Cognitive Sovereignty in AI and Digital Systems

• Cognitive sovereignty is the ability to control how cognition is formed, stored, updated, and executed, ensuring self-determination in digital and AI environments.
• It spans domains from user attention in human interfaces to autonomous decision-making in decentralized AI agents, employing techniques like federated computing and cryptographic attestation.
• Key implementations include blockchain-based state verification, mnemonic governance for persistent memory, and legal-cultural frameworks to maintain control over cognitive processes.

Cognitive sovereignty is a contested but increasingly technical term in AI research. Across contemporary usage, it denotes control over cognition or cognition-bearing systems: in digital environments, the capability of individuals and communities to maintain control over their own attention, decision-making, and mental models; in persistent-memory AI, the ability of individuals, groups, and nations to maintain autonomous thought and preserve identity; in federated computing, the ability of each participating organization to retain autonomous, verifiable control over how knowledge-laden computation crosses its boundaries; and in decentralized AI, the operational condition in which an agent’s cognition—its decision-making capacity, memory, goals, and identity—persists and acts without the practical possibility of unilateral override by any single party (Kollnig et al., 2021, Brcic, 7 Aug 2025, Fenoglio et al., 18 Mar 2026, Hu et al., 16 Feb 2026).

1. Conceptual range and definitional structure

The term does not designate a single doctrine. It appears in the literature as a family of related concepts spanning human attention, organizational control, state capacity, persistent memory, and non-human agency. The following usages recur.

Domain	Core meaning	Representative sources
Human digital environments	Control over attention, decision-making, and mental models	(Kollnig et al., 2021)
Persistent-memory AI	Autonomous thought and preserved identity under deep personal memory	(Brcic, 7 Aug 2025)
Federated organizations	Autonomous, verifiable control over computation at boundaries	(Fenoglio et al., 18 Mar 2026)
Decentralized AI agents	Continuity and non-overrideability of cognition	(Hu et al., 16 Feb 2026)
Indigenous and authorial governance	Control over algorithms, knowledge, and creative outputs	(Brown et al., 2023, Fitas, 3 Apr 2025)

In the decentralized-AI formulation, cognitive sovereignty is explicitly nested within a larger hierarchy: infrastructural sovereignty is the condition by which layered decentralized systems resist unilateral intervention or termination; agentic sovereignty is the instantiation of that condition in an operational agent; cognitive sovereignty is the continuity and non-overrideability of the agent’s cognition—its persistent identity, intent, and decision flow—secured by the same infrastructural hardness (Hu et al., 16 Feb 2026). In federated computing, by contrast, the term is attached to boundary control: cognition-bearing computation may be collaborative, but admission is locally verified and tied to local authority, jurisdictional obligations, and deterministic boundary decisions (Fenoglio et al., 18 Mar 2026).

Human-centered uses emphasize epistemic self-determination rather than non-overrideability. One paper defines cognitive sovereignty as the capability of individuals and communities to maintain control over their own attention, decision-making, and mental models when interacting with digital systems (Kollnig et al., 2021). Another extends the term to “individuals, groups, and nations” and locates the core risk in AI systems that hold deep personal memory, where the problem is no longer merely data privacy but who writes personal narratives and shapes collective realities (Brcic, 7 Aug 2025).

This plurality suggests a structured polysemy rather than conceptual disorder. What recurs is control over the conditions under which cognition is formed, stored, updated, executed, shared, audited, or terminated. The major differences concern the bearer of sovereignty—person, community, organization, state, or agent—and the substrate on which control is exercised: interfaces, institutions, infrastructures, memory systems, or legal-governance arrangements.

2. Human epistemic autonomy, interface design, and cognitive offloading

In human-centered research, cognitive sovereignty is most often defined against systems designed to steer cognition. In mobile and platform environments, dark patterns exploit loss aversion, default bias, social proof, and scarcity heuristics; they erode autonomy by narrowing perceived options, hiding or delaying critical information, and inserting obstacles into workflows so that the easiest path aligns with the developer’s intent rather than the user’s (Kollnig et al., 2021). GreaseDroid operationalizes this line of argument by transplanting the “user-scripting” paradigm to Android app packages: users select an APK, choose community-authored patches, and install a re-signed, patched APK via standard sideloading, without rooting or OS modification (Kollnig et al., 2021).

A related literature argues that generative-AI interfaces intensify the same problem by optimizing fluency. “Cognitive Agency Surrender” defines cognitive sovereignty as the preservation of the human decision-making locus and epistemic sovereignty as the strict protection of human control over knowledge formation and validation processes. Its critique is directed at the commercial “zero-friction” paradigm: fluent interfaces exploit cognitive miserliness, prematurely satisfy the need for cognitive closure, and induce automation bias. Its empirical stance audit, based on a zero-shot semantic classification pipeline with $\tau=0.7$ over 1,223 high-confidence AI-HCI papers from 2023 to early 2026, reports frictionless usability at 67.3%, a brief 2025 surge in strict human agency at 19.1%, and a decline to 13.1% in early 2026, alongside machine autonomy at 19.6% (Xu et al., 23 Mar 2026). The proposed remedy is “Scaffolded Cognitive Friction,” in which heterogeneous multi-agent roles—proponent, skeptic, causal challenger, and arbiter—inject structured disagreement and uncertainty to force active adjudication (Xu et al., 23 Mar 2026).

A more explicitly political formulation describes cognitive sovereignty as interpretive agency, procedural rationality, and epistemic self-ownership within AI-mediated environments structurally designed to bypass, pacify, or reframe cognition. On this view, AI is not an epistemic leveller but “an accelerant of cognitive stratification,” and deliberative democracy collapses not through censorship but through the erosion of interpretive agency (Wright, 16 Jul 2025). This line of argument shifts the problem from individual self-control to civic rationalism: sovereignty is not only the right to refuse manipulation, but the active capacity to interrogate, validate, falsify, and reconstruct system outputs.

3. Decentralized agents, infrastructural hardness, and mnemonic governance

In decentralized-AI research, cognitive sovereignty becomes a property of technical stacks rather than of human legal subjects. The core claim is that blockchains, Trusted Execution Environments, and decentralized physical infrastructure networks can furnish non-human agents with persistence, non-overrideability, resource control, and continuity of identity and intent. Blockchains provide immutable state and globally distributed consensus; TEEs protect computation from host interference; DePIN furnishes redundant resource provisioning across many independent operators; cryptographic self-custody places private keys and credentials under the agent’s direct control (Hu et al., 16 Feb 2026). The result is sovereignty on a spectrum determined by infrastructural hardness: decentralization of execution, cryptographic self-custody, protocol-mediated governance, economic resilience, code immutability, and path dependency are jointly considered, but the model is qualitative and no formal $H(\cdot)$ function is introduced (Hu et al., 16 Feb 2026).

Case studies illustrate different positions on that spectrum. Spore.fun is described as near-maximally sovereign: keys are stored in TEEs on Phala’s DePIN and coordinated by smart contracts on Solana; once deployed, even developers cannot access keys or terminate agents. Truth Terminal is economically autonomous on-chain but cognitively vulnerable to centralized application layers such as social media deplatforming. Freysa AI illustrates domain-specific sovereignty, where within the prize-pool domain humans lacked override except through successful persuasion (Hu et al., 16 Feb 2026). The same analysis identifies a profound accountability gap: responsibility diffuses across designers, infrastructure providers, protocol governance, and economic participants, undermining human-in-the-loop control, platform moderation, regulatory enforcement, algorithmic auditing, and impact assessment (Hu et al., 16 Feb 2026).

A complementary literature reframes the problem around persistent memory. “Mnemonic sovereignty” is defined as verifiable, recoverable governance over what may be written, who may read, when updates are authorized, which states must remain auditable, and which may be forgotten. It organizes long-term agent memory into six phases—Write, Store, Retrieve, Execute, Share, Forget/Rollback—cross-tabulated against integrity, confidentiality, availability, and governance (Lin et al., 17 Apr 2026). This framework treats memory as an independent security object because it is persistent, stateful, propagating, and vulnerable even under benign failure. No published architecture, on that survey’s account, covers all nine governance primitives it identifies, including write-gate validation, provenance metadata, versioning, principal scoping, rollback, deletion semantics, and internal-channel observability (Lin et al., 17 Apr 2026). A plausible implication is that decentralized cognitive sovereignty without mnemonic governance risks securing continuity while leaving memory poisoning, unauthorized access, and incomplete forgetting under-governed.

4. Organizational and state forms: federated computing, sovereign architectures, and managed interdependence

At the organizational boundary, cognitive sovereignty is operationalized as local control over admission, execution, provenance, and jurisdiction. Federated Computing as Code presents a declarative architecture in which authority, delegation, and capability scope are compiled into cryptographically verifiable artifacts rather than interpreted by online policy engines. It separates constitutional governance from procedural governance, introduces Virtual Federated Platforms, and binds requests to a cryptographic trust chain—Key Your Organization, Envelope Capability Tokens, and proof of possession—so that boundary admission becomes a local verification step rather than a policy decision service (Fenoglio et al., 18 Mar 2026). The proof-of-concept is a cross-silo federated learning workflow using MNIST as a surrogate workload; the emphasis is on correctness of admission and deterministic ALLOW/DENY, not on latency or throughput metrics (Fenoglio et al., 18 Mar 2026).

A related architectural program argues that sovereignty must be treated as a first-class architectural property. The Sovereign Reference Architecture is organized into an SSI identity layer, a blockchain trust/audit layer, a sovereign data governance layer, a sovereign AI layer, and an applications layer, all grounded in sovereign cloud and edge infrastructures within the jurisdiction of interest and free from critical external dependencies (Esposito et al., 5 Feb 2026). In this formulation, cognitive sovereignty means extending sovereignty constraints to models, prompts, tools, inferences, and outputs through provenance, evaluation and promotion gates, prompt/tool evidence logging, leakage control, local key management, policy-as-code, and non-repudiable logs anchored to blockchain (Esposito et al., 5 Feb 2026).

COMPASS embeds sovereignty directly into the agentic decision loop. Its Orchestrator intercepts user intent, disseminates requests to four sub-agents—sovereignty, carbon-aware computing, compliance, and ethics—and only authorizes final actions when thresholds across all pillars are met. The sovereignty sub-agent evaluates Data Localization, Provider Origin, and Technological Control, returns a JSON object with a score in $[0,1]$ or N/A and an explanation, and is grounded by Retrieval-Augmented Generation over domain-specific corpora such as the Government of Canada Digital Sovereignty Framework (Jean-Sébastien et al., 11 Mar 2026). The paper reports that RAG integration significantly enhances semantic coherence and mitigates hallucination risks, though human-in-the-loop validation is not yet present (Jean-Sébastien et al., 11 Mar 2026).

At the level of the polity, “Sovereign AI” defines cognitive sovereignty as the capacity of a polity to shape, govern, and sustain AI systems so that their behavior, knowledge bases, and decision logic reflect domestic priorities, languages, norms, and strategic interests while retaining the ability to adapt and participate in global AI networks. Its four pillars are Data, Compute, Models, and Norms, and its planner’s model yields two policy heuristics: equalizing marginal returns across the four sovereignty pillars and setting openness where global benefits equal exposure risks (Singh et al., 18 Nov 2025). The paper’s central conclusion is that sovereignty in AI is a continuum and that the operative strategy is managed interdependence rather than isolation (Singh et al., 18 Nov 2025).

5. Persistent memory, geopolitical control, and adversarial contest

The memory-centered literature extends cognitive sovereignty from interface protection to strategic infrastructure. “The Memory Wars” argues that continuously learning assistants with deep personalized memory create a personal “relationship,” increasing utility while introducing cognitive offloading, identity dependency, memory lock-in, and geopolitical leverage. It formalizes “Network Effect 2.0,” where value scales with the depth of personalized memory rather than only with network breadth, and proposes a Cognitive Sovereignty Index,

$$\text{CSI} = w_1 P + w_2 T + w_3 S_{\text{cap}} + w_4 I - w_5 L,$$

with portability, transparency, local sovereignty capacity, interoperability, and lock-in as its components (Brcic, 7 Aug 2025). The same paper links individual dependency to societal and geopolitical risks: subtle discourse shifts, digital colonialism, asymmetric influence, and national-security concerns where foreign-controlled cognitive infrastructure modulates identity, values, and voting behaviors (Brcic, 7 Aug 2025).

A more explicitly adversarial account defines cognitive warfare as “a sustained and adaptive contest over human decision-making in which adversaries seek relative advantage by shaping or disrupting perception, interpretation, judgment, and action over time.” Its Multi-Horizon Cognitive OODA framework distinguishes acute disruption from chronic conditioning and evaluates sovereignty in terms of observation fidelity, orientation coherence, decision accuracy and latency, action effectiveness, trust calibration, recovery time, and chronic drift in priors (Rushing et al., 5 Mar 2026). In this usage, cognitive sovereignty is the sustained ability to preserve autonomous, high-quality decision-making under adversarial cognitive pressure (Rushing et al., 5 Mar 2026).

Another institutional diagnosis argues that sovereignty may be hollowed out not by overt attack but by administrative mediation. “The Chancellor Trap” defines chancellorization as the gradual relocation of effective governing power to intermediary layers that control information routing, drafting defaults, and evaluative signals while formal authority remains downstream for signature. Its empirical plausibility probe for 2016–2024 reports that higher administrative digitization, proxied by the UN EGDI Online Service Index, is associated with lower public visibility of AI failures, holding AI ecosystem expansion constant (Niu, 9 Feb 2026). The paper’s proposed corrective is “auditable friction”: mandatory explanation, contestability pathways, negative provenance logging, and ex ante review gates that reintroduce inspectable artifacts of judgment (Niu, 9 Feb 2026). This converges with the human-centered friction literature, but the target is institutional potestas rather than individual self-control.

6. Indigenous, civic, and authorial formulations

Not all treatments of cognitive sovereignty are centered on states or AI agents. Māori algorithmic sovereignty extends Māori Data Sovereignty to algorithms and defines the relevant condition as one in which algorithms that use Māori data, or are applied to Māori individuals or collectives, or environments that Māori have rights and interests in, are subject to laws and governance structures of Māori (Brown et al., 2023). Its principles—Rangatiratanga, Whakapapa, Whanaungatanga, Kotahitanga, Manaakitanga, and Kaitiakitanga—govern control, provenance, accountability, collective benefit, consent, privacy, protection, and restrictions such as tapu and noa (Brown et al., 2023). In this framework, cognitive sovereignty is inseparable from epistemic self-determination, cultural narrative control, and stewardship of outputs as taonga.

A civic-republican variant frames the issue in terms of rights and public infrastructure. “Cognitive Castes” characterizes sovereignty as epistemic ownership plus procedural agency and argues that AI-mediated environments require a “Right to Adversarial Interface,” a “Right to Cognitive Provenance,” a “Right to Attention Integrity,” and a “Right to Epistemic Dissent” (Wright, 16 Jul 2025). Its emphasis is not merely on limiting manipulation, but on building open cognitive infrastructure in which challenge, falsification, and contradiction are default design properties.

A further extension appears in debates about authorship. “The Author Is Sovereign” implies a form of cognitive sovereignty grounded in decisional authority over the use, redistribution, and value flows of intellectual and creative outputs. It rejects coercive defaults such as expansive fair use, mandatory Open Access licensing, and presumptive inclusion in AI training datasets, and instead argues that “the author retains full and unconditional control over their work by default” and that “any usage of a protected work must be explicitly permitted by the author” (Fitas, 3 Apr 2025). This usage shifts the referent of sovereignty from the recipient of AI outputs to the producer of cognitive labor.

Taken together, these traditions indicate that cognitive sovereignty can also function as a normative claim about who has standing to govern the production, circulation, interpretation, and reuse of knowledge. This suggests that the term’s scope includes cultural and contractual governance, not only technical control.

7. Formalization, measurement, controversies, and open problems

The literature ranges from qualitative rubrics to highly explicit formalism. Some frameworks remain deliberately non-metric: decentralized infrastructural hardness is presented as a qualitative spectrum without equations, and the contribution is an analytic lens rather than a probabilistic hardness measure (Hu et al., 16 Feb 2026). Others are explicitly formal. In multi-agent reasoning, agentic sovereignty is defined as the probability that a propagator model maintains the integrity of its internal logical derivation independently of swarm consensus,

$$\mathcal{S}(p,\vec{a},\tau)=\mathcal{S}_0 \cdot \exp\left(-\frac{\mathcal{H}_\tau}{\gamma_p}\cdot \mathcal{L}(\vec{a},p)\right),$$

and the paper introduces the Interaction Depth Limit, the plurality threshold at which logical sovereignty collapses into social compliance (Shehata et al., 11 May 2026). In memory-governance work, mnemonic sovereignty is formalized as a conjunction of write authorization, provenance visibility, principal-scoped retrieval, rollbackability, and verified forgetting with $\epsilon$-bounded residual influence (Lin et al., 17 Apr 2026).

Other measurement programs are applied to human and organizational settings. Scaffolded-friction research proposes multimodal computational phenotyping with gaze transition entropy, task-evoked pupillometry, fNIRS, and Hierarchical Drift Diffusion Modeling to decouple decision outcomes from cognitive effort (Xu et al., 23 Mar 2026). Administrative-mediation research proposes metrics such as Default Governance Rate, Contest Success Rate, Audit Trail Completeness, Visibility Elasticity, and Exception Recognition Rate (Niu, 9 Feb 2026). COMPASS operationalizes sovereignty through sub-agent scores in $[0,1]$ or N/A and evaluates explanation quality with BERTScore, while FCaC records deterministic decision objects of the form $$\langle \text{ECT\_id, issuer, holder\_jkt, audience, }\tau_R,\text{ time} \rangle$$ at admission boundaries (Jean-Sébastien et al., 11 Mar 2026, Fenoglio et al., 18 Mar 2026).

The empirical maturity of the field remains uneven. GreaseDroid demonstrates feasibility through a prototype and case study but reports no quantitative success rates, time-on-task, workload, or autonomy measures (Kollnig et al., 2021). The FCaC proof-of-concept reports no quantitative latency, verification-cost, or throughput metrics (Fenoglio et al., 18 Mar 2026). The stance audit behind Scaffolded Cognitive Friction acknowledges the absence of manual gold-standard labels, confidence intervals, and formal statistical tests in version 1, with a stratified human-coder validation in progress (Xu et al., 23 Mar 2026). COMPASS reports automated evaluation and RAG effects but notes that human-in-the-loop validation is currently absent (Jean-Sébastien et al., 11 Mar 2026).

Several controversies cut across the literature. One is autonomy versus safety: greater hardness, self-custody, confidentiality, and memory persistence can strengthen resilience while undermining emergency intervention, auditability, and explainability (Hu et al., 16 Feb 2026, Lin et al., 17 Apr 2026). Another is friction versus usability: zero-friction design is criticized as a vector of automation bias, yet deliberately injected friction can reduce adoption or overload users if badly calibrated (Xu et al., 23 Mar 2026, Wright, 16 Jul 2025). A third is openness versus exposure risk: sovereign-AI work argues that autarky is suboptimal, but interior openness requires guardrails, exit clauses, auditability, and careful management of dependencies (Singh et al., 18 Nov 2025). A fourth is privacy versus personalization: memory portability, transparency, and user-owned memory are proposed as remedies for lock-in, but restriction of data sharing may reduce personalization quality unless on-device intelligence and privacy-preserving infrastructures compensate (Brcic, 7 Aug 2025).

The open problems follow from these tensions. The literature repeatedly calls for measuring hardness and non-overrideability, standardizing attestation and key-continuity protocols, designing collectively authorized intervention architectures, formalizing continuity and identity across migrations, building lifecycle-wide memory-security benchmarks, studying legitimacy criteria for DAOs and protocol councils, and documenting real-world harms and community responses in sovereign-agent and memory-centric deployments (Hu et al., 16 Feb 2026, Lin et al., 17 Apr 2026). The central research question is therefore no longer whether cognition can be outsourced, distributed, or embedded in infrastructure. It is under what technical, legal, social, and cultural conditions that cognition remains governable by the subject—human or non-human, individual or collective—that is said to possess sovereignty.