Research / Paper Files
Paper Files
Paper Files are a personal practice of continuous study in cybersecurity that applies the scientific method as a learning tool. Each article is written not for academic publication, but as a deliberate exercise to sharpen the axe — deepening knowledge on specific topics, organizing ideas with rigor, and observing how understanding of certain subjects develops over time. It is the intellectual equivalent of a training journal: each entry records what was studied, how it was analyzed, and what conclusions emerged.
I strongly recommend that security professionals adopt this method. Choose a topic you want to master, research primary sources (RFCs, papers, official documentation), structure your text with introduction, methodology, analysis, and conclusion, and write as if you were explaining to a colleague. The act of writing forces clarity of thought and exposes gaps in understanding that passive reading cannot reveal. Use the articles on this site as examples of format and depth, and build your own collection — over time, you will have a personal repository of refined knowledge that no course or certification can replace.
DAI & ADAI · 2026
DAI: A Domain-Specific LLM with Model-Excluded Authority, Capability-Disjoint Verification, and Recomputable Receipts
DAI (Decripte Artificial Intelligence) is a proprietary domain-specific LLM within a governed autonomous defensive architecture. It is an instance of the proposed ADAI category defined in the companion framework paper. T…
Read paper →ADAI · ENAutonomous Defensive AI: An Envelope-and-Evidence Framework for Governed LLMs Acting on Production Infrastructure
ADAI (Autonomous Defensive Artificial Intelligence) is a proposed category of governed autonomous cyber defense. The framework specifies defended perimeters, bounded effects, declared rollback and capability-disjoint ver…
Read paper →15 papers
Identity and Access Management as a Cybersecurity Control Plane in 2026: Threat-Driven Design, Zero-Trust Integration, and Operational Hardening
Identity and Access Management (IAM) has evolved from a directory-centric administrative function into the primary control plane for enterprise cybersecurity. By 2026, the convergence of cloud-native architectures, SaaS sprawl, remote work, machine identities, and AI-assisted attack tradecraft has made identity the most contested layer in the enterprise stack. This paper presents a technical, threat-driven analysis of IAM as a security system, rather than merely an access administration service. We formalize IAM as a set of policy, authentication, authorization, provisioning, and telemetry primitives whose security objective is to minimize unauthorized privilege while preserving operational continuity. We examine contemporary attack paths including credential phishing, adversary-in-the-middle session hijacking, token theft, consent abuse, OAuth application abuse, SCIM and federation misconfiguration, dormant privilege exploitation, and identity-provider compromise. We then propose a hardened IAM methodology that combines phishing-resistant authentication, least-privilege privilege orchestration, just-in-time access, continuous conditional evaluation, device and workload attestation, and graph-based anomaly detection over identity relationships. The paper maps these controls into practical deployment patterns for modern environments spanning Microsoft Entra ID, Okta, Google Cloud Identity, AWS IAM/Identity Center, and hybrid LDAP/Active Directory estates. We further present an implementation blueprint, evaluation framework, and a detailed case study of a SaaS-centric enterprise migration from password-based access to a zero-trust IAM architecture. The findings show that the dominant failure modes in 2026 are not cryptographic weakness but lifecycle, federation, and operational design flaws, especially around stale entitlements, over-broad OAuth grants, and token persistence. The paper concludes that IAM security in 2026 must be treated as a continuously measured, automated, and adversary-aware control plane integrated with security telemetry, governance, and incident response.
Read paper →IAMIdentity and Access Management as a Cybersecurity Control Plane: Threats, Formal Models, and Defensive Engineering in the 2026 Era
Identity and Access Management (IAM) has become the primary control plane of modern cybersecurity, replacing perimeter-centric assumptions with identity-centric authorization decisions across cloud, SaaS, hybrid enterprise, and machine-to-machine environments. By 2026, the operational attack surface of IAM extends far beyond human logins: adversaries exploit phishing-resistant authentication gaps, token theft, consent abuse, OAuth misconfiguration, federation trust weaknesses, privilege escalation paths, and machine identity sprawl. This paper examines IAM as both a defensive architecture and a persistent target for offensive operations. We formalize IAM risk as a composition of authentication assurance, authorization correctness, federation trust, lifecycle hygiene, and monitoring visibility, and we analyze how these properties interact under real-world constraints such as zero trust deployment, cloud-native workloads, and AI-accelerated social engineering. Building on established standards and industry practice, we propose a defense-oriented methodology that unifies identity inventory, privilege graph analysis, continuous verification, policy-as-code, just-in-time access, phishing-resistant MFA, workload identity governance, and anomaly detection over sign-in and token telemetry. We also present an implementation blueprint spanning Microsoft Entra ID, Okta, AWS IAM, Google Cloud IAM, and Kubernetes service account patterns, with concrete automation and detection examples. An experimental evaluation framework is provided to measure reduction in standing privilege, mean time to revoke access, unauthorized consent exposure, and detection latency under simulated adversarial conditions. A case study of an OAuth consent-phishing incident demonstrates how identity telemetry, conditional access, and token revocation can compress attacker dwell time and limit blast radius. The paper concludes that IAM in 2026 must be treated as a dynamic cyber-defense system rather than a static directory service, requiring continuous governance, graph-based risk analysis, and operational integration with detection and response workflows.
Read paper →IAMIdentity and Access Management in Cybersecurity at 2026: A Risk-Adaptive, Zero-Trust, and AI-Resilient Framework for Enterprise Identity Defense
Identity and Access Management (IAM) has become the primary control plane of modern cybersecurity. By 2026, enterprise compromise increasingly begins not with perimeter intrusion but with credential theft, session hijacking, consent abuse, OAuth token abuse, SIM-swap-enabled MFA bypass, and privileged identity misuse across SaaS, cloud, and hybrid environments. This paper presents a technically grounded, publication-ready analysis of IAM as a security discipline, with emphasis on contemporary attack surfaces, control gaps, and defensive engineering patterns that reflect the 2026 threat landscape. We formalize IAM as a stateful authorization system spanning identities, authenticators, sessions, entitlements, device posture, and contextual risk, and we derive a risk-adaptive model that integrates phishing-resistant authentication, continuous authorization, privileged access governance, identity threat detection and response, and zero-trust enforcement. The proposed methodology is designed for heterogeneous enterprise environments that include Entra ID, Okta, Google Workspace, Active Directory, AWS IAM, and Kubernetes-native identity flows. We further discuss implementation patterns using SCIM, SAML, OIDC, FIDO2/WebAuthn, passkeys, conditional access, PAM, and CNAPP-integrated detection pipelines. The paper evaluates this framework through a realistic enterprise deployment scenario and a case study centered on modern identity-based intrusion tradecraft, including AiTM phishing, device-code abuse, OAuth consent attacks, and stale privilege accumulation. Experimental analysis shows that adaptive identity controls reduce blast radius, shorten mean time to detect anomalous sign-ins, and improve containment of token replay and lateral movement. The results support the conclusion that IAM in 2026 must be treated not as an administrative utility, but as a core security architecture requiring continuous telemetry, graph-based risk reasoning, and governance aligned with emerging AI-driven attack and defense capabilities.
Read paper →IAMFormal and Operational Architecture for Identity and Access Management (IAM) Resilient to Credential Compromise in Multi-Cloud Environments
TITLE: Formal and Operational Architecture for Identity and Access Management (IAM) Resilient to Credential Compromise in Multi-Cloud Environments ABSTRACT: Identity and Access Management (IAM) has established itself as a central element of security in distributed enterprise architectures. In multi-cloud environments, in which applications and services are executed simultaneously across multiple infrastructure providers, identity-based access control progressively replaces traditional network perimeters as the main defense mechanism. However, empirical evidence from incident reports indicates that credential compromise remains among the most recurrent vectors of data breaches. Attacks involving OAuth token reuse, exposure of API keys, and abuse of excessive permissions demonstrate that conventional authentication and authorization mechanisms do not ensure effective containment when legitimate credentials are obtained by adversaries. This work presents a formal and operational architecture for IAM resilient to partial credential compromise in multi-cloud environments. We propose a formal model that represents identity systems as a tuple composed of users, resources, permissions, tokens, and policies, enabling the definition of mathematical resilience properties under a threat model with graded adversaries. We introduce a security game that captures the scenario in which subsets of credentials are compromised and demonstrate how mechanisms such as ephemeral tokens, cryptographic key derivation, authorization domain segmentation, and risk-based adaptive authentication reduce the operational blast radius. The proposed architecture integrates Zero Trust principles, identity federation based on OpenID Connect and SAML, as well as behavioral monitoring mechanisms and coordinated revocation across multiple cloud providers. Experimental evaluations and case studies in critical infrastructure and financial sector contexts indicate that the approach significantly reduces the temporal window for exploiting compromised credentials while maintaining marginal computational overhead. By formalizing security properties often treated only at an architectural level in the literature, the work helps bring cryptographic models closer to operational guarantees in modern IAM systems. The results indicate that identity architectures explicitly designed to operate under partial compromise can measurably limit the impact of credential-based attacks.
Read paper →IAMAdaptive Identity and Access Management Architecture with Continuous Risk Assessment and Telemetry-Based Credential Abuse Detection
TITLE: Adaptive Identity and Access Management Architecture with Continuous Risk Assessment and Telemetry-Based Credential Abuse Detection ABSTRACT: Identity and access management (Identity and Access Management – IAM) has become a critical component of corporate security in distributed environments characterized by hybrid infrastructures, cloud computing, and intensive service integration. However, contemporary attacks often exploit compromised legitimate credentials, bypassing traditional mechanisms based solely on initial authentication and static permission control. Empirical reports indicate that credential misuse is one of the leading causes of security breaches in modern organizations. In this context, security architectures inspired by the Zero Trust paradigm emphasize continuous verification of identity and context throughout the entire session. This work proposes an adaptive IAM architecture that incorporates continuous risk assessment based on operational telemetry from endpoints, networks, and identity systems. We formalize a dynamic risk mathematical model R(u,s,t) associated with user u, session s, and time t, integrating metrics of behavioral deviation, access anomalies, and environmental context. The proposed architecture integrates components widely used in corporate and academic environments, including Keycloak as the identity provider, Open Policy Agent (OPA) as the policy decision mechanism, Elastic Stack as the event analytics infrastructure, and security sensors such as Sysmon and Suricata. In addition, we employ Sigma rules to standardize the detection of techniques associated with the MITRE ATT&CK framework. The experimental evaluation was conducted in a simulated corporate environment with more than 480 million log events collected over three months. Results indicate a significant reduction in operational incident response metrics, including a decrease in mean time to detect (MTTD) and mean time to respond (MTTR) when compared to models based exclusively on RBAC. A case study involving an attack chain with phishing, credential dumping, and lateral movement demonstrates the architecture’s ability to detect credential abuse at an early stage. The results indicate that the integration of IAM, operational telemetry, and continuous risk assessment mechanisms constitutes a viable approach to strengthening identity security in modern corporate environments.
Read paper →IAMThreat-Oriented Resilient IAM Architecture: Formalization, Implementation, and Empirical Evaluation of an Adaptive Model Based on Zero Trust and Behavioral Detection
TITLE: Threat-Oriented Resilient IAM Architecture: Formalization, Implementation, and Empirical Evaluation of an Adaptive Model Based on Zero Trust and Behavioral Detection ABSTRACT: The growing adoption of hybrid and multicloud environments has transformed digital identity into the primary vector for organizational security control. Identity and Access Management (IAM) systems, originally conceived as administrative mechanisms for account and permission provisioning, have come to play a central role in mitigating advanced attacks, particularly those based on credential compromise, session token abuse, and privilege escalation. Recent reports indicate that valid credentials continue to be one of the main intrusion vectors in enterprise environments. However, traditional IAM architectures remain predominantly based on static authorization models, such as Role-Based Access Control (RBAC), and are often unable to respond dynamically to contextual and behavioral changes associated with operational risk. In this work, we propose a threat-oriented resilient IAM architecture that integrates Zero Trust principles, dynamic risk assessment, and behavioral detection based on security telemetry. We mathematically formalize an adaptive risk model that combines identity sensitivity, operational context, behavioral deviation, and the criticality of the requested action. Based on this formalization, we design an access decision mechanism capable of applying adaptive authentication and blocking controls based on dynamic risk thresholds. We implemented an operational prototype using technologies widely adopted in enterprise environments in 2023, including Azure Active Directory, AWS IAM, Elastic Stack, Suricata, and Open Policy Agent. We conducted an empirical evaluation using an experimental environment containing thousands of simulated identities and operational telemetry collected over thirty days. The results demonstrate a substantial reduction in the mean time to detect credential abuse and a significant decrease in excessive permissions compared to a baseline based on static RBAC. In addition, we analyze operational implications for security teams and discuss limitations related to telemetry quality and model parameter calibration. We conclude that the integration of behavioral analysis, contextual assessment, and adaptive policies represents a significant advance in the construction of resilient IAM architectures in modern enterprise environments.
Read paper →IAMThreat-Oriented Resilient Identity and Access Management Architecture: Formal Modeling, Behavioral Detection, and Operational Hardening in Hybrid Environments
TITLE: Threat-Oriented Resilient Identity and Access Management Architecture: Formal Modeling, Behavioral Detection, and Operational Hardening in Hybrid Environments ABSTRACT: The consolidation of digital identities as the primary vector of compromise in corporate environments has transformed Identity and Access Management (IAM) into the core of modern security architecture. Hybrid and multi-cloud infrastructures, combined with the massive adoption of SaaS, APIs, and service identities, have significantly expanded the attack surface based on credentials and authentication tokens. Empirical reports indicate that most contemporary breaches involve abuse of legitimate accounts, privilege escalation, or exploitation of misconfigured authorization policies. This paper proposes a threat-oriented resilient IAM architecture that integrates three complementary dimensions: formal modeling of effective privileges, behavioral detection of anomalous identity usage, and automated operational hardening in hybrid environments. We initially formalize the privilege surface using directed graphs that represent relationships among users, roles, and permissions. Based on this model, we define quantitative metrics for measuring the identity attack surface and for analyzing privilege escalation paths. We then present an anomaly detection mechanism based on multidimensional behavioral vectors and Mahalanobis distance, applied to authentication events collected at scale. This mechanism makes it possible to identify abuse of valid accounts with a low false-positive rate, even in environments with thousands of identities and multiple identity providers. In addition, we describe an operational framework that integrates widely used tools in corporate environments — including federated identity providers, policy mechanisms based on Open Policy Agent, log analysis in Elastic Stack, and graph modeling in Neo4j — enabling continuous assessment of effective privilege and automated reduction of excessive permissions. The experimental evaluation was conducted in a simulated corporate environment containing more than twelve thousand identities and millions of authentication events. The results indicate a significant reduction in mean time to detect credential abuse, as well as a substantial decrease in excessive privileges. The study demonstrates that the integration of formal modeling, behavioral analysis, and operational automation constitutes an effective approach to strengthening identity security in zero trust architectures and contemporary hybrid environments.
Read paper →IAMAn Adaptive Risk-Oriented Identity and Access Management Architecture with Continuous Verification and Mathematical Formalization of Dynamic Policies
TITLE: An Adaptive Risk-Oriented Identity and Access Management Architecture with Continuous Verification and Mathematical Formalization of Dynamic Policies ABSTRACT: Identity and Access Management (IAM) systems constitute the core of security in modern enterprise environments, particularly in hybrid and multi-cloud infrastructures. However, traditional Role-Based Access Control (RBAC)-based models present structural limitations in the face of contemporary threats, including lateral movement (MITRE ATT&CK T1078), privilege abuse (T1068), and compromise of exposed credentials (e.g., CVE-2023-23397). In this work, we propose an adaptive risk-oriented IAM architecture, called ARIAM, that integrates attribute-based access control (ABAC), continuous context assessment, and real-time probabilistic risk modeling. We formalize the problem as a decision function $f: U \times R \times C \rightarrow \{0,1\}$, where $U$ represents users, $R$ resources, and $C$ dynamic context, incorporating a risk function $\rho(u,r,c)$ estimated via incremental Bayesian inference. We contribute (i) a rigorous mathematical modeling of security properties under adversaries with adaptive capabilities; (ii) decision algorithms with complexity $O(\log n)$ for incremental policy evaluation; and (iii) a scalable prototype implementation evaluated in an environment with 50,000 identities and 1.2 million daily requests. Experimentally, we demonstrate a 37.4% reduction in simulated unauthorized accesses and a 42.1% decrease in the mean time to detect privilege abuse, compared to a traditional RBAC baseline. The average overhead per request was 3.8 ms (σ = 0.6 ms), keeping total latency below 25 ms at the 95th percentile. Our results indicate that adaptive risk-oriented mechanisms offer substantial security gains without compromising operational scalability, establishing practical guidelines for the evolution of IAM architectures in compliance with the Zero Trust model advocated by NIST SP 800-207.
Read paper →IAMAdaptive Identity and Access Management Architecture with Continuous Risk Analysis and Mathematical Formalization for Mitigating Advanced Threats
TITLE: Adaptive Identity and Access Management Architecture with Continuous Risk Analysis and Mathematical Formalization for Mitigating Advanced Threats ABSTRACT: Identity and Access Management (IAM) systems constitute the core of security in distributed enterprise environments, being responsible for authentication, authorization, and identity governance in hybrid and multi-cloud infrastructures. However, recent incidents demonstrate that traditional approaches, based on static access control and rigid RBAC policies, are insufficient in the face of advanced persistent threats, valid credential exploitation, and lateral movement. In this work, we propose an adaptive, continuously risk-oriented IAM architecture, called A²-IAM, which integrates formal policy modeling, real-time probabilistic risk assessment, and decision mechanisms based on convex optimization. We formalize the authorization problem as a decision function $f(u, r, c, t)$ dependent on identity, resource, context, and time, subject to security and compliance constraints. We introduce two main algorithms: (i) Risk-Adaptive Policy Evaluation (RAPE) and (ii) Dynamic Privilege Minimization (DPM). We implemented a functional prototype integrated with Keycloak, Open Policy Agent (OPA), and a telemetry mechanism based on Apache Kafka. We evaluated the system in an experimental environment with 50,000 synthetic identities and 1.2 million access events, comparing it with traditional RBAC and static ABAC. The results indicate a 37.4% reduction in unauthorized high-risk accesses, a 28.1% decrease in the mean time to detect privilege abuse, and computational overhead below 11% in peak scenarios. We demonstrate that the proposed approach significantly improves resilience against MITRE ATT&CK techniques such as T1078 (Valid Accounts) and T1021 (Remote Services), while preserving scalability and regulatory compliance.
Read paper →IAMIAM-Guard: A Formal and Adaptive Architecture for Continuous Identity Governance and Privilege Minimization in Multicloud Environments
TITLE: IAM-Guard: A Formal and Adaptive Architecture for Continuous Identity Governance and Privilege Minimization in Multicloud Environments ABSTRACT: Identity and Access Management (IAM) constitutes the core of security controls in modern enterprise infrastructures, especially in multicloud environments and microservices-based architectures. Recent incidents, including exploits associated with MITRE ATT&CK techniques T1078 (Valid Accounts) and T1098 (Account Manipulation), demonstrate that failures in privilege governance and the detection of excessive configurations remain primary vectors of compromise. In this work, we propose IAM-Guard, a formal and adaptive architecture for continuous identity governance based on mathematical policy modeling, probabilistic detection of behavioral deviations, and automatic synthesis of least-privilege policies. Our methodology combines (i) a formalization of the privilege state as a labeled directed graph, (ii) a privilege-reduction algorithm based on multi-objective optimization, and (iii) an anomaly detection mechanism grounded in Gaussian mixture models and authorization sequence analysis. We implemented an operational prototype integrated with AWS, Azure, and Kubernetes environments, evaluating it over 1.2 billion authentication events collected over 180 days across three medium-sized organizations. The results indicate an average reduction of 37.8% in detected excessive privileges, a 42.3% decrease in the effective attack surface, and a credential abuse detection rate of 96.1% with a false positive rate of 3.4%. Compared to approaches based exclusively on static RBAC, IAM-Guard achieved a 28% improvement in privilege-minimization accuracy and a 31% reduction in mean response time to incidents involving compromised accounts. These results demonstrate the feasibility of formally grounded and adaptive identity governance as a central mechanism for defense in depth.
Read paper →IAMAn Adaptive and Formally Verifiable Architecture for Identity and Access Management (IAM) Resilient to Credential Compromise and Lateral Escalation
TITLE: An Adaptive and Formally Verifiable Architecture for Identity and Access Management (IAM) Resilient to Credential Compromise and Lateral Escalation ABSTRACT: Identity and Access Management (IAM) systems constitute the core security control in modern enterprise infrastructures, especially in hybrid and multi-cloud environments. However, recent data breach reports indicate that more than 60% of critical incidents involve abuse of valid credentials, privilege escalation, and misconfigured access policies. This work investigates the problem of dynamic management of identities and privileges under a realistic threat model that encompasses partial credential compromise, lateral movement, and exploitation of overly permissive policies. We propose an adaptive IAM architecture called A-FIAM (Adaptive and Formally-verified IAM), which integrates attribute-based access control (ABAC), continuous behavioral analysis, and formal verification of policies using linear temporal logic. The methodology combines mathematical modeling of the privilege graph, attack surface minimization algorithms, and anomaly detection based on statistical deviations in access profiles. We implemented a prototype in a hybrid environment (Kubernetes + Azure AD + Keycloak) and conducted experiments with 12.4 million real and synthetic events. The results demonstrate a 47.3% reduction in the surface of effective privileges, detection of credential abuse with a true positive rate of 96.1% and a false positive rate of 2.8%, in addition to an average overhead of less than 8.6 ms per authorization request. Compared with static RBAC approaches and native cloud provider IAM, our system reduces lateral escalation paths by 39% and automatically mitigates 92% of separation-of-duty violations. We conclude that the integration of formal verification with continuous behavioral adaptation represents a significant advance in the resilience of IAM systems against sophisticated adversaries.
Read paper →IAMAdaptive Identity and Access Management Architecture with Continuous Graph-Based Risk Assessment and Formal Policies for Multicloud Environments
1. Introduction
Read paper →IAMA Formally Verifiable Architecture for Adaptive Identity and Access Management with Quantitative Security Guarantees in Multi-Cloud Environments
1. Introduction
Read paper →IAMAdaptive and Formally Verifiable Architecture for Identity and Access Management with Risk-Oriented Privilege Minimization
1. Introduction
Read paper →IAMIAM-GraphSec: A Formal, Verifiable, and Scalable Architecture for Adaptive Identity Governance and Attack Surface Minimization in Multi-Cloud Environments
1. Introduction
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