Industry Perception of Security Challenges with Identity Access Management Solutions
- URL: http://arxiv.org/abs/2408.10634v1
- Date: Tue, 20 Aug 2024 08:19:58 GMT
- Title: Industry Perception of Security Challenges with Identity Access Management Solutions
- Authors: Abhishek Pratap Singh, Ievgeniia Kuzminykh, Bogdan Ghita,
- Abstract summary: The study aims to outline the current perception and security issues associated with IAMs solutions from the perspective of the beneficiaries.
The main challenges for cloud based IAM solutions were Default configurations, Poor management of Non-Human Identities such as Service accounts, Poor certificate management, Poor API configuration and limited Log analysis.
In contrast, the challenges for on premise solutions were Multi Factor Authentication, insecure Default configurations, Lack of skillsets required to manage IAM solution securely, Poor password policies, Unpatched vulnerabilities, and compromise of Single-Sign on leading to compromise of multiple entities.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Identity Access Management (IAM) is an area posing significant challenges, particularly in the context of remote connectivity and distributed or cloud-based systems. A wide range of technical solutions have been proposed by prior research, but the integration of these solutions in the commercial sector represent steps that significantly hamper their acceptance. The study aims to outline the current perception and security issues associated with IAMs solutions from the perspective of the beneficiaries. The analysis relies on a series of interviews with 45 cyber security professionals from different organisations all over the world. As results showed, cloud IAM solutions and on premises IAM solutions are affected by different issues. The main challenges for cloud based IAM solutions were Default configurations, Poor management of Non-Human Identities such as Service accounts, Poor certificate management, Poor API configuration and limited Log analysis. In contrast, the challenges for on premise solutions were Multi Factor Authentication, insecure Default configurations, Lack of skillsets required to manage IAM solution securely, Poor password policies, Unpatched vulnerabilities, and compromise of Single-Sign on leading to compromise of multiple entities. The study also determined that, regardless the evolving functionality of cloud based IAM solutions, 41% of respondents believe that the on premise solutions more secure than the cloud-based ones. As pointed out by the respondents, cloud IAM may potentially expose organisations to a wider range of vulnerabilities due to the complexity of the underlying solutions, challenges with managing permissions, and compliance to dynamic IAM policies.
Related papers
- Generative AI-Empowered Secure Communications in Space-Air-Ground Integrated Networks: A Survey and Tutorial [107.26005706569498]
Space-air-ground integrated networks (SAGINs) face unprecedented security challenges due to their inherent characteristics.<n>Generative AI (GAI) is a transformative approach that can safeguard SAGIN security by synthesizing data, understanding semantics, and making autonomous decisions.
arXiv Detail & Related papers (2025-08-04T01:42:57Z) - SkyEye: When Your Vision Reaches Beyond IAM Boundary Scope in AWS Cloud [0.0]
Cloud security has emerged as a primary concern for enterprises.<n> IAM constitutes the critical security backbone of most cloud deployments.<n>SkyEye is a cooperative multi-principal IAM enumeration framework.
arXiv Detail & Related papers (2025-07-01T01:36:52Z) - Knowledge Augmented Complex Problem Solving with Large Language Models: A Survey [48.53273952814492]
Large Language Models (LLMs) have emerged as powerful tools capable of tackling complex problems across diverse domains.<n>Applying LLMs to real-world problem-solving presents significant challenges, including multi-step reasoning, domain knowledge integration, and result verification.
arXiv Detail & Related papers (2025-05-06T10:53:58Z) - A Trustworthy Multi-LLM Network: Challenges,Solutions, and A Use Case [59.58213261128626]
We propose a blockchain-enabled collaborative framework that connects multiple Large Language Models (LLMs) into a Trustworthy Multi-LLM Network (MultiLLMN)<n>This architecture enables the cooperative evaluation and selection of the most reliable and high-quality responses to complex network optimization problems.
arXiv Detail & Related papers (2025-05-06T05:32:46Z) - Towards Trustworthy GUI Agents: A Survey [64.6445117343499]
This survey examines the trustworthiness of GUI agents in five critical dimensions.
We identify major challenges such as vulnerability to adversarial attacks, cascading failure modes in sequential decision-making.
As GUI agents become more widespread, establishing robust safety standards and responsible development practices is essential.
arXiv Detail & Related papers (2025-03-30T13:26:00Z) - Privacy-Enhancing Paradigms within Federated Multi-Agent Systems [47.76990892943637]
LLM-based Multi-Agent Systems (MAS) have proven highly effective in solving complex problems by integrating multiple agents, each performing different roles.
In this paper, we introduce the concept of Federated MAS, highlighting the fundamental differences between Federated MAS and traditional FL.
We then identify key challenges in developing Federated MAS, including: 1) heterogeneous privacy protocols among agents, 2) structural differences in multi-party conversations, and 3) dynamic conversational network structures.
To address these challenges, we propose Embedded Privacy-Enhancing Agents (EPEAgent), an innovative solution that integrates seamlessly into the Retrieval-Augmented Generation phase and the
arXiv Detail & Related papers (2025-03-11T08:38:45Z) - 2FA: Navigating the Challenges and Solutions for Inclusive Access [55.2480439325792]
Two-Factor Authentication (2FA) has emerged as a critical solution to protect online activities.
This paper examines the intricacies of deploying 2FA in a way that is secure and accessible to all users.
An analysis was conducted to examine the implementation and availability of various 2FA methods across popular online platforms.
arXiv Detail & Related papers (2025-02-17T12:23:53Z) - Token-based identity management in the distributed cloud [0.0]
This research paper centres on identity management in distributed environments.
The paper concentrates on implementing robust security paradigms to minimise communication overhead among services.
The proposed solution incorporates an Identity and Access Management server as a component that authenticates all external requests.
arXiv Detail & Related papers (2024-10-29T09:00:01Z) - A Survey of AIOps for Failure Management in the Era of Large Language Models [60.59720351854515]
This paper presents a comprehensive survey of AIOps technology for failure management in the LLM era.
It includes a detailed definition of AIOps tasks for failure management, the data sources for AIOps, and the LLM-based approaches adopted for AIOps.
arXiv Detail & Related papers (2024-06-17T05:13:24Z) - Object Detectors in the Open Environment: Challenges, Solutions, and Outlook [95.3317059617271]
The dynamic and intricate nature of the open environment poses novel and formidable challenges to object detectors.
This paper aims to conduct a comprehensive review and analysis of object detectors in open environments.
We propose a framework that includes four quadrants (i.e., out-of-domain, out-of-category, robust learning, and incremental learning) based on the dimensions of the data / target changes.
arXiv Detail & Related papers (2024-03-24T19:32:39Z) - PresAIse, A Prescriptive AI Solution for Enterprises [6.523929486550928]
This paper outlines an initiative from IBM Research, aiming to address some of these challenges by offering a suite of prescriptive AI solutions.
The solution suite includes scalable causal inference methods, interpretable decision-making approaches, and the integration of large language models.
A proof-of-concept, PresAIse, demonstrates the solutions' potential by enabling non-ML experts to interact with prescriptive AI models via a natural language interface.
arXiv Detail & Related papers (2024-02-03T03:23:08Z) - HAZARD Challenge: Embodied Decision Making in Dynamically Changing
Environments [93.94020724735199]
HAZARD consists of three unexpected disaster scenarios, including fire, flood, and wind.
This benchmark enables us to evaluate autonomous agents' decision-making capabilities across various pipelines.
arXiv Detail & Related papers (2024-01-23T18:59:43Z) - The Security and Privacy of Mobile Edge Computing: An Artificial Intelligence Perspective [64.36680481458868]
Mobile Edge Computing (MEC) is a new computing paradigm that enables cloud computing and information technology (IT) services to be delivered at the network's edge.
This paper provides a survey of security and privacy in MEC from the perspective of Artificial Intelligence (AI)
We focus on new security and privacy issues, as well as potential solutions from the viewpoints of AI.
arXiv Detail & Related papers (2024-01-03T07:47:22Z) - A Roadmap towards Intelligent Operations for Reliable Cloud Computing
Systems [30.952201576129056]
This paper highlights two main challenges, namely internal and external factors, that affect the reliability of cloud.
We discuss the data-driven approach that can resolve these challenges from four key aspects: ticket management, log management, multimodal analysis, and the microservice resilience testing approach.
arXiv Detail & Related papers (2023-10-01T14:08:02Z) - Interactive Greybox Penetration Testing for Cloud Access Control using IAM Modeling and Deep Reinforcement Learning [6.350737151909975]
We propose a precise greybox penetration testing approach called TAC for third-party services to detect IAM PEs.
We first propose IAM modeling, enabling TAC to detect a broad class of IAM PEs based on the partial information collected from queries.
Experimental results on both synthetic and real-world tasks show that, compared to state-of-the-art whitebox approaches, TAC detects IAM PEs with competitively low false negative rates.
arXiv Detail & Related papers (2023-04-27T21:44:59Z) - Privacy-Preserving Joint Edge Association and Power Optimization for the
Internet of Vehicles via Federated Multi-Agent Reinforcement Learning [74.53077322713548]
We investigate the privacy-preserving joint edge association and power allocation problem.
The proposed solution strikes a compelling trade-off, while preserving a higher privacy level than the state-of-the-art solutions.
arXiv Detail & Related papers (2023-01-26T10:09:23Z) - On solving decision and risk management problems subject to uncertainty [91.3755431537592]
Uncertainty is a pervasive challenge in decision and risk management.
This paper develops a systematic understanding of such strategies, determine their range of application, and develop a framework to better employ them.
arXiv Detail & Related papers (2023-01-18T19:16:23Z) - Machine Learning (In) Security: A Stream of Problems [17.471312325933244]
We identify, detail, and discuss the main challenges in the correct application of Machine Learning techniques to cybersecurity data.
We evaluate how concept drift, evolution, delayed labels, and adversarial ML impact the existing solutions.
We present how existing solutions may fail under certain circumstances, and propose mitigations to them.
arXiv Detail & Related papers (2020-10-30T03:40:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.