new technologies. The cycle was never meant to last, though – in other words – if the ultimate goal is total or near-total automation, then eventually, jobs and workplace roles would be begin to disappear. In 2025, after many had been predicting this outcome for decades; it's finally beginning to happen.

This phenomenon is manifesting itself in several tangible ways:

1. Multiple rounds of layoffs in Big Tech (companies). This is occurring even in cases where the companies are making large profits. Typically, the companies in question don’t overtly announce that the layoffs are associated with the replacement of personnel with AI tools, but in some cases it has been. The exact number of layoffs since 2023 is hard to track and subdividing it even more specifically into AI-driven layoffs is even more difficult – but the likely figure associated with AI replacement in IT is now in the 100’s of thousands.

2. Targeted job replacements (across all industries). The areas where AI replacement is most prevalent across industries at the moment are associated with Software Engineering, Customer Service and other low to mid-level management and administration roles. Other targeted areas though include writers and other creative personnel across multiple industries.

3. The job market for new grads is cratering. This may seem counter-intuitive at first, given that the new grads tend to have the most / best AI-related skills; however, most of this demographic initially fills lower-level or junior positions and those are precisely the ones being targeted first for replacement.

Obviously, these trends when combined will radically change the hiring landscape for both IT and potentially all other White-Collar jobs. It is quite likely that within the next 5 years, the IT industry will shed millions of jobs as AI replaces an ever-greater percentage of positions and even roles. How much will be replaced in that timeframe is difficult to predict, but it could be anywhere from 25-50% of all current IT jobs and a like number of roles (that will vanish or come close to vanishing). For years, young people were told to study STEM curriculums, learn to code and focus on IT as a stable career – this is all about to change if it hasn’t already. As recently as last year, I was still advocating to all the young people I knew to consider a career in Information Technology – as of this year, I’m no longer making such recommendations. The IT Boom is over. Besides changing the landscape for young people just starting their careers, this trend will impact the jobs & careers of nearly everyone already in the field. Those impacts will include (or already include):

1. Fewer positions with more competition (across the board).

2. Lower salaries (and / or contract rates).

3. The elimination of some job roles entirely.

4. Few, if any, new replacement roles in IT to shift to.

AI & Data Architecture

Data Architecture is a relatively new practice within the larger field of IT. While there was always an aspect of data design associated with system development and maintenance, there were relatively few people who likely considered themselves to be “Data Architects” until sometime around the mid-1990s. The change that occurred during the 1990s that led to an increased need for such a role was related to a proliferation of data sources, new standards and new tools that hit the market (and workplace) more or less at the same time. In other words, the field became sufficiently complex (over a short timespan) to justify a number of additional roles for specialized data-focused roles - including a “Data Architect” role. Ever since then, Data Architecture and the Data Architect have played a prominent part within most mid-sized and large enterprises. And this was only one of perhaps more than two dozen new roles introduced around the same time in relation to IT innovations being adopted in the workplace.

What is a Data Architect anyway?

We’ll go with a top-level definition here: “A Data Architect is someone who is charged with oversight of the entire data footprint associated with an organization or is charged with some defined portion or data domain within that enterprise (in a very large organizations). The Data Architect is typically involved in Data Strategy, Data Design, Data Governance and sometimes data development and helps to ensure that the organization gains the most value possible from their data resources.” This definition is necessarily a bit broad, but the idea is that this role allows one person to act as the Point of Contact (PoC) across many systems, data issues and groups within an organization. The exact mix of associated data roles depends on the size of the organization; but from an industry perspective, there are probably in the neighborhood of around two dozen additional data-specific roles that are often used in a typical enterprise. This covers everyone from Database Engineers, to ETL development and BI report designers, etc. Several years ago, there was a surge of “Chief Data Officers” or CDOs being hired across industry, but this has since tapered off. A CDO (role) is really an extension of the Data Architect role with more focus on strategy and less on design.

How is AI Impacting Data Architecture?

Notice I didn’t say “how will…” – that’s because it’s already happening. AI is changing the practice Data Architecture in real time. This is happening from several perspectives:

1. Analysis & Data Augmentation

2. Data DevOps Automation

3. Introduction of new AI-driven capability

The Data Architect is involved in each of these areas as both the main advocate and key oversight resource. We’ll look at each of these in turn.

Data Analysis – Historically, this has been an onerous task in large enterprises with highly complex systems and / or lot’s of systems to deal with. In this context, AI empowers existing roles w/o total replacement and makes completion of these tasks more likely and the resulting designs more effective. Data Design both within systems and across systems is becoming increasingly automated, though and this will reduce the need for Data Modelers & Database Engineers over time. While the Data Architect is sometimes involved in directing or participating in data design and definition, less of this will be required now due to AI.

Data DevOps Automation – This spans various areas of development with a large focus on testing and configuration alignment. Much of this can now be automated and less oversight will be required here than has been required historically.

New AI-driven Capability – At first, this represents additional work for the Data Architect as he/she will be responsible for determining which AI capabilities to adopt and how to integrate / rationalize them within the enterprise. Later, some of this work will be done by the AI’s themselves. In this context, Data Architects might also be considered AI Architects given the extraordinary amount of overlap that will occur (but only for a limited time).

Ultimately, AI tools will allow Data Architects to accomplish more than they did before, but over time their duties will be diminished and begin to mirror more of the previous CDO role that has largely vanished. As Architect / CDO, the main focus will likely become more centered around interaction with Business leadership to allow them to do some data work directly and also continuing the enterprise oversight role with fewer human roles supporting – but still remaining answerable to leadership for the full range of activities & capabilities in the enterprise associated with data.

This view covers the next five years or so for Data Architects. Things are likely to change further when looking 5 to 10 years out. It’s entirely possible that all IT Architecture-related roles may disappear somewhere around 10 years out. Why? Because the extreme levels of automation being introduced are eliminating the very complexity that required IT Architects in the first place. This prediction assumes that no serious attempt from an industry perspective to save certain job roles will happen (in order to ensure that humans “stay in the loop”), which certainly seems to be the path that we’re on now.

Copyright 2025, Stephen Lahanas

(a typical) Assessment

Most Assessments examine some discrete program, process/es and / or their associated systems and data in detail. While the interactions and interconnections with external entities (e.g. systems, data, processes, programs etc.) may be tracked in this context, the goal is not to understand those external elements in any detail per se, but rather to document the interactions in relation to impacts on the one focus area being studied.

An Enterprise Assessment

An Enterprise Assessment may not encompass an entire organization, but at minimum looks at a domain or grouping of related systems, processes, programs etc. An example of this might be an examination of all Human Resources (HR) related systems and processes within a single organization (for example, a large company or perhaps a Government Agency). In this context, the interactions become more important than the individual elements per se, as the goal is to understand the synergistic impacts of various combinations of capabilities and influences. Enterprises outside of the core enterprise are tracked here in much the same way as external systems are in a typical assessment.

A Meta-Assessment

This type of Assessment is the most complex of all three and can take several forms; a) it can be done as a specific analysis of the relationships (within a specific domain) across enterprises. So, for example, it may look at the entire HR industry in relation to trends, systems and processes. There are various research companies that do nothing but these types of studies, (Gartner, for example); b) the second type of Meta-assessment is when you’re dealing with a Meta organization. For example, the Department of Defense could be considered a Meta organization, being split into 4 to 7 branches now (depending on how you count them) and of course it is in itself part of a larger Meta grouping of US Government agencies. There are one or two other variations, but these two scenarios cover most Use Cases.

In the context of DOGE, the type of work that they should be doing would fall under both the 2nd and 3rd type of Assessment described above. As reported in many quarters, none of the types of Assessment described above are actually occurring right now at DOGE. There are several clues that can help to verify that this contention is true; including:

1. The work being done so far has been far too rapid given the complexity of the programs and organizations being reviewed. In some cases, decisions have been made after only week or two of examination, if that. In the real world, the absolute fastest Assessments that apply any level of Due Diligence would require a minimum of 90 days.

2. There is no stated (and / or published) methodology showing how their work is being done. This is highly irregular and of course ensures that there is little or no consistency across assessments - and given the news coming out we can assume that there are perhaps dozens of simultaneous efforts underway.

3. Far too many basic mistakes are being made (and with little regard for potential negative impacts). This last point has the potential to negate any benefits from the cuts being made.

4. And there is far more cutting occurring than restructuring. Cutting programs is always easier than fixing programs. In theory, one doesn’t have to understand an organization or program in order to simply ax it. Fixing an entity is far more challenging and always requires at least a basic level of understanding as to what the current structure actually does.

The Big Question

If an agency like DOGE were to be chartered to provide Government-wide assessments to help facilitate its newly defined mission (remember the one they haven’t defined yet), is such a mission even possible? And if it were possible, how would something like this, (Enterprise Assessments & Meta Assessments across the entire Federal Government), be structured and managed?

The short answer is yes, this is possible, but no, not under the current Elon Musk-led paradigm. There are certain basic requirements that need to be met in order to do something like this and actually succeed:

1. A real Charter (that would include a new Mission Statement combined with Congressional authority, not just an Executive Order).

2. A real Methodology - one that lays out all of the various Use Cases that must be met with well-defined workflows for each type of Assessment. There also needs to be realistic ROI-based business case and financial analysis done here. And of course there needs to be well managed and well-defined projects schedules encompassing all of the current and planned assessments.

3. Serious, skilled employees (this is always important and is currently one of the biggest problems with the current incarnation of DOGE). Bringing in under-educated minions of Musk with little or no Government-related experience just isn’t ever going to cut it.

4. A definition of Success for each examination as well as for the agency as a whole (Just saying everything is cut doesn’t necessarily equate to savings or value for the people of the United States). Defining successful outcomes for Health Human Services (HHS) for example would need to be linked to providing more and / or better health care to citizens. If cuts were to reduce either or both of those in easily measurable ways, then it's absolutely not going to be successful.

5. Time - Taking the necessary time is the most critical requirement of all. While many may argue that it would take years to do thorough Assessments across Government; that’s not necessarily true. While situations like those have certainly occurred in Government many times, the Assessments themselves can be designed to be both Agile and detailed enough to to support intelligent decision-making.

We will take a look at this last point in the next article in this series.

Copyright 2025, Stephen Lahanas

This week we got a partial glimpse into the types of action that the United States might consider to be acts of Cyber Warfare. I had written about this topic 2 weeks ago in regards to Voting Integrity in the face of Russian cyber attacks, but the story has escalated since then – culminating this week in direct accusations against the Russian government. The CIA and even President Obama have directly implicated Putin as being personally involved with the deliberate aim of swaying the 2016 election. In a year of big stories, this may have been the most far reaching in its implications. One of those implications, which has already been alluded to by many in Washington, is that this act may in fact represent a form of Cyber Warfare.

So, what exactly does Cyber Warfare mean and how does it differ – if at all – from Cyber Terrorism? That’s a tough question, one that I’ve not seen answered clearly before. Cyber Terrorism can come from nation states, such as China, North Korea, Iran and so forth, but one might expect that actions perpetrated by nation-states are less like terrorism per se and more like warfare. It is worthwhile at this point to step back into the not too distant past and bring up a similar question that also still applies here – what’s the difference between a “Cold” and a “Hot” war? The Cold War, as you might remember, involved a whole host activities from espionage to proxy wars. The Hot or real war between the super-powers never occurred and it didn’t happen primarily because of the concept of Mutual Assured Destruction through use of our nuclear arsenals. In that case, the distinction between the terms also involved both the nature of the participants as well as the types of activities involved which is similar to the current question.

None of this really helps though to clear up the confusion regarding what is or what isn’t Cyber Warfare. Here are a few reasons why:

• Cyber Warfare can be both covert and overt – depending on the nature and intent of the attacks as well on the determination as to whether they should be publicized in any way.

• Cyber Warfare could be conducted by both Nation States and Terrorist organizations. The key distinction here though would be that we wouldn’t necessarily classify acts committed by smaller unknown groups or even individuals as Cyber Warfare. In those instances, the term Cyber Terrorism might be more applicable. However, it is also clear that in Cyber Warfare, as in traditional warfare, non-nation state organizations can and have conducted offensive operations.

• Cyber Warfare can be a standalone or blended activity (e.g. coordinated with other traditional war-fighting activities). It’s conceivable that an entire conflict could be fought solely within the Cyber Domain. Cyber “Domain” here refers to the notion that Cyber represents one of several potential war-fighting domains such as Land, Sea, Air and Space. The US military formally acknowledged Cyber as such a domain with its creation of US Cyber Command several years ago. Of course the reality of this statement is more complicated than it sounds as Cyber also infiltrates all other warfare domains through the technology implied by it – it is cross-cutting domain and even if an attack were completely limited to Cyber actions it is highly likely that physical capabilities (such war-fighting assets as ships, planes etc.) might be impacted.

• Cyber Warfare can be directed at the Government or the Industrial Base or both. We can’t say for example, that all attacks against businesses must be considered Terrorism per se – the intent is what’s important. If the intent of an attack is to cripple the country that’s been targeted, then a Cyber attack like that is no different in principle from the types of bombing raids we conducted against Germany in WW2 in order to cripple its industrial base. Today though, the sectors that are perhaps more vulnerable might be Energy and Finance as opposed to Manufacturing. The results might be the same though if the goal is hobble an economy or otherwise disrupt a nation state.

Now, we are ready to consider what the distinctions between Cyber Warfare and Cyber Terrorism really are. They would likely involve the following considerations:

1. Cyber Warfare must necessarily consist of a sustained campaign of Cyber activities, designed to disrupt any mission critical functions of an enemy at a national level. This doesn’t mean the activities have to occur in many places to effect a national impact, it merely has to be designed to impact an opponent that way (and would also likely encompass more than one attack or incident).

2. Cyber warfare must necessarily occur between substantial Cyber combatants. The nature of what constitutes a ‘substantial’ combatant lies in what resources they have to bring to bear in any given conflict. A well-established terrorist or rebel group may have the money and personnel to manage sustained attacks. However smaller groups with few resources may only be able to sustain limited operations or a single attack. While there is always the possibility that an individual or a small group might be able to do harm at the national level, it is unlikely that they could sustain this over months or years and it would be more akin to one-off terrorism than warfare in the context of sustained operations and likely outcomes.

3. Cyber warfare, in general, involves more specific objectives in contrast to Terrorism which is often random in nature and may only be focused on making a statement rather than effecting some desired outcome.

By these definitions, I’d have to say that the Russian hacking of the DNC computers and related activities designed to impact the 2016 election falls under the category of Cyber Warfare rather than Terrorism. And this begs the question, why does all of this matter and why do we need more specific definitions? The bottom line is, that if we don’t have a clear idea of what represents acts of Cyber warfare (either covert or overt), it’s highly likely we won’t be able measure our response properly. Deciding how to respond is obviously a very big deal – as any such decisions could quickly escalate from the Cyber domain into all the others. Perhaps our government does have all of this worked out, and maybe it’s just too secret for any of us to know about. However, from our vantage point now it’s all bit fuzzy. When the President says “we will retaliate in a manner and time of our own choosing” we basically don’t have a clue to what that really means.

Rather than spend a lot of time speculating as to what our response might be, we can instead highlight some principles that may apply to any such situation. The following principles represent a potential framework that might be used to help deal with Cyber warfare as it continues to evolve.

1. Proactive Awareness – In order to survive or win any Cyber conflict, the nation needs to know when in fact it is under attack. Some attacks are more obvious than others and as the recent election shows, our response can be slow or too late to avoid impacts. Proactive Cyber Awareness is not about hacking into everyone’s cell phones, but rather it is about being able to identify unusual behavior in key systems and sectors across the country (or wherever our interests may be). This means we need more selective and actionable intelligence then we seem to be getting now.

2. Measured Response – This has been mentioned in the news, but as I noted it’s not been explained by anyone (at least publicly) yet. For this to actually work, someone needs to define the measured responses up front rather than assessing each event as if it were the first time it had been considered. The landscape is fairly complicated so this involves a lot of work and some automation. However, it shouldn’t fully automatic any more than our current traditional war-fighting capabilities are – the human in the loop must always be present.

3. Defined Escalation Approach – This is a process and it ought to be built atop the measured responses defined previously, the idea being that whenever or wherever Cyber activities begin crossing over to other areas there needs to be another level of safeguards built in to avoid any type of cascading escalation that could lead to something like a nuclear conflict.

4. Maintain a Consistent Policy - In theory, our management of Cyber war shouldn’t be unique in each potential scenario – there ought to be a consistent expectation as to what will happen if enemies launch attacks against the US. This is a key point in the recent debate over Russia as the situation has also become embroiled in US political differences, confusing the matter. While there will always need to be specific considerations given to certain situations, we should never give an indication to any opponent that Cyber attacks may be permitted without any response coming from the US. This would be an extremely dangerous precedent and helps to explain why the President and CIA made statements this week to the effect that election interference would not go unpunished. Better late than never and like all of warfare, if we're in the game we should build policy around what's necessary to win - as opposed to settling for mere survival. There may such as a thing as a Cyber Maginot Line...

5. Continuous Innovation – This may be the most important point, given the stark reality that it is easier and more cost effective to mount a Cyber attack than it is to defend against one. Despite the billions spent each year in the US across government and the private sectors, Cyber Security breaches and attacks have only become more prevalent and severe. More focus needs to be given to pushing the envelope on innovation to help reduce the current advantages enjoyed by our Cyber opponents. Today, much if not the majority of innovation has come from the attackers and we’ve been playing catch-up. As in every other realm of warfare, the side with the greatest technological advantage tends to win.

It’s anyone’s guess as to whether the current Russian hacking crisis will boil over into something more, but one thing is certain, the age of Cyber Warfare has most definitely dawned.

Copyright 2016, Stephen Lahanas