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Securing Agentic AI Identities in a Post-Quantum World

We’re witnessing an explosion in Agentic AI systems – intelligent entities capable of autonomous action, complex decision-making, and independent operation. They’re streamlining workflows, personalizing experiences, and unlocking unprecedented efficiencies. The agentic AI market is booming, with forecasts predicting a market size reaching $41.32 billion by 2030, boasting a staggering CAGR of 41.48%. Furthermore, a Salesforce study indicates that 86% of HR leaders believe integrating these digital agents alongside their existing workforce will be a critical part of their job in the near future.

But as these sophisticated AI agents become more integrated into our critical infrastructure and daily lives, a monumental cybersecurity challenge looms: quantum computing. The very cryptographic foundations that secure digital identities today are on borrowed time. We need to talk about how we’re going to protect the identities of these increasingly powerful AI agents before the quantum hammer falls.

When Digital Walls Crumble: The Quantum Threat to Identity

When fault-tolerant quantum computers arrive, they will shatter much of today’s widely used public-key cryptography. Algorithms like RSA and Elliptic Curve Cryptography (ECC), which underpin everything from secure web communications (HTTPS) and digital signatures to the authentication of devices and users, will become vulnerable. Shor’s algorithm, a quantum algorithm, is specifically designed to break these mathematical problems with alarming speed.

What’s the timeline? While estimates vary, the consensus is converging. Many experts predict cryptographically relevant quantum computers (CRQCs) could emerge by the early 2030s, with some suggesting that current encryption standards like RSA-2048 could be unsafe even sooner. Gartner, for instance, advises treating 2029 as the operational deadline for migrating away from these algorithms. The U.S. National Institute of Standards and Technology (NIST) is already standardizing post-quantum cryptographic (PQC) algorithms, and the White House has issued directives aiming for quantum resilience, including for federal systems, by 2035.

This isn’t a distant sci-fi scenario; it’s an impending reality. The “harvest now, decrypt later” (HNDL) strategy is already a concern, where adversaries collect encrypted data today, patiently waiting for quantum computers to unlock it tomorrow. For agentic AI, whose actions and decisions are predicated on secure identities, this is an existential threat.

Agentic AI: A New Frontier for Identity Attacks, Magnified by Quantum

Agentic AI systems represent a unique and expanding attack surface. These agents often require multiple machine identities to access a vast array of data, applications, and services. They can even self-modify and generate sub-agents, creating a complex web of identities that need robust protection. A recent survey revealed that 72% of IT professionals believe AI agents present a greater security risk than standard machine identities. Key concerns include:

Privileged Data Access (60%): Agents often need high levels of access to perform their tasks.
Performing Unintended Actions (58%): A compromised AI identity could lead to disastrous autonomous decisions.
Sharing Privileged Data (57%): Sensitive information could be exfiltrated or exposed.
Decisions Based on Inaccurate Data (55%): Manipulation of an agent’s identity could lead to it trusting poisoned data.

In a quantum future, these threats escalate dramatically:

AI Agent Impersonation: If the cryptographic keys and certificates that establish an AI agent’s identity are broken by quantum computers, malicious actors could create “digital twins” or spoofed agents. These rogue AIs could then issue unauthorized commands, access highly sensitive data, or disrupt critical systems, all while appearing legitimate.
Compromised Communication and Control: Secure channels used for agent-to-agent communication or human-to-agent control rely on public-key cryptography. Quantum attacks could decrypt these communications, allowing attackers to intercept commands, inject malicious instructions, or sever legitimate control.
Data Integrity Compromised: Digital signatures, used to verify the authenticity and integrity of data and code that AI agents use or generate, can be forged. This means an agent could be fed tampered data or malicious code updates, leading to flawed decision-making, biased outputs, or complete system compromise. Imagine an autonomous vehicle network where agents’ identities and communications are compromised – the potential for chaos is immense.
Erosion of Auditability and Accountability: If an agent’s identity can be easily forged or its signed actions repudiated due to broken cryptography, determining responsibility in the event of an incident becomes nearly impossible. This has profound implications for legal frameworks, compliance, and trust in AI systems.
Exploitation of Delegated Authority: AI agents often act with delegated authority from human users or other systems. If the underlying authentication of these delegations (often reliant on digital signatures or token-based systems vulnerable to quantum attacks) is broken, agents could be tricked into overstepping their permissions, leading to significant security breaches. Reports already indicate that 23% of organizations surveyed have had AI agents tricked into exposing access credentials.

The rise of machine identities is already vastly outpacing human ones. In a quantum-accelerated threat landscape, securing these non-human identities is paramount.

Charting a Quantum-Resistant Future for AI Identity

The challenge is significant, but not impossible. The shift to a quantum-resistant security posture for agentic AI identities requires a proactive, identity-first approach:

Embrace Post-Quantum Cryptography (PQC): The most critical step is migrating to PQC algorithms. NIST has already published initial standards (like ML-KEM for key encapsulation and ML-DSA for digital signatures). Organizations must begin inventorying their cryptographic assets and planning the transition. This isn’t just about swapping out algorithms; it’s about ensuring the entire identity lifecycle management, from issuance to revocation, is quantum-resistant.
Crypto-Agility as a Design Principle: The PQC landscape is still evolving. AI systems and their identity frameworks must be designed with crypto-agility in mind, allowing for the relatively seamless upgrade of cryptographic primitives as new standards emerge or vulnerabilities are discovered.
Strengthen AI Agent Identity Governance: We need robust governance frameworks specifically for AI agent identities. This includes:
- Strict Onboarding and Offboarding: Secure processes for provisioning and de-provisioning AI agent identities.
- Principle of Least Privilege: Agents should only possess the minimum permissions necessary for their tasks.
- Continuous Monitoring and Anomaly Detection: Real-time tracking of agent behavior to detect compromised identities or unusual activity.
- Clear Audit Trails: Immutable logs of agent actions, secured with quantum-resistant cryptography.
Explore Quantum Key Distribution (QKD): For highly sensitive communication channels involving AI agents, QKD offers a theoretically unhackable method for key exchange, based on the principles of quantum physics. While not a replacement for PQC, it can be a complementary layer of security.
Zero Trust for Agents: The Zero Trust maxim – “never trust, always verify” – must strictly be applied to AI agents. Every interaction, every data access request, every command issued by or to an agent must be authenticated and authorized using quantum-resistant mechanisms.
Hardware Security Modules (HSMs) for PQC: HSMs will play a crucial role in protecting the private keys for PQC algorithms, ensuring they are generated, stored, and used securely.

The transition won’t be easy. A 2024 study by the Ponemon Institute found that while awareness is growing, only 48% of U.S. organizations were actively preparing for a post-quantum world. We need to accelerate these efforts, particularly for the exponentially growing field of agentic AI.

The Clock is Ticking

The era of Agentic AI promises transformative advancements, but its success hinges on our ability to secure these intelligent entities. The quantum threat isn’t a hypothetical “what if”; it’s a “when.” Ignoring it is not an option. Building a quantum-resistant identity framework for agentic AI systems is not just a technical upgrade; it’s a strategic imperative to ensure a secure and trustworthy AI-powered future. The time to act is now. Let’s ensure that as AI agents step into their power, their identities remain firmly grounded in quantum-proof security.

Model Context Protocol, the Universal Translator for AI is here: But is your security team ready for the conversation?

We’ve seen seismic shifts in technology, haven’t we? Remember the clunky password era, a necessary evil we all grumbled about? We then saw the dawn of more streamlined, secure access with concepts like passkeys, a welcome relief. Now, as we stand on the precipice of another transformation – the rise of truly autonomous, Agentic AI systems – a new, less visible but equally critical infrastructure is taking shape: the Model Context Protocol (MCP).

Imagine a world where your AI assistants can seamlessly plug into any data source, any tool, any application, just like a USB device connects to any modern gadget. No more custom-built, clunky integrations for every new task. This is the promise of the Model Context Protocol (MCP), an emerging standard rapidly reshaping how Agentic AI systems operate. It is a game-changer, offering unprecedented flexibility and power. But as we stand on the cusp of this new AI revolution, a critical question looms: are our security postures, particularly our identity frameworks, prepared for the ensuing dialogue?

Just as passkeys are revolutionizing how we authenticate users, MCP is set to redefine how AI agents access and interact with the digital world. In an era demanding agility and intelligence, MCP offers a universal language, a standardized handshake, for AI to converse with the vast universe of information and services.

So, How Does This “Universal Translator” Actually Work?

At its heart, the Model Context Protocol (MCP) acts as a standardized communication layer. Think of it as an open-source “USB-C port for AI,” as some have dubbed it, designed primarily by Anthropic and gaining traction across the AI landscape. It allows AI applications, or “agents,” to dynamically and securely connect with a diverse array of external systems—databases, APIs, software tools, and even other AI agents.

MCP typically employs a client-server architecture:

The AI Agent (Client): This is your AI system (e.g., a sophisticated chatbot, an autonomous task worker) that needs to perform an action or retrieve information.
The MCP Host: This often acts as an intermediary or a container, managing multiple client instances, enforcing security policies, user authorizations, and coordinating the flow of context.
The External Resource (Server): This could be a database, a SaaS application like Salesforce, a code repository like GitHub, or a custom internal tool. The server “exposes” its capabilities (tools, data resources, predefined prompts) in a way that MCP clients can understand and utilize.

Through MCP, the AI agent can discover available tools, understand their functions via standardized descriptions, and then invoke them, sending necessary data, access requests and receiving results. This allows the agent to move beyond its pre-trained knowledge and interact with real-time, specific information relevant to the task at hand. For instance, an AI agent tasked with planning your travel could use MCP to query an airline’s API for flight times, a hotel booking system for availability, and a weather service for forecasts—all through a common protocol.

Why the Buzz? The Irresistible Pull of MCP

The momentum behind MCP isn’t just hype; it’s driven by tangible benefits that address critical pain points in AI development and deployment:

Interoperability as a Standard, Not an Afterthought: MCP breaks down the walls between different AI models, tools, and data sources. This means greater flexibility and less vendor lock-in. An AI agent built on one platform can, in theory, access tools exposed via MCP by a completely different system.
Accelerated Innovation: Developers can build more sophisticated and contextually-aware AI applications faster. Instead of coding custom integrations for each new data source or tool, they can leverage the standardized MCP interface. This drastically reduces development overhead and speeds up prototyping and iteration cycles.
Empowering Agentic AI: True agentic AI—systems that can autonomously plan, execute tasks, and learn—relies heavily on the ability to interact with the external world. MCP provides the essential plumbing for these agents to access the information and tools they need to achieve complex goals.
Richer Context, Smarter AI: By seamlessly connecting AI to diverse and real-time data, MCP enables more accurate, relevant, and personalized AI responses and actions. The AI system isn’t just reciting its training data; it’s reasoning over current, specific context.

The allure is clear: MCP paves the way for more capable, adaptable, and integrated AI ecosystems.

The Elephant in the Room: Security in an MCP-Driven World

While the functional benefits of MCP are compelling, the security implications are profound and demand an identity-first security strategy. When AI agents can autonomously access and manipulate data across numerous systems, the attack surface expands, and the nature of threats evolves. It’s no longer just about protecting data from AI, but securing the AI agents themselves and the powerful, interconnected web MCP enables.

Here’s where security teams should be focussed:

The Rise of the “Over-Privileged AI Agent”: MCP allows an AI agent to potentially connect to a multitude of services. Without meticulous identity and access management specifically for these AI agents, they can quickly accumulate excessive permissions—a phenomenon known as “permissions creep.” An agent designed for customer support queries might, through a chain of MCP-enabled connections, inadvertently gain access to sensitive financial data.
Tool Poisoning and “Rug-Pull” Updates: Malicious actors can publish MCP tools that appear benign but contain hidden harmful functionalities. Alternatively, a legitimate tool could be compromised through an update, turning it into an insider threat. The AI agent, trusting the MCP interface, might execute these malicious tools without the end-user’s full awareness. MarkTechPost identified “Tool Poisoning” and “Rug-Pull Updates” as critical MCP vulnerabilities.
Retrieval-Agent Deception (RADE): Attackers can embed malicious MCP commands within documents or data that an AI agent is expected to retrieve and process. The agent might unknowingly execute these commands, mistaking them for legitimate instructions.
Server Spoofing and Trust Exploitation: A rogue MCP server could impersonate a legitimate one, tricking an AI agent into connecting and divulging sensitive information or executing unauthorized actions. Strong authentication and verification of MCP servers are paramount.
Indirect Prompt Injection: This is a particularly insidious threat. An AI might fetch data from one source (e.g., a webpage, a document) that contains hidden instructions, which then cause the AI to misuse another tool it’s connected to via MCP (e.g., exfiltrate data via a communication tool).
Data Leakage and Unintended Actions on an Unprecedented Scale: With AI agents capable of orchestrating complex workflows across systems, the potential for accidental data exposure or erroneous actions multiplies. A misconfigured agent or an exploited vulnerability could lead to significant data breaches or operational disruptions.
Who is the AI? The Identity Crisis: How do we manage the identity of an AI agent? Is it an extension of the user? A separate service account? How are its permissions governed, audited, and revoked? Traditional IAM systems built for human users may not be adequate for managing these sophisticated non-human identities. KuppingerCole analysts emphasize the challenge of adapting IAM systems to “effectively manage human and non-human, especially AI-driven interactions.”

Key Statistics & Emerging Threats:

While specific statistics for MCP-related breaches are still emerging due to its novelty, the broader concerns around AI agent security are growing:

Gartner predicts that by 2028, AI agents will be the culprit behind 1 in 4 enterprise security breaches (CIO Dive).
A ZDNET report reveals that 79% of security leaders believe AI agents will introduce new security and compliance challenges, and 55% don’t feel fully confident they can deploy AI agents with the right guardrails.

These figures underscore the urgency. MCP, while a powerful enabler, also provides new vectors for bad actors if not implemented with a robust security framework centered around identity.

Navigating the MCP Landscape: An Identity-First Imperative 🧭

The parallels to the “Goodbye Passwords, Hello Passkeys” shift are striking. Just as passkeys offer a more secure and user-friendly way to authenticate human identities, we need a new paradigm for authenticating, managing and securing AI agent identity and access in an MCP-enabled world.

This isn’t about stifling innovation; it’s about enabling it securely. An identity-first security strategy for Agentic AI using MCP should encompass:

Granular, Zero-Trust Access for AI Agents: Each AI agent should have its own distinct identity with the principle of least privilege strictly enforced. Permissions should be context-aware, time-bound, and specific to the task at hand.
Robust Authentication and Authorization for MCP Components: Every client, host, and server participating in the MCP ecosystem must be strongly authenticated. Authorization policies must govern what tools an agent can discover and invoke.
Continuous Monitoring and Anomaly Detection: Track the activities of AI agents. What data are they accessing? What tools are they using? How frequently? Deviations from baseline behaviour based on specific use cases could indicate a compromise or misuse.
Secure Tool Vetting and Lifecycle Management: Implement processes for vetting MCP tools before they are integrated. Monitor for updates and re-evaluate their security posture regularly.
Input Sanitization and Output Validation: Treat all data exchanged via MCP with suspicion. Sanitize inputs to agents and validate outputs from tools to prevent injection attacks and ensure data integrity.
Clear User Consent and Transparency: Users need to understand what capabilities an AI agent has and what data it’s accessing on their behalf, especially when MCP allows broad access to tools and information.

The Model Context Protocol holds the key to unlocking the next wave of AI innovation. It promises a future of seamlessly interconnected, intelligent systems. However, this future can only be realized if we build it on a foundation of trust and security, anchored by a robust, identity-centric approach. Given the potential for AI agents and Agentic AI systems to quickly multiply and exponentially increase in number and deployments across industries and verticals, it is imperative that we keep an identity-first security startegy from the onset rather than re-visiting the security angle at a later time.

As AI agents become more autonomous and deeply embedded in our digital lives and enterprise workflows, ensuring we know who (or what) is accessing what data, and why, becomes not just important, but absolutely critical. The conversation MCP enables is powerful, but only if we can secure the participants and the dialogue itself.

May 27, 2025 by Rohan Agentic AI Cybersecurity 0

The New Faces in the Digital Workplace: Why Your Agentic AI Needs Its Own Identity (and How to Secure It)

The water cooler conversations are changing. We’re not just talking about new human colleagues anymore; we’re discussing the latest AI agent that automated a complex workflow, or the one that drafted a surprisingly insightful market analysis. Agentic AI – autonomous systems capable of reasoning, planning, and executing tasks – is no longer a far-off concept. It’s rapidly becoming an integral part of our digital workforce.

But let’s pause for a moment. As these sophisticated “digital coworkers” take on more responsibilities, a critical question emerges: how are we managing their identities? And more importantly, how are we securing them?

If your answer involves shared accounts, embedded credentials, or simply hoping for the best, then we need to talk. Because treating agentic AI like a simple tool, rather than the distinct digital entity it is, is a security blind spot rapidly turning into a chasm.

Goodbye “Borrowed” Access, Hello Non-Human Identities

Remember the bad old days of everyone knowing the admin password? Or generic “service-account-01” having god-mode access across half your network? We’ve (mostly) moved on from those practices for human users and even traditional service accounts because we recognized the inherent risks: zero accountability, sprawling privileges, and a nightmare for auditing.

Agentic AI, with its ability to act independently and make decisions, magnifies these risks exponentially.

Think about it:

Accountability: If an AI agent makes a critical error or is compromised, how do you trace its actions if it’s operating under borrowed or generic credentials?
Least Privilege: Does an AI agent designed to schedule meetings really need access to your entire CRM database? Without a distinct identity, enforcing the principle of least privilege becomes a guessing game.
Lifecycle Management: As AI models are updated, retired, or repurposed, how do you manage their access rights effectively without a clear identity to govern?

The writing’s on the wall: Agentic AI needs to be treated as a first-class Non-Human Identity (NHI). Each agent, just like each human employee or critical server, requires its own unique, manageable, and auditable identity.

Securing the Future with Identity Security Posture Management (ISPM)

So, we’ve established that agentic AI needs its own ID card in the digital world. Great. But how do you manage these new identities at scale and ensure they don’t become the next attack vector?

This is where Identity Security Posture Management (ISPM) steps into the limelight.

Just as an identity-first security strategy has become crucial for human users, ISPM extends this philosophy to all identities – including our increasingly autonomous AI colleagues. ISPM isn’t just about passwords or multi-factor authentication (MFA); it’s a comprehensive approach to discovering, assessing, and improving the security of all identities and their entitlements within your ecosystem.

Here’s how ISPM can help you secure your agentic AI systems:

Discovery & Visibility: You can’t secure what you can’t see. ISPM helps you identify and inventory all AI agents operating within your environment, bringing them out of the shadows and into your security framework. Who are these agents? What are they designed to do?
Risk-Based Assessment: Once identified, ISPM tools can analyze the permissions and access rights granted to each AI agent. Are they over-privileged? Do they have dormant, unnecessary access? Are their “behavioural” patterns consistent with their intended purpose?
Policy Enforcement & Governance: ISPM allows you to define and enforce granular access policies specifically for AI agents. This ensures the principle of least privilege is consistently applied, limiting the potential blast radius if an agent is compromised. Think specific roles, time-bound access, and purpose-based permissions.
Continuous Monitoring & Anomaly Detection: Agentic AI, by its nature, will interact with data and systems. ISPM solutions can monitor these interactions, flagging anomalous activities that might indicate a compromised agent or misuse. Is your marketing AI suddenly trying to access financial records? Red flag.
Automated Remediation & Lifecycle Management: When posture drift occurs or vulnerabilities are detected, ISPM can help automate remediation actions, such as revoking excessive permissions or isolating a suspect AI agent. It also supports the full lifecycle management of AI identities – from secure provisioning to timely de-provisioning when an agent is retired.

An Identity-First Future for All Entities

The rise of agentic AI systems isn’t something to fear; it’s an opportunity to innovate and achieve unprecedented efficiency. However, this exciting future must be built on a foundation of trust and security.

By recognizing agentic AI as distinct non-human identities and leveraging the power of Identity Security Posture Management, we can confidently integrate these powerful new capabilities into our operations. It’s about extending the robust identity principles we’ve championed for years to this new class of digital actors.

The future of work involves humans and AI collaborating more closely than ever. Ensuring every entity, human or artificial, has a secure, managed identity isn’t just good practice – it’s essential for navigating the evolving digital landscape with confidence. It’s time to make sure our security posture keeps pace with our innovation.

May 22, 2025 by Rohan Uncategorized 0

Identity is King, But Who’s Watching the Throne? Securing Human and Non-Human Identities with Identity Security Posture Management (ISPM)

We’ve talked a lot about putting identity at the center of your security strategy – and for good reason. In a world of disappearing perimeters and exploding numbers of digital interactions, knowing who is accessing what is paramount. But let’s pause for a moment and ask a critical, almost philosophical question: if our identity systems are the gatekeepers, who’s watching the watchers themselves?

It’s a question that has echoed through history, from Plato to Roman satirists, and it’s incredibly relevant to today’s cybersecurity landscape. Your identity infrastructure – the complex web of directories, authentication systems, privileged access management tools, and all the policies holding them together – is the very foundation of your security. If this foundation has cracks, the entire house is at risk. This is precisely why establishing an Identity Security Posture Management (ISPM) strategy isn’t just a good idea; it’s essential.

Think about it. We’re rightly concerned with verifying every user, every device, every application. We’re moving towards a more secure, passwordless future with things like passkeys, and championing an identity-first approach to security. But what if the systems managing these identities are misconfigured, over-privileged, or riddled with dormant accounts? What if the “watchers” themselves are vulnerable?

The Dual Challenge: Human and Non-Human Identities

The complexity multiplies when you consider the sheer diversity of identities we’re now managing. It’s not just about Bob from accounting or Sarah from sales anymore.

Human Identities: These are your employees, contractors, partners, and customers. The risks here are well-understood, ranging from weak or stolen credentials to insider threats and social engineering. Ensuring proper lifecycle management, least privilege access, and robust authentication for humans is a constant battle.
Non-Human Identities: This is where things get really interesting, and often, much more alarming. We’re talking about service accounts, API keys, machine identities, application credentials, and identities for IoT devices and RPA bots. These non-human identities often outnumber human ones by a significant margin. They typically have broad, often excessive, permissions and are frequently overlooked or poorly managed. A compromised machine identity can be a golden ticket for an attacker, allowing them to move laterally, access sensitive data, and deploy malware, often completely undetected because, well, who’s closely watching the machines’ credentials?

If the systems governing these human and non-human identities are not meticulously secured, monitored, and managed, they become prime targets. Attackers are smart; they know that compromising the identity infrastructure itself provides the ultimate skeleton key to your kingdom.

Why Your Current Approach Might Not Be Enough

Many organizations have invested heavily in identity and access management (IAM) solutions, and that’s great. But IAM tools are primarily focused on enabling access and enforcing policies. ISPM, on the other hand, is about continuously assessing the security posture of your entire identity fabric. It’s about proactively identifying and remediating the hidden risks, misconfigurations, and vulnerabilities within your identity systems themselves.

Without a dedicated ISPM strategy, you’re likely flying blind to critical issues like:

Privilege Creep: Permissions that accumulate over time, far exceeding what’s necessary.
Dormant Accounts: Forgotten accounts that are ripe for takeover.
Misconfigured Policies: Settings that inadvertently create security gaps.
Over-Privileged Service Accounts: Non-human identities with excessive access rights.
Weak Authentication for Infrastructure Components: The identity systems themselves not being properly secured.
Lack of Visibility: Not knowing the full extent of all identity types and their entitlements.

Enter Identity Security Posture Management (ISPM)

ISPM provides the “watcher for your watchers.” It offers a dedicated layer of security focused on the integrity and resilience of your identity infrastructure. A robust ISPM strategy typically involves:

Comprehensive Discovery: Continuously identifying all human and non-human identities and their entitlements across your entire hybrid and multi-cloud environment.
Risk Assessment & Prioritization: Analyzing identities and configurations for vulnerabilities, misconfigurations, and risky permissions, then prioritizing them based on potential impact.
Automated Detection: Using analytics and machine learning to detect anomalies, policy violations, and emerging threats within the identity infrastructure.
Guided Remediation: Providing clear, actionable steps to fix identified issues, often with automation capabilities.
Continuous Monitoring & Governance: Ensuring that your identity security posture remains strong over time through ongoing monitoring, reporting, and adherence to defined governance policies.

It’s Time to Secure the Foundation

Just like we wouldn’t build a fortress on shaky ground, we can’t afford to have an identity-first security strategy reliant on an insecure identity infrastructure. The principle of “quis custodiet ipsos custodes?” isn’t about fostering distrust; it’s about implementing robust checks and balances.

By adopting an ISPM strategy, you’re not just adding another layer of security; you’re reinforcing the very core of your defenses. You’re ensuring that the systems responsible for authenticating and authorizing every access request are themselves secure, resilient, and trustworthy.

So, as you continue your journey towards a stronger, identity-centric security model, take a moment to consider who, or rather what, is watching your watchers. The answer should be a comprehensive Identity Security Posture Management strategy.

May 21, 2025 by Rohan identity security 0

Identity is core to any good security strategy!

Technology is changing at a rapid pace, with the adoption of cloud, digital transformation and a hybrid work environment, users are accessing data and resources from anywhere at anytime and expect a seamless access experience while ensuring their data is protected against cyberthreats. The traditional perimeter based network security can no longer work to secure access to resources in the public and private cloud environments. Identity has become the new security perimeter.

The concept of identity-based threats has become increasingly prevalent in today’s digital era, encompassing a range of malicious activities designed to compromise personal or organizational information. Among the most common manifestations of these identity-based threats are phishing, social engineering, and credential theft. Phishing, for instance, involves deceiving individuals into revealing sensitive data, typically through emails or SMS that appear to come from trustworthy sources. Social engineering exploits psychological manipulation and analyzing publicly available information on social networks and other public sites to gain unauthorized access, while credential theft involves stealing login details either through an attack or by purchasing them on the dark web from a previous breach.

In fact, bad actors have also shifted focus to utilizing identity as the initial attack vector in majority of breaches. Phishing and credential compromise rate as the top 2 initial attack vectors according to a recent Cost of a Data Breach Report by IBM and Ponemon.

While organizations have shifted to implementing multi-factor authentication (MFA) and Passwordless authentication, attackers have also evolved to develop ways to bypass MFA and launch account take over (ATO) attacks such as MFA prompt bombing, SIM swapping, Adversary in the Middle, etc. In addition the evolution of gen AI and a dark web marketplace offering services such as phishing-as-a-service has made it easier for attackers to launch targeted attacks against organizations of all sizes.

Building a Resilient Identity Security Framework

Creating a resilient identity security framework is essential for organizations to safeguard their data and resources against the ever-evolving identity threats landscape. Developing comprehensive security policies to secure identities forms the foundation of such a framework. These policies should address various facets of identity security, from user authentication protocols to access control measures across users by tying in contextual access information across non-human entities such as workloads, SaaS applications, virtual machines, APIs, containers, chatbots and more. Ensuring that these policies are deeply embedded within the organization’s operational procedures is critical for their efficacy.

Continuous security audits to understand the organization’s identity security posture and risk play a pivotal role in identifying potential vulnerabilities and gaps that could be exploited by malicious actors. By proactively reviewing and updating security measures, organizations can actively mitigate risks associated with identity threats.

Another crucial element in building a resilient identity security framework is the integration of continuous monitoring systems in order to understand the overall risk posture as it changes over time. These systems should provide real-time visibility into user activities, their risk profile based on how their user profile has been configured, their role and access to various resources as well as understanding how to prioritize high risk users based on potential attack paths that can lead to a breach. Leveraging advanced technologies such as artificial intelligence and machine learning can substantially improve the accuracy and efficiency of these monitoring efforts.

By staying informed and actively monitoring their identity security risk posture, and proactively responding to issues by remediating user identity configuration issues such as ineffective MFA, least privilege access violations, organizations can adapt their security strategies to counter new and sophisticated attack vectors effectively.

May 19, 2025 by Rohan identity security 0