March 23, 2016
Assailing IC ITE: Exposing the Limitations on Combinational Information Designations
Douglas Paul Rose
For one to de-construct the function, ability, and flexibility of the Office of the Director of National Intelligence (ODNI) as it relates to the ability of that office to facilitate anticipated future needs of information sharing, it is necessary to embrace the notion that current impressions and metrics likely represent a high degree of inaccuracy when used in a predictive model. Today’s varied and violent data movements with the associated methods and machines utilized in their dissection are multi-dimensional by today’s standards while remaining arguably flat when viewed through a historical lens. Even so, designation of and focus on information requires significant rigor that the current state of these efforts like the Intelligence Community’s Information Technology Enterprise (IC ITE) openly disregards.
The questions of how and if are distinct and representative of varying levels of requirements and standards; a distinction of great importance and representative of a required synchronicity at the national level in any asymmetric assessment even outside of their effectiveness within an information domain. This constant considers the lack of implied weight amongst distinct queries, and depends on the lack of succession as a prerequisite surrounding the advancement of any argument either for or against the opinions and positions aligned within the following pages. Any study such as this considers standardized offerings such as training, policy, relationships, definitions, and overall effectiveness while intimating or stating specific limitations underlying all. Given the varied directions, requirements, and mandates of the body of agencies subordinate to ODNI these filters are only placeholders for a more strategic view centered around the physical state of information before sharing, however there are logical lines of thought that challenge the validity of the current information constructs that ODNI embodies.
In this sense, the concentration of application, intelligence, and effort represents strategic management of the intelligence enterprise[i] as the principal desired output with all other details, judgements, and derived value placed in separate categories. Despite the lack of required order, inclusion of both sides of this discussion holds substantive value since similar planes exist within discussions regarding prosecution of big data analysis with situational connectivity meeting short term need. This is representative of an inclusion which is subject to the same standards already mentioned here, and the relationships between so-called enterprise level struggles are singular and duplicitous in parallel; the alignment of information or data at the core of both these domains makes them relative to each other with each requiring inter-disciplinary consideration to advance some semblance of knowledge.
Meeting the organic needs of any of the intelligence community members is best done within the channels of the operational knowledge and experience of individual agencies with their own structures for training, analysis, data storage, organization, and presentation. This latter point speaks specifically to the disadvantage of importing information or forcing adoption of another framework developed with foreign logic. Acceptance of misuse is implied, and sans a community-wide standard for training and analysis – a premise that is unattainable given individual variances in personal ability and perception within the analytical workforce – placement on some reactive scale is guaranteed.
In order to demonstrate this example, this line of inquiry has to itself regress and temporarily remove the designations of data and information herein introduced and discuss the effective elements of process itself. In a technologically advanced environment where impression and perception can shift with the swipe of a finger, process matters and requires clarification at the point of effectiveness; not at the enterprise level. David Astin considers some of these points in his book Top Secret America: The Rise of the New American Security State:
“…policymakers are flooded with marginally informative and redundant conclusions, and major challenges exist in processing the enormous volume of intelligence gathered. Data is often outdated by the time it arrives to the appropriate decision-making entity, thus making it essential to develop an efficient processing, exploitation, and dissemination cycle capable of culling the most essential intelligence elements. Unfortunately, the emphasis appears to be on the development of technology and equipment rather than the means that direct such technology to the proper end”[ii].
ODNI and IC ITE
The heart of ODNI’s vision of a future integrated intelligence enterprise are the stated goals for the IC ITE which began in 2013:
Goal 1: Enhance Intelligence Integration
Promote the Intelligence Community’s ability to integrate and unify intelligence activities by fully leveraging IC ITE.
Goal 2: Optimize Information Assurance to Secure and Safeguard the IC Enterprise
Enhance Intelligence Community (IC) mission success through a trusted collaborative environment while protecting national intelligence information, sources, and methods, as well as privacy and civil liberties.
Goal 3: Operate as an Efficient, Effective IC Enterprise
Achieve an IC ITE operating model that employs common business practices and Community teams to deliver, adopt, and sustain shared enterprise services and capabilities across the IC[iii].
Published benefits surrounding IC ITE exceed these goals and appear to move beyond what one might consider physical architecture, however weaknesses in the approach remain. The IC ITE Fact Sheet on ODNI’s website touts the following methodology:
The IC ITE represents a strategic shift from agency-centric information technology (IT) to a common enterprise platform where the IC can easily and securely share technology, information, and capabilities across the Community (ODNI, 2016).
Focusing on the shortcomings or successes of information sharing efforts may be misplaced since classifying it as a discipline lacks developed, substantive parallel methods for overcoming issues that analysis itself struggles with such as structure and bias. The requirement to mechanically share information versus the need to increase collaborative access to data that exceeds machine-level structure does not equate to information sharing and in fact, forces adoption of destructive assumptions since the chief export is only representative of one side of this debate. Thoughtful organization prefaces analysis within cultural channels and ICITE removes this ability from proper point of assembly.
Said another way, the application and introduction of a finished product into an environment beyond where it was originally developed is reactionary only and does not equate to leveraging dynamical systems in developing predictive models as an output. Meeting the organic needs of any of the intelligence community members is best done within the channels of the operational knowledge and experience of individual agencies with their own structures for training, analysis, and data storage, organization, and presentation. IC ITE anticipates this conflict and deliberately dismisses it in the name of a smaller technological bottom line.
In itself, this process is enduring since disparate efforts centered on re-organization, definition, and designation of datasets and metadata is what limits most enterprises. As a function of simple existence, this internal limitation is magnified exponentially when exported across the national security environment. The structure represented within the intelligence community’s information and data sharing abilities are not unique. The Journal of Dental Hygiene explored the feasibility of “Virtual Communities of Practice” and found benefits of targeted distribution of data included “access, mentorship, professional development, and problem solving”[iv]. This example is particularly relevant because of the relationship to data and problem solving within these Communities; an assumption ODNI makes prematurely based on the misapplication of filters, logic, and technological dependence.
This asymmetric information designation is not something ODNI embraces by its adoption of IC ITE as the organizational method of choice. Finer points within asymmetric response models demonstrate that effective leveraging of portions of a system requires unemotional application of effectively organized components within specific boundaries. In essence, the flexibility of an open system and its ability to work within certain and uncertain frameworks [v] acts as a natural boundary without introducing unnecessary entropy from a systemic standpoint. Information sharing on its face is too vague of goal for any organization and given the organic requirements of interpretation, perception, and organizational success in sharing is highly situational and fluid. Without commentary on, or standardized requirements for, deliberately introducing systemic restrictions ODNI’s overall picture seems incomplete.
Data vs. Information
The surface considerations such as those represented by the Director of the National Geospatial Agency (NGA), Mr. Robert Cardillo begin to frame the lack of distinction between two terms that seem interchangeable within the IC ITE framework: information and data. In an interview from with C4ISR, Cardillo notes “…activity-based intelligence makes some assumptions that while that extraction still must be done, computers more often than not will be able to do it in the future. The source material is getting more and more conversant with that computer language” [vi]. The usage of the term activity here is unclear; especially given the dependency on computerized linkages. While this is not an effort to delve deeper into classified linkage methodologies, or impose a universal overlay on specific types of events, activities by themselves are data and the fact that their fusion depends principally on assumptions is a flaw. These observations are particularly relevant since Cardillo was the first Deputy Director for Intelligence Integration responsible for overseeing the initiation of the IC ITE framework. Cardillo concludes his comments here by saying “…the computer will then compute the data, and it will test the proposition just like we've always done in intelligence. The result is determining whether that assumption is valid or not when it is tested against different conditions. That testing results in the impression that we’ve generated new information” [vii]. Referencing the above point surrounding the defects in this approach, the placement of the assumption in this process is key where such connections contribute to the application of tags meant to organize data and inform sharing. In a separate article another representative of NGA asserts they “…envision a future where analysts may live within the data”[viii] in order to leverage the full body of knowledge that IC ITE represents. Cardillo’s assertion that the point in which data is transformed into information solely on the basis of an algorithm completely discards physiological, perceptional, and social impacts espoused within the discipline of analysis. Both sides of the intelligence problem depend on some sort of social mobility; the analyst with their peers and an actor amongst their actions. Removing the ascribed and achieved characteristics [ix] by reducing the analyst’s awareness to that of an automaton precludes accurate identification of the problem on a social level and prefaces discussions on information vs. data. In this sense one is left to conclude sharing of either is intellectually premature.
Opening comments about the value of historical information in modern intelligence analysis intersect IC ITE at this point in the study. While there are any number of layers and controls within the overall intelligence community which IC ITE will enforce in some fashion, those controls have limitations which speak directly to the categorization of any practice and its designation as either facilitating sharing of content as either information or data. The examination of ODNI documentation surrounding Originator Controlled (ORCON) access within a broader sharing environment reveals the following:
The Access Control Encoding ORCON specification furthers IC Enterprise goals by codifying mappings and combinational logic between data attributes and user/entity attributes to facilitate consistent enterprise-wide Boolean access decisions. Historically, access control decisions have been made in local environments based on local interpretations of agreements and policies resulting in decisions that are not uniform across the entire enterprise. [x]
Certainly, ORCON material is only one representative sample of what the overall intelligence sharing environment subsumes, however the fact that this control overlay “…provides no backward capability”[xi] prevents the application of the information label within IC ITE clouds and repositories based on what the system concedes is an incomplete capacity. Mechanically dismissing the reason behind the establishment of this framework breeds ignorance and introduces risk at the strategic level without a requirement to complete the construction of an information picture before it is queued up for exportation. Intelligence analysis relies upon such access and maintenance of knowledge – not data or information – and any system that depends on such ingredients requires “…actions within it to have random and unpredictable components that explore a larger space of possibilities”[xii]. The fact that the ORCON control mechanism restricts and even eliminates such movement within the system that is the modern sharing environment restricts said output to the data designation. Further, it impedes movement within what is supposed to be a dynamical environment by interfering with the development of information, thereby forcing the use of cognitive biases that Richards Heuer specifically targets in his basic training offerings for the Central Intelligence Agency:
Cognitive biases are mental errors caused by our simplified information processing strategies. The apparent distance of an object is determined in part by its clarity. The more sharply the object is seen, the closer it appears to be. This rule has some validity, because in any given scene the more distant objects are seen less sharply than nearer objects. However, the reliance on this rule leads to systematic errors in estimation of distance. Specifically, distances are often overestimated when visibility is poor because the contours of objects are blurred [xiii].
Even if one holds the ORCON model less restrictive than implied here in light of Cardillo’s comments and this analysis, the resultant information is still representative of a product rife with cognitive limitations and flaws. If this focus exists outside of this declaration, the degree of control stripped away from a strategist working on an enduring or emergent problem such as warning is alarming and endures beyond the imposed structure. Outside of the enduring debate on how any entity possesses warning, an analyst cannot get to the point of delivering it without the ability to conduct exhaustive research. If failures in warning are due to the delayed arrival of information,[xiv] then blind application of machine logic serves as nothing other than an advanced stove piping mechanism.
Extrapolating this example to the exportation of data makes the crevasse between actionable intelligence and accuracy even larger if all other assumptions remain constant. The damaging results of either of these designations in this case is the lack of awareness of an analyst using said output even if he or she possesses the cognitive ability to conceive these limitations. There is a chance these hurdles are mitigated by historical personal knowledge on the part of the same analytical community, however since this paper demonstrates the technological limitations to the viewing of historical knowledge, that ability is a risky crutch at the enterprise level. Not to mention, the likelihood of that possibility diminishes over the course of time beyond a certain point in time as the elder workforce removes itself from circulation. Even a crop of analysts or data scientists who progress professionally while only knowing the IC ITE environment are still subject to the same restrictive environment since evolution of this information, data, or knowledge environment continues to mature in a singular direction.
Conclusion
This means that ongoing efforts to corral big data are misplaced since they must defer to proper identification of information while confining the flow of data where appropriate; adherence to a neutral sharing model without this effort is unmitigated arrogance. Having both and leveraging either in an effective manner is not clearly represented within ODNI’s approaches which equates to endorsement of varied assessments of both usefulness and impact. ODNI accomplishes nothing if it only enables possession while weakening the ability of a post-modern workforce to leverage a purposely limited historical understanding on the road to actually having what can be commonly considered information. The problem with reliance on combinational logic is that it does not fit within typical feedback loops between the points of intake and output which transform designations from data into information[xv]. Combinational logic is basically two inputs with one output; designing a targeted search string or even the use of Boolean constructs to retrieve data requires basing said construction on known information and a limited amount of pre-determined filters when shaping shifting digital systems [xvi]. IC ITE already demonstrates acceptance of historical limitations and the requirement to continually define filters suffers the analyst even if one accepts Cardillo’s advocacy for mechanical information standards. This separation between title and law, self-importance versus need for demonstration of the value, supersedes the naïve assumption that current approaches relating to information sharing will succeed.
Abstracts are required in multi-dimensional environments and prevented by pre-established, unemotional Boolean impositions. The information landscape needs to move past the idea that infrastructure and mandated platforms will make any reasonable headway toward more effective interagency transparency. Determining which one represents a more advantageous approach is not and cannot solely be a decided outside of cognizance of the state of information and not data as any other approach results in utilization of finished products confined to environments beyond where the underlying geneses exist as underdeveloped pillars.
References
Astin, David W. "Top Secret America: The Rise of the New American Security State." Parameters Autumn 2012: 101+. Military and Intelligence Database Collection. Web. 12 Mar. 2016.
Cardillo, R. (2015). C4ISR, 8. Retrieved from http://search.proquest.com/docview/1697207022?accountid=136858
Cole, G. A. & Kelly, P. (2011). Management Theory and Practice (7th ed.). United Kingdom: Centrage Learning.
Grabo, C. (2004). Anticipating surprise: analysis for strategic warning. Maryland: University Press of America.
Guo, G., & Stearns, E. (2002). The social influences on the realization of genetic potential for intellectual development (*). Social Forces, 80(3), 881+. Retrieved from http://go.galegroup.com/ps/i.do?id=GALE%7CA84341560&v=2.1&u=san44689&it=r&p=GPS&sw=w&asid=79ea47be6e9f04f0b52700ef816c2f94
Heuer, R. (2007). Psychology of Intelligence Analysis. Retrieved fromhttps://www.cia.gov/library/center-for-the-study-of-intelligence/csi-publications/books-and-monographs/psychology-of-intelligence-analysis/art12.html
Long, L. (2014). C4ISR, 8. Retrieved from http://search.proquest.com/docview/1519277435?accountid=136858
McDowell, D., (2009), Strategic intelligence: a handbook for practitioners, managers, and users.Maryland: Scarecrow Press, Inc.
Mitchell, M. (2009). Complexity: a guided tour. New York: Oxford University Press.
National Geospatial-Intelligence Agency. (2016). Robert Cardillo biography. Retrieved fromhttps://www.nga.mil/About/History/NGAinHistory/Pages/RobertCardillo.aspx
National Telecommunications & Information Administration, United States Department of Commerce. (1998). Elements of Effective Self-Regulation for Protection of Privacy – Discussion Draft. Retrieved from https://www.ntia.doc.gov/report/1998/elements-effective-self-regulation-protection-privacy-discussion-draft
Office of the Director of National Intelligence. (2016). IC ITE enterprise. Retrieved fromhttp://www.dni.gov/files/documents/IC%20ITE%20Fact%20Sheet.pdf
Office of the Director of National Intelligence. (2016). ORCON need to know access. Retrieved fromhttp://www.odni.gov/index.php/newsroom/ic-in-the-news/105-dni/about/organization?start=165
Office of the Director of National Intelligence. (2016). What We Do: An Integrated Intelligence Enterprise. Retrieved from http://www.dni.gov/index.php/about/organization/chief-information-officer-what-we-do
Roderick, R. R. (2015, December). Using a virtual community of practice for knowledge sharing among dental hygienists with community practices: a case study. Journal of Dental Hygiene, 89(6), 429. Retrieved from http://go.galegroup.com/ps/i.do?id=GALE%7CA439636268&v=2.1&u=san44689&it=r&p=GPS&sw=w&asid=2e2e95c266f35030ce65ca087ea85a27
Shulaker, M. M., Hills, G., Patil, N., Wei, H., Chen, H., Wong, H. P., & Mitra, S. (2013). Carbon nanotube computer. Nature, 501(7468), 526-30. Retrieved fromhttp://search.proquest.com/docview/1445354912?accountid=136858
Zink, J. C. (1999, June). Information and control merge in new environment. Power Engineering, 103(6), 18+. Retrieved from http://go.galegroup.com/ps/i.do?id=GALE%7CA55174614&v=2.1&u=san44689&it=r&p=GPS&sw=w&asid=cb5ef8cdc5d96420d0e22868dc41d33f
End Notes
[i] McDowell, D., (2009), Strategic intelligence: a handbook for practitioners, managers, and users.Maryland: Scarecrow Press, Inc.
[ii] Astin, David W. "Top Secret America: The Rise of the New American Security State." ParametersAutumn 2012: 101+. Military and Intelligence Database Collection. Web. 12 Mar. 2016.
[iii] Office of the Director of National Intelligence. (2016). IC ITE enterprise. Retrieved fromhttp://www.dni.gov/files/documents/IC%20ITE%20Fact%20Sheet.pdf
[iv] Roderick, R. R. (2015, December). Using a virtual community of practice for knowledge sharing among dental hygienists with community practices: a case study. Journal of Dental Hygiene, 89(6), 429. Retrieved from http://go.galegroup.com/ps/i.do?id=GALE%7CA439636268&v=2.1&u=san44689&it=r&p=GPS&sw=w&asid=2e2e95c266f35030ce65ca087ea85a27
[v] Cole, G. A. & Kelly, P. (2011). Management Theory and Practice (7th ed.). United Kingdom: Centrage Learning.
[vi] Cardillo, R. (2015). C4ISR, 8. Retrieved from http://search.proquest.com/docview/1697207022?accountid=136858
[vii] Ibid.
[viii] Long, L. (2014). C4ISR, 8. Retrieved fromhttp://search.proquest.com/docview/1519277435?accountid=136858
[ix] Guo, G., & Stearns, E. (2002). The social influences on the realization of genetic potential for intellectual development (*). Social Forces, 80(3), 881+. Retrieved fromhttp://go.galegroup.com/ps/i.do?id=GALE%7CA84341560&v=2.1&u=san44689&it=r&p=GPS&sw=w&asid=79ea47be6e9f04f0b52700ef816c2f94
[x] Office of the Director of National Intelligence. (2016). ORCON need to know access. Retrieved fromhttp://www.odni.gov/index.php/newsroom/ic-in-the-news/105-dni/about/organization?start=165
[xi] Ibid.
[xii] Mitchell, M. (2009). Complexity: a guided tour. New York: Oxford University Press.
[xiii] Heuer, R. (2007). Psychology of Intelligence Analysis. Retrieved fromhttps://www.cia.gov/library/center-for-the-study-of-intelligence/csi-publications/books-and-monographs/psychology-of-intelligence-analysis/art12.html
[xiv] Grabo, C. (2004). Anticipating surprise: analysis for strategic warning. Maryland: University Press of America.
[xv] Zink, J. C. (1999, June). Information and control merge in new environment. Power Engineering,103(6), 18+. Retrieved from http://go.galegroup.com/ps/i.do?id=GALE%7CA55174614&v=2.1&u=san44689&it=r&p=GPS&sw=w&asid=cb5ef8cdc5d96420d0e22868dc41d33f
[xvi] Shulaker, M. M., Hills, G., Patil, N., Wei, H., Chen, H., Wong, H. P., & Mitra, S. (2013). Carbon nanotube computer. Nature, 501(7468), 526-30. Retrieved fromhttp://search.proquest.com/docview/1445354912?accountid=136858
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