The quantum threat is not a gradual erosion of security margins but what the authors describe as a structural break in cryptographic trust. Classical public-key systems, including the schemes protecting most internet traffic today, derive their security from mathematical problems such as integer factorization and discrete logarithms, which are assumed to be intractable for classical computers. Quantum computers, by exploiting superposition and entanglement, change the feasibility of solving exactly these problem classes in a qualitative rather than incremental way. Once sufficiently capable quantum machines exist, the guarantees underpinning widely deployed cryptosystems simply no longer hold. Symmetric cryptography fares somewhat better, but even its effective security levels must be re-evaluated under quantum-capable adversarial models, which is why researchers are now calling for quantum-safe approaches across the entire security stack, including federated learning systems.

Post-quantum cryptography, or PQC, is the field’s answer to this looming crisis. Unlike quantum cryptography, which relies on specialized physical hardware, PQC schemes are designed to run on conventional computers while resting on mathematical problems believed to remain hard even against quantum attackers. The review highlights that PQC research has produced several distinct families, each grounded in different mathematical foundations. Lattice-based schemes rest on the hardness of high-dimensional lattice approximation problems; code-based schemes on the difficulty of decoding random linear codes; hash-based schemes on preimage and collision resistance; and multivariate schemes on the hardness of solving systems of polynomial equations. Crucially, no single family yet commands universal confidence, which makes cryptographic agility—the ability to swap algorithms as knowledge evolves—a central design requirement rather than an afterthought.

A major milestone arrived in 2024, when the U.S. National Institute of Standards and Technology finalized its first post-quantum standards. ML-KEM, formerly known as CRYSTALS-Kyber, became the primary key encapsulation mechanism, while ML-DSA, formerly CRYSTALS-Dilithium, became the primary digital signature scheme. SLH-DSA, derived from SPHINCS+, offers a hash-based signature alternative built on deliberately conservative assumptions, and FN-DSA, formerly FALCON, was selected as a compact lattice-based signature option for bandwidth-constrained environments. These standards give engineers concrete primitives to build with, but they also introduce practical friction: larger keys, bigger signatures, and heavier computational overheads that ripple through every protocol they touch, including the AI monitoring pipelines that depend on timely data flows.

Meanwhile, the defensive side of the field has been undergoing its own revolution. Threat hunting has evolved from rule-based, signature-driven detection toward intelligence-driven, learning-based analysis. Traditional monitoring assumes malicious activity can be identified through predefined indicators drawn from historical observations—an assumption that collapses in the face of adaptive adversaries who deliberately evade static defenses. AI-driven threat hunting reframes security monitoring as a continuous inference process: models learn representations of normal operational behavior and flag deviations that may signal malicious intent, enabling the discovery of previously unseen threats. The review formalizes this as a decision process mapping observed system states to threat hypotheses, and notes that it can be instantiated through Bayesian networks for transparent probabilistic reasoning, LSTM and Transformer models for capturing temporal patterns in security logs, Isolation Forest and One-Class SVM for unsupervised anomaly detection when labeled data is scarce, and reinforcement learning for selecting defensive actions under uncertainty.

The core argument of the review is that these two pillars are structurally interdependent, yet have almost always been studied in isolation. Cryptographic strength alone cannot prevent misuse, misconfiguration, or insider-driven exploitation; even a perfectly quantum-proof cipher offers no visibility into how a system is being abused. Conversely, AI-driven threat hunting is only as trustworthy as the data it observes. If cryptographic protections are weakened or rendered obsolete, adversaries can manipulate observation channels, poison training data, or obscure malicious activity in ways that quietly corrupt the AI’s judgment. The authors also point to an emerging hybrid threat model in which adversaries combine quantum-enabled cryptanalysis with AI-assisted automation to accelerate reconnaissance and adapt attacks in real time—a scenario that defeats defenses built around single-dimensional assumptions.

To move from argument to architecture, the review proposes an integrated AI–PQC defense framework built on four tightly coupled layers. The cryptographic assurance layer, grounded in the NIST-standardized schemes, establishes a trusted substrate protecting communication channels, identities, and system states. Above it, an observation and feature abstraction layer transforms cryptographically protected events into behavioral and structural representations—key exchange patterns, protocol state transitions, message timing, and the frequency of cryptographic operations—that AI models can analyze without ever touching sensitive cryptographic material. The intelligent threat inference layer then applies probabilistic reasoning to estimate the most plausible threat hypothesis, and an adaptive response coordination layer selects defensive actions, from increased monitoring to cryptographic re-keying, by minimizing expected risk. A feedback loop closes the system, allowing cryptographic policies and AI threat models to co-evolve as conditions change.

The framework’s most demanding proving ground may be smart grid infrastructure, which the review examines in unusual depth. Grid communications rely on protocols such as IEC 61850 for substation automation, DNP3 for SCADA traffic, and GOOSE messaging for time-critical protection signaling—protocols designed with no quantum-resilient mechanisms in mind. ML-DSA signatures are substantially larger than their ECDSA equivalents, potentially exceeding DNP3 frame payload limits or colliding with GOOSE delivery windows measured in single-digit milliseconds. The threat model is equally severe: the harvest-now-decrypt-later strategy, in which adversaries stockpile encrypted grid telemetry today for decryption once quantum computers mature, is particularly acute for critical infrastructure. Attackers may also pursue false data injection against metering systems and disruption of relay coordination, requiring defenses that address both the cryptographic channel and behavioral anomaly layers simultaneously—precisely the integration the proposed framework is designed to deliver.

The authors are candid about the obstacles standing between concept and deployment. Scalability is a structural tension: lattice-based key encapsulation and signature verification impose overheads that compound across thousands of concurrent authentication events, while AI threat hunting demands continuous telemetry processing, and the combined latency can degrade detection timeliness exactly when responsiveness matters most. Explainability is another barrier with regulatory teeth—in finance, healthcare, and critical infrastructure, AI-generated alerts that trigger re-keying or access restrictions must be auditable, and post-hoc explanation methods such as SHAP and LIME remain largely unintegrated into real-time security operations. The review also flags the adversarial fragility of the AI components themselves, which can be poisoned, misled by injected telemetry, or probed through model inversion; the scarcity of representative quantum-era datasets; and the overlooked security risks of the multi-year migration period itself, when hybrid classical-PQC configurations must operate safely and adversaries may exploit transitional windows of reduced coverage.

Looking forward, the review outlines research directions that go beyond incremental improvement: AI-assisted optimization of cryptographic parameters and protocol adaptation, quantum-aware threat intelligence that accounts for quantum-enabled reconnaissance, autonomous cyber defense systems that balance autonomy with human oversight, and hybrid classical–quantum security architectures as quantum technologies mature. The authors’ synthesis of 84 studies—selected from an initial pool of 847 records screened across IEEE Xplore, Scopus, Web of Science, and the ACM Digital Library—reveals recurring patterns: most evaluations rely on synthetic or narrowly scoped data, few explicitly model adversarial robustness of the AI layer, and explainability remains a consistent gap. Their conclusion is blunt but constructive: the convergence of post-quantum cryptography and AI-driven threat hunting is not an incremental enhancement but a necessary evolution in cybersecurity design philosophy, one that demands interdisciplinary collaboration, careful trust and risk reasoning, and sustained attention to how cryptographic assurance and intelligent detection must grow together to survive the quantum era.

Subject of Research: Convergence of post-quantum cryptography and AI-driven threat hunting in quantum-resilient cybersecurity defense frameworks

Article Title: The convergence of post-quantum cryptography and AI-driven threat hunting: a review of quantum-resilient defense frameworks

Article References: Mohamed, N., & Taherdoost, H. (2026). The convergence of post-quantum cryptography and AI-driven threat hunting: a review of quantum-resilient defense frameworks. Frontiers in Communications and Networks, 7, Article 1816116. https://doi.org/10.3389/frcmn.2026.1816116

Image Credits: AI Generated

DOI: 10.3389/frcmn.2026.1816116

Keywords: post-quantum cryptography, quantum computing, AI threat hunting, cybersecurity, NIST standards, lattice-based cryptography, smart grid security, zero trust, threat intelligence, cryptographic agility, adversarial machine learning, quantum-resilient defense