What are some of the latest trends and innovations in IAM that you need to keep up with and learn about?

IDaaS is a cloud-based IAM solution that provides centralized identity and access management capabilities. It allows organizations to manage user identities, access controls, and authentication mechanisms across multiple applications and platforms. IDaaS offers scalability, flexibility, and simplified administration.

In my opinion, most organizations are now well aware of the need for having a robust IAM programs to meet the regualtory compliance and also to safe guard the ever weekest link in the cybersecurity. Adpting to Zero Trust IAM. Embracing AI/ML to modernize the enterprize wide employee certifications are few things to learn and keep upto date.

Cloud-based IAM fits in well for simplistic cloud based applications, however, for traditional on prem applications with a lot of complexity around roles/permissions, the business need is fulfilled by on prem solutions with a lot of customization which makes it challenging to scale and adds operational overhead. Hybrid approach is what I have seen works for most part where we can leverage cloud as well as on prem solutions

IDaaS or Identity as a Service are Cloud Based IAM applications that follows all standard protocols and IAM best practices. In this case, won't be required to maintain internal infrastructure and you'll be charged by the number of identities in your environment only. It's not as flexible and customizable as the on-prem IAM solutions, however, due this "lack of customization possibilities", it prevents bad customizations that usually makes very hard to maintain or change IAM solutions. The number of out-of-the-box connectors makes it very easy and fast to connect with other SaaS applications.

Cloud Based IAM is a great way to manage roles and permissions natively in the public cloud. It can be used on its own or with a central identity provider ( ie. AWS SSO integrated with Okta via SCIM or SAML). Building custom roles and permission sets can enable more granular access control, but can lead to increased environmental complexity and an over-abundance of roles (known as role-explosion) due to varied access needs across user roles and the dynamic nature of the cloud. Additionally, the different approaches cloud vendors take to IAM requires vendor-specific knowledge to configure and can make standardization labor intensive in multi-cloud orgs. Relevant trends: JIT (Just-In-Time), Centralized Cloud Access Governance, ABAC

Using an authorization model like Policy-Based Access Controls (PBAC) can be a great start for organizations to implement zero-trust IAM. PBAC allows organizations to combine user's roles, attributes and other contextual attributes to make real-time authorization decisions rather then just relying on static role assignments.

Zero Trust is an approach to IAM that emphasizes continuous verification and strict access controls. It assumes that no user or device should be trusted by default, even if they are inside the network perimeter. Zero Trust focuses on authentication, authorization, and continuous monitoring to ensure secure access to resources.

Zero-trust IAM goes beyond simple authentication and authorization to incorporate risk-based access control. This means that access privileges are dynamically assigned based on a user's risk profile, considering factors such as device type, location, and behavior. This dynamic approach reduces the likelihood of unauthorized access or exfiltration of sensitive data. One of the cornerstones of zero-trust IAM is micro-segmentation, which divides networks into smaller, more isolated segments. This granular control over access permissions compartmentalizes sensitive data and resources, effectively limiting the impact of a breach if one segment is compromised.

Not sure Zero Trust IAM is the best term here as it conflates two already intensely interrelated (and sometimes confusing) concepts. A mature approach to IAM serves as a foundational element in successfully enforcing a zero trust strategy. Well defined roles and groups are critical to enforcing least privileged access while MFA and Continuous Authorization ensure the user is who they say they are. Zero Trust IAM might also refer to the concept of Zero Standing Privilege, where no user has any permissions or level of access by default, but receive only relevant access on a temporary, as needed basis. Exciting innovation on the user experience front is helping make this concept more practical, especially for highly sensitive resources.

Zero-trust IAM redefines security, verifying every access request. Picture a corporation implementing multifactor authentication and adaptive access control, enhancing security and user experience. Zero-trust reduces attack surfaces, like microsegmentation isolating network segments. Continuous monitoring ensures real-time threat detection. Adoption requires mastering tools like MFA and SSO, ensuring adaptive security. This approach fortifies security, ensures compliance, and enhances user experience in the evolving threat landscape.

As others have pointed out, one threat to biometric IAM is "spoofing". But it's not an existential threat–far from it. *Some* providers are protected, and others aren't. There are two types of attack to be aware of: presentation and injection. In presentation attacks, fraudsters use deepfakes to spoof their way through a biometric system. Sometimes they even use life-like synthetic masks, just like in Mission Impossible. Or they just hold up a printed-out photo. Injection attacks hack their way into the data stream to "inject" false biometric data. These are rarer, because they require advanced coding knowledge and exploit vulnerabilities in software apps themselves. Do your due diligence and run a "deepfake pentest" on your provider.

One of the major issues with Biometric IAM is spoofing and presentation attacks. Despite advancements in technology, there are still ways to fool biometric systems. For instance, high-quality photographs can be used to deceive facial recognition software, or synthetic fingerprints might be used to trick a fingerprint scanner. Therefore, the use of Biometric IAM must adhere to the zero trust model. It should not be relied upon as a sole method of identification and authentication but rather should be incorporated into a broader array of IAM tools for more robust security.

Balancing Security and Privacy is grey area in Biometric IAM. It leverages unique physical or behavioral traits for user authentication, promising enhanced security but raising privacy concerns. MFA integration, biometric convergence, and emerging tech drive advancements. Cloud-based IAM, AI/ML-powered solutions, and IGRM gain prominence. Carefully weigh privacy, cost, and user acceptance before implementation.

The biggest trends in Biometric IAM is the integration of AI & Machine Learning to expand the potential use cases. Previously you were limited in being able to use biometrics when you had a device that supported it. Machine learning is enabling you to begin leveraging methods that have no such requirement. For example, Twosense are using behavior (how you type and move the mouse) to create a biometric of you which follows you and doesn't require anything beyond you.

Biometric IAM redefines authentication. Utilizing unique biological or behavioral traits, like fingerprints or facial recognition, it offers robust security and user convenience. For instance, airports deploy biometrics for seamless boarding, enhancing both security and passenger experience. However, the technology raises complex ethical, legal, and technical considerations, such as privacy and accuracy. Experts must evaluate various biometric technologies, ensuring they strike the right balance between security and usability. Embracing biometric IAM promises enhanced personalization, but its successful implementation necessitates a deep understanding of these multifaceted challenges.

I understand that AI is now a "buzzword" being used in every aspect of cybersecurity to commercialize and stand out among competitors. Many IAM solutions are trying to commercialize their AI modules. Are they effective? Not all the time. I believe there are many false positives, and AI may miss sophisticated attacks. However, if it is well-trained, it can be helpful in complex decision-making and pattern recognition. For example, AI can consider a multitude of factors such as user role changes, historical access patterns, and contextual information. Analyzing all this data and making decisions can be difficult to achieve manually, but with AI, it can be achieved more efficiently and accurately.

How about Ethical AI-powered IAM for fairness, transparency, privacy and security? Fairness & Bias Mitigation: Foster bias-free IAM algorithms, granting equal user access Transparency & Explainability: Decisions are transparent and clearly explained to increases trust and help refine systems Privacy Preservation: Safeguard user data within IAM through anonymization and encryption Security & Accountability: Security against attacks and clear accountability for AI decisions to address misuse User Consent & Autonomy: Respect user consent to allow data control, including opt-outs Continuous Monitoring & Improvement: Regular audits ensure AI-IAM aligns with ethical standards

I'm not so confident AI-powered IAM can remove biases because the training of the AI is done by humans and there are always biases present wherever humans are involved.

AI enhances security through machine learning, detecting threats like unusual user behavior. Context-aware authentication improves the user experience by adapting to habits. Automated provisioning efficiently scales tasks with organizational growth. Challenges include potential bias in AI algorithms, leading to unintentional discrimination. Integrating AI into existing systems demands careful planning. Risks of cyberattacks, like adversarial attacks, pose threats. Extensive data processing raises privacy concerns, necessitating robust protection. Initial stages involve a learning curve for administrators and users, requiring proper training.

I suppose the obvious Application areas are new user onboarding automation ie. eKYC Space with computer vision and Identity as a new perimeter to protect ie. ITDR in Zero Trust architectures. Notable shift in Biz logic towards the user thru consent management ie. SSI means that AI could provide decision support from risk management PoV to user as well and not only to service side.

Blockchain IAM will take IAM to another level. With Decentralized identity, user itself will be responsible for sharing what it decides is required for that application. This will enhance privacy and can lead to more use cases and a unique Identity for that user along several different scenarios.

Blockchain-based IAM revolutionizes identity management. It eliminates the reliance on centralized authorities and enhances security through decentralized, tamper-resistant records. For instance, a government can deploy a public blockchain to secure citizen identities. Smart contracts enable automated access control, ensuring only authorized parties access sensitive data. This innovation fosters user privacy, control, and trust, making it a compelling trend in IAM. However, it requires expertise in blockchain design, deployment, and smart contract development to harness its full potential.

Blockchain-based IAM can improve security and privacy by eliminating central authorities and providing unchangeable records. However, there are some unexpected challenges: The system might slow down as more identities are added, making scalability an issue. Setting up and maintaining blockchain IAM is complex and expensive, requiring specialised knowledge. Public blockchains use a lot of energy, increasing costs and raising environmental concerns. The permanent nature of blockchain data can clash with privacy laws like GDPR, which require data deletion. Lastly, many solutions still rely on some central management, which can undermine decentralisation. Balancing these challenges with the benefits is key to successful implementation.

Maybe all the above approaches looks like layering stuff on a broken and strained model. Maybe we should fix the model first.

Staying ahead in IAM involves keeping tabs on the latest trends & innovations. Zero Trust Architecture is gaining traction, emphasizing continuous verification of users and devices, reducing reliance on assumed trust. API security is a growing concern. IAM systems need to adapt to secure the increasing use of APIs for data exchange between applications. Passwordless authentication is on the rise. AI and machine learning are being integrated for advanced threat detection & risk assessment. Blockchain can be explored for its potential in IAM. Containerization & microservices are impacting IAM. Regulatory compliance GDPR & CCPA, continues to shape IAM strategies, emphasizing the need for robust data protection and user privacy measures

- Multiple Personas where same person play multiple roles and has separate accounts based on their separate roles as well as accesses. Common industries are Hospital, College / Universities, etc. Managing accesses effectively and allowing users to use roles / groups / permissions / profiles / licenses associated with a given persona when the user is using that persona is the key. - Biometrics as part of IAM is equally interesting and efficient asset management associated with this is equally important. - Use of AI along with Zero Trust, determining risks dynamically with help of AI is quite emerging space ams sky is limit in terms of opportunities. - Efficient Cloud resource management using IAM is emerging as extremely niche requirement.

IAMs role in dev sec ops. Ask any leading product company how they help secure your dev ops pipelines. They will stare at you blankly or giver you a high level non-answer. IAM practitioners are a decade behind development teams.

Other things which I should mention are 1) User and Entity Behavior Analytics (UEBA): UEBA involves the use of machine learning and analytics to detect anomalies in user behavior and identify potential security threats. By monitoring user activity and comparing it to established patterns, UEBA can identify suspicious actions or deviations from normal behavior, helping to prevent data breaches and unauthorized access. 2) Adaptive Access Control: Adaptive access control solutions use contextual information (such as user location, device, time of access, and behavior) to dynamically adjust access controls. This approach allows organizations to balance security and usability by providing the right level of access based on the current context.