Cryptographic Risk AI

Every enterprise runs on cryptography — encryption protocols, certificates, keys, and algorithms embedded across applications, infrastructure, and data in transit. Most organizations have no complete picture of what cryptography they are actually using, where it lives, or how much of it is vulnerable. That gap matters now for two converging reasons: the scale of AI-driven threats to cryptographic infrastructure has grown dramatically, and the post-quantum migration clock is running. NIST finalized its first three post-quantum cryptography standards in August 2024; quantum-vulnerable algorithms are targeted for complete transition by 2035; and a full PQC migration takes 2–5 years for most organizations — meaning the window to act is open now. AI-driven cryptographic risk management is the discipline of using AI to close the visibility gap: discovering cryptographic assets at enterprise scale, prioritizing vulnerabilities by actual risk, and orchestrating remediation across complex environments. SandboxAQ’s AQtive Guard is built for exactly this workflow.

The cryptographic visibility problem

Modern enterprise environments are cryptographically complex in ways that most security teams have not fully mapped. Applications, services, endpoints, cloud workloads, and infrastructure components each carry cryptographic assets — keys, certificates, algorithms, libraries, and protocols — deployed at different times, by different teams, under different standards, and subject to different expiration and rotation schedules. The aggregate is not a system anyone designed; it is the accumulated output of years of independent decisions across an organization.

Manual inventory of this landscape is not feasible at enterprise scale. Most organizations discover expired or misconfigured certificates only when a service fails. Vulnerable encryption algorithms may remain in production for years simply because no one has a complete view of where they are in use. Policy compliance — against standards like FIPS 140-2 or PCI DSS — is difficult to enforce continuously when the inventory is incomplete and constantly changing.

The rise of AI agents has compounded this problem significantly. With AI agents now exceeding one billion across enterprise environments, the number of non-human identities (NHIs) — autonomous processes, service accounts, API credentials, and AI-driven workflows — carrying cryptographic credentials has grown faster than traditional identity and certificate management tools were designed to handle. Each NHI is a potential attack surface if its credentials are stale, misconfigured, or quantum-vulnerable. The machine-to-machine communication surface has grown in a way that has outpaced human-administered oversight.

The practical consequence is that organizations cannot remediate what they cannot see. And without a complete, continuously updated picture of their cryptographic footprint, they cannot plan or execute a post-quantum migration rationally — because they do not know the scope of what needs to change.

Why the post-quantum threat changes the urgency

The case for fixing the cryptographic visibility problem has existed for years. What has changed is the timeline pressure. The “harvest now, decrypt later” strategy — sometimes called HNDL — means that adversaries are already intercepting and storing encrypted communications today: financial records, government data, corporate trade secrets, healthcare records. The decryption has not happened yet, but it does not need to. Any data encrypted with RSA, ECC, or other quantum-vulnerable algorithms that carries long-term sensitivity is already compromised in transit, waiting for the moment when a cryptographically relevant quantum computer (CRQC) reaches sufficient capability. Gartner has warned that “quantum computing will render traditional cryptography unsafe by 2029.” After an eight-year evaluation process, NIST published ML-KEM, ML-DSA, and SLH-DSA as finalized post-quantum cryptography standards in August 2024, with a fifth algorithm, HQC, selected in March 2025. For a detailed overview of the PQC standards and what they mean for enterprise transition planning, see post-quantum cryptography and the PQC overview.

The critical point for cryptographic risk management is sequencing. A PQC migration cannot begin with algorithm replacement; it has to begin with discovery. An organization that does not know which systems are running RSA-2048 cannot prioritize which ones to migrate first. An organization that does not have a live inventory of its certificates cannot manage a coordinated cutover without service disruption. The inventory step is not a prerequisite to be completed once and set aside — it is an ongoing operational requirement that must be maintained throughout the multi-year transition.

What AI-driven cryptographic risk management does

AI addresses the cryptographic visibility and remediation problem at the scale and speed that manual processes cannot reach.

Discovery

Automated scanning across networks, applications, filesystems, endpoints, and cloud environments builds a complete, continuously updated inventory of cryptographic assets: keys, certificates, algorithms, libraries, protocols, and their dependencies. Unlike point-in-time audits, AI-driven discovery runs continuously — capturing new assets as they are deployed and flagging changes as they occur. Integrations with existing security and IT management platforms pull cryptographic data directly from the tools organizations already use, without requiring separate agent deployment on every system.

Analysis and prioritization

A raw cryptographic inventory is not the same as actionable risk information. AI applies risk scoring to the inventory — identifying quantum-vulnerable algorithms, expired or near-expiry certificates, policy violations against frameworks like FIPS 140-2 and PCI DSS, anomalous behavior patterns, and non-human identities with stale or misconfigured credentials. The output is a prioritized list of what to remediate first, based on actual risk exposure rather than raw inventory counts. This is where the difference between a traditional scanning tool and an AI-driven platform is most apparent: the former tells you what exists; the latter tells you what matters.

Remediation orchestration

Automated remediation workflows handle the operational work of fixing identified vulnerabilities: credential rotation, certificate renewal, algorithm replacement, and lifecycle management of NHI credentials. Integration with issue trackers, CMDB, and certificate management platforms closes the loop between discovery and resolution — ensuring that identified vulnerabilities generate tracked remediation actions, not just alerts. Automated remediation reduces the manual overhead of managing a large, dynamic cryptographic environment and accelerates time to resolution.

Continuous monitoring

The cryptographic landscape of a large enterprise changes continuously as applications are updated, infrastructure is modified, and new services are deployed. A cryptographic inventory is not a project deliverable; it is an ongoing operational function. AI-driven platforms maintain the inventory in real time and surface new risks as they emerge, rather than waiting for the next scheduled audit to reveal them.

Cryptographic risk AI with AQtive Guard

SandboxAQ’s AQtive Guard platform applies this approach across the full cryptographic risk management lifecycle through two core modules, powered by SandboxAQ’s Cyber Large Quantitative Model.

The Discover module builds a complete, continuously updated inventory of NHIs and cryptographic assets — keys, certificates, algorithms, and libraries — across networks, applications, filesystems, and cloud environments including AWS and GCP. It integrates directly with the CrowdStrike Falcon platform: one-click ingestion pulls cryptographic and NHI data from endpoints without requiring separate deployment, translating to operational value from the first hour of use. Integrations with Tanium, ServiceNow, development tools, issue trackers, and CMDB close the loop between discovery and the systems teams already use to manage their infrastructure. The platform cross-references cryptographic usage across the environment and identifies vulnerabilities and policy violations against frameworks including FIPS 140-2 and PCI DSS. It also flags algorithms that do not meet quantum-resistant standards — providing the foundational visibility that PQC migration planning requires.

The Protect module orchestrates automated remediation and lifecycle management: credential rotation, certificate renewal, revocation, and the enforcement of protection policies across the environment. Marc Manzano, General Manager of Cybersecurity at SandboxAQ, has stated: “Being able to automatically remediate vulnerabilities and policy violations identified is crucial to decrease time to mitigation and prevent potential breaches within the first day of use of our software.” The platform also integrates with Palo Alto Networks for additional enforcement and monitoring coverage.

The Cyber LQM applies to the unified inventory across both modules: advanced metadata filtering and clustering enables efficient exploration of large, complex cryptographic environments; root-cause analysis surfaces the systemic issues behind individual vulnerabilities; and prioritized, actionable insights reduce false positives so remediation resources are directed where they matter. An integrated GenAI assistant supports security teams in navigating relevant regulatory and compliance frameworks as requirements evolve.

For teams evaluating AQtive Guard alongside the broader PQC software landscape, the PQC software guide covers what to look for across vendors. SandboxAQ maintains affiliations with NIST’s NCCoE, the GSMA’s Post-Quantum Telco Network (PQTN), the Linux Foundation’s Post-Quantum Cryptography Alliance (PQCA), and MITRE’s PQC Coalition. Cybersecurity teams at Accenture, Deloitte, EY, and Carahsoft are trained to deploy AQtive Guard as part of SandboxAQ’s partner network. For SandboxAQ’s broader commentary on emerging cyber threats, see the Jack Hidary on cyber threats Insights article.

Production deployments

‍

AQtive Guard has been deployed in production across enterprise and government environments.

In 2024, SoftBank Corporation deployed AQtive Guard across its enterprise network. The deployment uncovered unnoticed vulnerable encryption and certificate issues across a large-scale infrastructure — the kind of visibility gap that manual auditing processes routinely miss in environments of that size and complexity.

In December 2025, SandboxAQ announced a five-year agreement with the U.S. Department of War (DoW) Chief Information Officer to deploy AQtive Guard for comprehensive, automated cryptographic discovery and inventory (ACDI) across DoW systems. The agreement is described as a foundational step in the DoW’s managed transition to post-quantum cryptography. It follows a successful earlier demonstration of SandboxAQ’s capabilities during a prototype project with DISA Emerging Technology’s QRC PKI program. The DoW partnership opens a path for other DoW agencies to access AQtive Guard as their own PQC migration planning matures.

Frequently asked questions

What is cryptographic risk AI?

Cryptographic risk AI is the use of AI to discover, inventory, prioritize, and remediate encryption vulnerabilities across enterprise infrastructure. It automates the process of building and maintaining a complete cryptographic asset inventory — keys, certificates, algorithms, and libraries — and applies AI-driven risk analysis to identify what is vulnerable, what violates policy, and what requires remediation before emerging threats, including quantum computing, make that remediation urgent.

How do organizations discover cryptographic vulnerabilities?

AI-driven cryptographic discovery platforms scan continuously across networks, applications, filesystems, endpoints, and cloud environments to build a complete, up-to-date inventory of cryptographic assets and their dependencies. They integrate with existing security platforms — including endpoint detection tools, CMDB, and certificate managers — to pull data from systems already in use. The inventory is then analyzed to surface vulnerable algorithms, expired certificates, policy violations, and anomalous behavior patterns.

What is the difference between cryptographic discovery and remediation?

Cryptographic discovery is the process of building and maintaining a complete inventory of cryptographic assets across an organization’s environment — identifying what exists, where it is used, and what its current security status is. Cryptographic remediation is the process of fixing what discovery identifies as vulnerable: rotating credentials, renewing certificates, replacing weak algorithms, and enforcing policies. Both are necessary; discovery without remediation leaves vulnerabilities exposed, and remediation without discovery cannot be systematically applied to everything that needs it.

Why is AI needed for cryptographic risk management?

Enterprise cryptographic environments are too large, too dynamic, and too distributed for manual inventory and remediation to be feasible. Large organizations run thousands of applications and services, each with cryptographic assets deployed and modified independently over time. AI-driven platforms can scan continuously at scale, integrate across the tools organizations already use, apply risk scoring to prioritize what matters, and automate remediation workflows — compressing the time between identifying a vulnerability and resolving it from weeks to hours.

What is crypto agility?

Crypto agility is the ability of an organization to rapidly update or replace the cryptographic algorithms it uses across its infrastructure — without service disruption and without requiring a complete system redesign. It is a design property, not a one-time action: a cryptographically agile organization can respond to newly discovered vulnerabilities or new standards requirements by deploying updated algorithms at speed. Achieving crypto agility requires a complete, continuously maintained cryptographic inventory and automated remediation workflows — the operational foundation that AQtive Guard is built to provide.

‍

Explore SandboxAQ’s cryptographic risk platform

• AQtive Guard — AI-driven cryptographic discovery, inventory, and remediation
• Large Quantitative Models — the AI platform powering AQtive Guard’s Cyber LQM
• SandboxAQ

‍