{"id":743,"date":"2026-05-24T22:41:50","date_gmt":"2026-05-25T02:41:50","guid":{"rendered":"https:\/\/constantinpoindexter.com\/blog\/?p=743"},"modified":"2026-05-24T22:41:52","modified_gmt":"2026-05-25T02:41:52","slug":"narrative-engineering-for-deception-in-the-age-of-a-i-yes-but-with-some-caveats","status":"publish","type":"post","link":"https:\/\/constantinpoindexter.com\/blog\/narrative-engineering-for-deception-in-the-age-of-a-i-yes-but-with-some-caveats\/","title":{"rendered":"Narrative Engineering for Deception in the Age of A.I.? Yes, but with some Caveats"},"content":{"rendered":"\n

Henry W. Prunckun’s Narrative Engineering for Deception (NED) framework offers a disciplined architecture for thinking about operational deception. His recent piece, Engineering Plausibility in Deception Operations<\/a>, <\/em>published in the International Journal of Intelligence and CounterIntelligence, is just another in a long list of robust scholarship on intelligence and counterintelligence matters, many volumes of which I had the pleasure of reading and citing during my Master’s Degree in Intelligence studies years past. By treating plausibility as a behavioral threshold rather than an epistemic state, and audit resilience as expected time to falsification, Prunckun reframes the deception planner’s work as a problem of structural engineering rather than improvisational craft. The seven levers he identifies, namely point of view, coherence, persona, pacing, texture, cognitive alignment, and exit strategy, provide a vocabulary that is parsimonious AND operationally tractable. The framework deserves the respect it has begun to receive in professional literature.<\/p>\n\n\n\n

The framework was calibrated for an operational environment that is already disappearing. Verification in the twentieth and early twenty-first centuries was human-paced, institutionally siloed, and bounded by the labor cost of cross-domain correlation. Each of those constraints has now collapsed under the combined weight of machine learning, aggregated commercial datasets, and what the United States Intelligence Community now formally designates Ubiquitous Technical Surveillance (Office of the Director of National Intelligence 2024). The thesis of this essay is therefore qualified rather than dismissive: NED remains a coherent and useful design discipline, but several of its central levers invert under machine audit, and the framework requires recalibration toward operational ephemerality rather than durable cover.<\/p>\n\n\n\n

The Pre-A.I. Verification Environment that NED Presupposes<\/strong><\/p>\n\n\n\n

NED’s levers presume a particular kind of adversary. The adversary is intelligent, motivated, and possibly skilled, but is also resource-constrained. The adversary tires. The adversary works within bureaucratic procedures that batch information rather than fuse it. The adversary’s analysts each see a portion of the picture, and the seams between their views are exploitable. Prunckun’s pacing lever, for instance, exists precisely because human auditors trade thoroughness for speed under workload pressure. His texture lever exists because mundane detail saturates limited human attention. His cognitive alignment lever depends on biases (anchoring, fluency, confirmation, closure) that are characteristically human (Kahneman 2011).<\/p>\n\n\n\n

These presumptions described real adversaries for most of the twentieth century. The operational histories Prunckun draws on, Argo and Operation Copperhead, succeeded against human pattern recognition operating without machine support. Tony M\u00e9ndez exploited the bureaucratic fluency of customs officers reviewing paperwork at human speed (M\u00e9ndez and Baglio 2012). M.E. Clifton James exploited the visual heuristic that famous generals look like themselves (Crowdy 2008). Both operations would have run differently, and arguably failed, against modern fused intelligence.<\/p>\n\n\n\n

How Machine Audit Screws with the Levers<\/strong><\/p>\n\n\n\n

The transition from human to machine-assisted verification does not merely accelerate audit. It changes its kind. Five of Prunckun’s seven levers face structural inversion.
Texture inverts most sharply. Prunckun treats mundane detail as camouflage, on the premise that ordinary noise occupies the auditor’s attention without rewarding scrutiny. Under machine audit, metadata is more legible to algorithms than to humans. Aggregated commercial datasets, including those acquired in bulk by foreign services, permit rapid correlation of timestamps, registries, travel records, financial transactions, and social graphs (Sherman 2025). What was once camouflage becomes signal density. The receipts, routine emails, and habitual patterns that grounded a cover identity in the human era now feed anomaly detection systems that thrive on exactly such structured exhaust. The Federal Bureau of Investigation’s recent Office of Inspector General review found UTS to constitute, in plain language, an existential threat to operational security across U.S. federal law enforcement (Department of Justice Office of the Inspector General 2025).<\/p>\n\n\n\n

Coherence faces a parallel inversion. Prunckun’s lever assumes coherence is tested within a domain by an analyst with limited cross-domain access. Machine systems test coherence across domains simultaneously. A timeline that is internally consistent across an identity’s documentary record may nonetheless contradict ambient cellular geolocation, satellite imagery cadence, or financial network rails the planner did not know were being correlated. Former Central Intelligence Agency Deputy Director for Science and Technology Dawn Meyerriecks publicly acknowledged in 2018 that in roughly thirty countries, CIA officers were no longer followed by local services because surveillance density made physical followers redundant (Dorfman 2020). The point generalizes: the auditor’s view has become panoramic in ways no individual analyst could previously achieve.<\/p>\n\n\n\n

Persona degrades under stylometric, biometric, and behavioral pattern analysis. Prunckun lists idiolect as a persona strength, reasoning that distinctive speech patterns reinforce credibility under contact. Machine learning systems now cluster on idiolect as a fingerprint (Const\u00e2ncio et al. 2023). Facial recognition, gait analysis, voice print identification, and writing pattern matching collectively render alias work, in former intelligence officials’ assessment, likely impossible across hard target environments such as China, Russia and perhaps Iran within the next operational generation (Dorfman 2020). The persona that survives casual human interrogation may yet fail silently against a database query the target never visibly executed.<\/p>\n\n\n\n

Pacing loses its purchase. The lever depends on the gap between heuristic and deep verification, the seam through which an operation completes its objective before slow checks catch up. Machine systems do not batch, do not tire, and do not have a casual inspection mode. The seam narrows toward zero for anything machine auditable. Verification that previously took days now runs in seconds, and runs continuously rather than on prompt.<\/p>\n\n\n\n

Cognitive alignment, the lever Prunckun treats as cross-cutting, breaks hardest against algorithmic auditors. Anchoring, fluency, confirmation, availability, and closure are human cognitive shortcuts. A trained classifier does not weight early evidence more heavily simply because it arrived first, does not find fluent narratives more credible, and does not seek closure in ways that suppress further inquiry (Const\u00e2ncio et al. 2023). The bias terrain on which Prunckun’s framework operates is, at the machine layer, largely flat.<\/p>\n\n\n\n

The Levers that Survive, and Why<\/strong><\/p>\n\n\n\n

Two levers retain force under machine audit, though with shifted emphasis.
Point of view survives because what an auditing system checks is determined by what an operation triggers it to check. A well-constructed narrative that does not enter high-resolution surveillance regimes, or that does not trip the thresholds that escalate routine screening to a targeted investigation, may still slip through. Footprint minimization is the modern expression of the point of view lever. The planner shapes not the auditor’s interpretation of signals but the auditor’s decision to collect them.<\/p>\n\n\n\n

Exit strategy survives, and arguably becomes more important. Where machine audit makes durable cover increasingly infeasible, the disposability of an operation becomes a planning variable rather than a contingency. The National Security Commission on Artificial Intelligence warned explicitly that adversary adoption of AI tools makes the U.S. Intelligence Community more vulnerable to deception, source exposure, and counterintelligence pressure, and the Commission’s framing implicitly favors operations designed to complete and dissolve before correlation catches them (National Security Commission on Artificial Intelligence 2021).<\/p>\n\n\n\n

The Two Audience Dilemma<\/strong><\/p>\n\n\n\n

A critical refinement is required. Even in an environment of machine audit, the ultimate target of most deception operations remains a human decision maker. Machine systems flag anomalies; humans decide whether to escalate, act, or stand down. The behavioral threshold that NED targets is still crossed inside a human head, and the cognitive biases that align with the lever framework still operate at that level.<\/p>\n\n\n\n

So, modern deception is a two-audience problem. The first audience is the machine filter, which is governed by data, correlation, and statistical anomaly. The second audience is the human decision maker downstream, who is governed by attention, narrative, and the cognitive shortcuts NED was designed to exploit. The framework retains substantial force at the second audience and substantially less at the first.<\/p>\n\n\n\n

This insight reframes the planning problem. The operation must clear the machine filter not by being credible to it but by being uninteresting to it, that is, by minimizing the data footprint the filter has to correlate. Once past the filter, the operation may then engage human cognition through the classical levers of persona, texture, and pacing. Phase compression becomes structural rather than optional. The operation must complete its work in the window between machine flagging and human investigation.<\/p>\n\n\n\n

Generative A.I. and the Offense Defense Balance<\/strong><\/p>\n\n\n\n

The picture is not uniformly bleak for the deception planner. Generative artificial intelligence cuts in both directions. Mandiant has documented threat actors using generative models to produce backstop content for inauthentic personas, including profile imagery, plausible filler text, and language-localized communications that previously required substantial human labor (Mandiant 2024). The Federal Reserve and the World Economic Forum have separately documented the rise of agentic AI fraud systems that create synthetic identities, interact with verification systems in real time, and adjust behavior based on outcomes (Federal Reserve 2024; World Economic Forum 2025). The Office of the Director of National Intelligence has noted, in an unclassified discussion of foreign intelligence service activity, that adversary services now reportedly generate inauthentic communications and synthetic digital identities at operational scale (Goldman 2026).<\/p>\n\n\n\n

What this means for NED is that the framework’s underlying logic, treating narrative elements as engineerable parameters under verification pressure, becomes more rather than less relevant. The arms race is not planner versus machine but planner with machine versus auditor with machine. Defense holds the structural advantage of aggregation, where one anomaly is enough, but offense retains the advantage of needing only a short operational window. Whether the balance favors offense or defense in any given operation is now a function of phase compression, footprint discipline, and exit modularity, all of which Prunckun’s framework already names, though without developing them to the depth current conditions require.<\/p>\n\n\n\n

A Recalibrated NED<\/strong><\/p>\n\n\n\n

What survives the transition, then, is the framework’s grammar rather than its operational parameters. The constructs of plausibility as behavioral threshold and audit resilience as time to falsification remain valid and arguably become sharper under machine audit, where falsification windows are measurable in hours rather than months. The seven levers remain a useful vocabulary for design discussion, though several require inversion in their operational settings. The phase model retains explanatory power, though its emphasis shifts decisively toward the withdrawal phase.<\/p>\n\n\n\n

The honest version of post-A.I. NED might be like the following. Assume verification windows in hours or days, not months. Build for phase compression. Minimize footprint to the bone, accepting thinner cover in exchange for fewer correlation points. Design the exit before the entry. Treat texture not as camouflage but as a controlled liability whose volume must be calibrated against the data exhaust it generates. Assume that idiolect, gait, and metadata signatures are identifying even when the underlying identity claim is uncontested. Treat the human decision maker as a distinct audience from the machine filter, and design lever settings appropriate to each.<\/p>\n\n\n\n

Prunckun has given the field a vocabulary it badly needed. Aside from just loving this guy and appreciating the depth and breadth of his knowledge in our subject matter, the discipline of treating deception as engineering rather than as intuitive craft will outlast the particular operational environment in and for which he is currently articulating the framework. What changes is the calibration. The durable, texture-rich, persona-deep cover that worked against human auditors in the bureaucratic era is increasingly infeasible against A.I.-assisted verification in a world of ubiquitous data exhaust. What replaces it is something closer to operational ephemerality, . . . thin, fast, disposable narratives engineered to complete their behavioral work before correlation completes its analytic work. NED in the age of A.I. is not obsolete. It is, however, a different framework than the one initially described, and the gap between the framework as written and the framework as required is the territory in which the next generation of operational deception is going to be contested.<\/p>\n\n\n\n

~\u00a0C. Constantin Poindexter, MA in Intelligence, Graduate Certificate in Counterintelligence, JD, CISA\/NCISS OSINT certification, DoD\/DoS BFFOC Certification<\/a><\/p>\n\n\n\n

Bibliography<\/strong><\/p>\n\n\n\n