Emerging technologies widen attack surfaces across AI, cloud and IoT

At a glance

What this is: This is an analysis of how emerging technologies broaden cyber risk, with AI, IoT, cloud, blockchain and quantum computing each creating distinct attack paths.

Why it matters: It matters because IAM, PAM, NHI governance and adjacent security programmes must adapt controls for weak authentication, misconfigured access and impersonation risks across new technology stacks.

By the numbers:

• 79% of organisations have experienced secrets leaks, with 77% of these incidents resulting in tangible damage.
• Only 5.7% of organisations have full visibility into their service accounts.
• 97% of NHIs carry excessive privileges, increasing unauthorised access and broadening the attack surface.

👉 Read INTIGRITI's analysis of emerging technologies and their security implications

Context

Emerging technologies often raise capability faster than governance, which is why AI, IoT, cloud and blockchain frequently widen the control gap before teams have updated access, verification and monitoring models. The primary issue is not adoption itself, but the mismatch between new operational behaviour and legacy security assumptions, especially where identity, credentials and trust are involved.

In practical terms, this becomes an IAM and NHI problem as soon as cloud services, machine-to-machine access, API-driven workflows or AI-enabled interactions depend on weak authentication or over-permissioned accounts. 90% of IT leaders say properly managing NHIs is essential for a successful zero-trust implementation, which is why these risks are typical rather than exceptional for modern enterprise environments.

The 3D-printed facial mask example also shows that attackers increasingly target the trust layer rather than only software flaws. That is atypical in technique, but typical in direction: adversaries keep looking for whichever control was assumed to be reliable rather than continuously verified.

Key questions

Q: How should security teams handle identity risk when legacy infrastructure and AI threats collide?

A: They should treat this as a single governance programme with two time horizons. Legacy authentication, weak credential hygiene, and manual response need immediate remediation, while AI-driven and agentic risks require forward planning. The mistake is building separate control tracks that ignore how the same identity estate is exposed today and targeted tomorrow.

Q: Why do AI systems complicate zero trust assumptions?

A: AI systems complicate zero trust because they can act continuously, reuse credentials across sessions, and operate with delegated access that outlives the original request. Zero trust still applies, but the enforcement point must move closer to runtime identity, session context, and policy-based revocation. Otherwise, trust decisions become stale too quickly.

Q: What do organisations get wrong about biometric authentication and deepfakes?

A: They often assume a biometric match proves that a live human is present. In practice, biometrics only confirm similarity to stored reference data unless the system also checks liveness and resists injection attacks. Without those controls, a convincing synthetic feed can satisfy the biometric layer and still be fraudulent.

Q: How do teams reduce risk from machine identities in cloud environments?

A: Start with inventory, because you cannot govern what you cannot see. Then remove default credentials, shorten token lifetimes, restrict privileges to the task at hand and monitor for unusual use patterns. Machine identities should be treated as production assets with owners, scope limits and lifecycle controls.

Technical breakdown

Why emerging technologies expand the attack surface

Every new platform changes where trust is established and where failure is possible. Cloud services add shared responsibility and configuration complexity, IoT adds large fleets of weakly managed devices, AI adds automated decision paths, and blockchain adds immutable code that can still contain exploitable logic. The common thread is that defenders inherit more entry points, more identities and more dependencies. Security breaks when organisations treat these systems as extensions of older architectures rather than as environments with new trust boundaries and more volatile access paths.

Practical implication: Map each emerging technology to its trust boundary, then review where identity, authentication and authorization assumptions no longer hold.

AI-enabled attacks and verification bypass

AI changes both attacker speed and attacker quality. It can generate convincing phishing content, support deepfake impersonation and adapt malware to evade detection. The key security issue is not that AI creates entirely new crime patterns, but that it makes deception cheaper, faster and more scalable. That puts pressure on verification controls such as MFA, step-up authentication and human approval workflows, especially where executive impersonation or automated social engineering can bypass normal judgment.

Practical implication: Harden verification paths for high-risk requests and assume text, voice and video can no longer be trusted on their own.

Cloud and IoT misconfiguration as a control failure

Cloud and IoT risk often stems from weak access control rather than sophisticated exploitation. Public exposure through misconfigured permissions, default credentials, poor firmware hygiene or unsegmented networks creates conditions where attackers can move from initial access to data exposure or service disruption quickly. In identity terms, the control failure is usually over-permission, poor lifecycle management or weak authentication on accounts, keys and device identities. That is why NHI governance becomes central to cloud security, not peripheral to it.

Practical implication: Prioritise least privilege, secret hygiene and lifecycle controls for every machine identity that touches cloud or IoT assets.

NHI Mgmt Group analysis

Emerging technology risk is increasingly an identity governance problem. AI, cloud and IoT introduce more machine identities, more delegated access and more opportunities for trust to be assumed rather than verified. When verification and authorization do not scale with the technology footprint, the result is not just broader attack surface but weaker governance over who or what can act. The practitioner conclusion is simple: modern security programmes need identity controls that understand runtime behaviour, not just login events.

Deepfake and impersonation attacks expose a verification trust gap. The article's 3D-printed mask example is a reminder that identity assurance fails when teams rely on a single recognition factor or static trust signal. Biometric and human verification processes need layered challenge, fallback and exception handling because attackers target the decision point, not just the credential. The practitioner conclusion is that assurance must be continuous, not point-in-time.

Cloud security and NHI governance now overlap by default. Misconfigured permissions, exposed APIs and third-party dependencies all become identity issues as soon as workloads, service accounts and tokens are part of the access path. This is where OWASP-NHI thinking belongs alongside cloud posture management, because the real risk is uncontrolled privilege on machine identities. The practitioner conclusion is to treat NHIs as first-class assets in cloud control design.

Quantum risk is a long-horizon encryption governance issue, not a distant science project. The article correctly frames post-quantum readiness as a planning problem because long-lived sensitive data can outlast today's cryptography. Organisations that retain data for years need migration plans for crypto agility, inventory visibility and replacement timing. The practitioner conclusion is to start cataloguing where strong encryption is needed now so future transition work is not chaotic.

Bug bounty and defensive AI are compensating controls, not substitutes for architecture. The article highlights the value of crowdsourced testing and AI-assisted detection, but those mechanisms only reduce exposure if core controls already exist. Discovery is useful when governance, access control and monitoring can turn findings into action. The practitioner conclusion is to use these tools to validate control coverage, not to justify weak design.

What this signals

Emerging technology governance will increasingly be judged by how well teams control machine and delegated identities. The practical test is no longer whether a platform is modern, but whether its identities are visible, scoped and revocable across the full runtime. That makes NHI lifecycle discipline, supported by the NHI Lifecycle Management Guide, a baseline for cloud and AI programmes rather than a specialist add-on.

Verification trust gaps will keep widening as impersonation techniques get more convincing. Teams should expect more requests that look human, sound human or behave like trusted automation while still coming from adversarial sources. The operational response is stronger proofing at the decision point, better exception handling and tighter ties between identity assurance and incident response.

AI and cloud programmes will need closer alignment between security architecture and identity governance. When access paths become more dynamic, static reviews are not enough, and the control model has to account for runtime context, ownership and revocation speed. That is where NHI visibility, access reviews and privileged access constraints stop being support functions and start becoming core controls.

For practitioners

• Map new technology to identity controls Create a control matrix for AI, IoT and cloud services that identifies the human and non-human identities in scope, then assign authentication, authorization and audit requirements for each.
• Harden verification for impersonation risk Add step-up checks, out-of-band approval and exception review for executive requests, finance actions and helpdesk resets that could be targeted by deepfake or social engineering attacks.
• Review cloud and IoT access for over-permission Inventory service accounts, device credentials and API tokens connected to cloud and IoT environments, then remove standing privilege and enforce least privilege at the point of use.
• Build a post-quantum migration inventory Document which systems store long-lived sensitive data, which cryptographic algorithms protect it, and where replacement timelines will be needed as quantum-safe standards mature.
• Use bug bounty findings to validate controls Feed external findings into remediation workflows that verify patching, segmentation, authentication and monitoring rather than treating reports as standalone issues.

Key takeaways

• Emerging technologies widen attack surfaces by increasing the number of trust decisions security teams must govern.
• The biggest exposure is often not the technology itself but weak identity assurance, over-permission and misconfiguration around it.
• Security programmes that extend visibility, lifecycle control and verification to machine identities will absorb these risks more effectively.

Key terms

• Non-Human Identity (NHI): A digital identity assigned to a non-human entity such as a software application, service account, API key, bot, machine, or AI agent that enables it to authenticate and interact with systems without direct human involvement. NHIs now outnumber human identities in most enterprises by 25 to 50 times.
• Activation Trust Gap: The activation trust gap is the difference between trusting data because it is protected and governing it because it is being reused. It appears when organisations move data from backup or archival systems into AI pipelines without reapplying access, sensitivity, and consumer controls.
• Cloud misconfiguration: A security failure caused by incorrect permissions, exposure settings, or integration design in cloud services. It is often less about the cloud platform itself and more about access paths that were created quickly, left broad, and never fully revalidated against actual business need.
• Post-Quantum Cryptography Readiness: Post-quantum cryptography readiness is the capacity to test, deploy, and manage quantum-resistant or hybrid algorithms before they are urgently required. It depends on flexible architecture, limited hard-coded cryptography, and operational processes that can absorb repeated algorithm change.

What's in the full article

INTIGRITI's full article covers the operational detail this post intentionally leaves for the source:

• Examples of AI-enabled phishing, deepfake impersonation and adaptive malware that expand the attacker toolkit.
• Cloud and IoT control recommendations for patching, segmentation, authentication and misconfiguration monitoring.
• Blockchain threat examples including smart contract flaws, 51% attacks and privacy exposure.
• Long-horizon quantum risk discussion for encryption planning and cryptographic transition readiness.

👉 INTIGRITI's full article expands on the threat patterns and mitigation themes across AI, IoT, cloud, blockchain and quantum computing.

Deepen your knowledge

NHI Foundation Level course, the industry's only accredited NHI security programme, covers NHI governance, machine identity security and secrets management. It helps security practitioners translate identity controls into practical lifecycle and access decisions.