Although CTEM has enjoyed a lot of acceptance in the business world as a strategy for managing cyber risks, many security teams in the enterprise space have had problems executing CTEM due to issues such as workflow disconnections, outdated tools, and manual friction. AVA Agentic OS addresses this gap by orchestrating specialized teams of artificial intelligence agents to autonomously discover, validate, and eliminate threat debt.

“Organizations don’t have a CTEM strategy problem they have a CTEM execution problem,” said Carl Wright, Chief Commercial Officer at AttackIQ. “AVA OS is the operational layer that makes Continuous Threat Exposure Management executable. It coordinates specialized AI agents that continuously perform the work required to discover, validate, and reduce threat debt. The next generation of cybersecurity operations will be built on specialized, mission focused agentic systems.”

Also Read: Harness Integrates Agentic AI Security with Google Cloud Apigee

Unifying Security Operations into Autonomous Missions

Rather than relying on isolated task automation, AVA Agentic OS deploys mission-focused AI agent teams across six core cybersecurity domains:

Cyber Threat Intelligence (CTI): Continuously ingests and analyzes global threat actor techniques to prioritize emerging exposures.

Security Validation: Emulates real-world adversary behavior across production networks to verify defensive control efficacy.

Detection Engineering: Automatically updates and refines security telemetry rules to catch novel threat variants.

Control Optimization: Identifies gaps in firewall, endpoint, and identity coverage to optimize existing tech stack investments.

Threat Debt Reduction: Streamlines remediation pathways to neutralize critical attack paths before exploitation occurs.

AI Security Validation: Evaluates and safeguards enterprise AI infrastructure against prompt injection, data leakage, and adversarial manipulation.

Transforming Security Posture Through Agentic Automation

AVA Agentic OS offers enterprise security teams the potential to completely abandon relying on vulnerability scanning by integrating threat validation, detection engineering, and remediation into smooth-flowing workflows. It is also possible for companies to use the system for continuous monitoring of their highest-value assets thereby being sure that defensive measures adjust themselves to the ever-changing threat environment.

AVA Agentic OS is available immediately for deployment. Enterprise security leads, Chief Information Security Officers (CISOs), and IT operations managers can evaluate platform specifications or request operational demonstrations by visiting the official AttackIQ digital portal.

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This offering seeks to meet the growing need to deal with an increasing number of autonomous AI applications deployed in various departments of corporations without coordination and supervision. The phenomenon is termed “agentic sprawl” and involves a number of risks for organizations because it can lead to a breach of data privacy, regulatory fines, and unplanned IT infrastructure costs.

Mitigating Risk in an Era of Autonomous AI Sprawl

Although enterprise software providers include proprietary AI functionality in their applications more often nowadays, it remains challenging for companies to impose consistent security policies across multi-vendor infrastructures. In order to manage the agentic sprawl, Rimini Govern for AI creates an independent management layer controlling the activity of AI agents on various platforms, including SAP, Oracle, and Salesforce.

Also Read: Resecurity Integrates Native Threat Intelligence with IBM QRadar SIEM

The service provides enterprise technology leaders with four primary operational safeguards:

Centralized Policy Enforcement: Manages user permissions, data access boundaries, and regulatory compliance protocols from a single dashboard.

Continuous Security and Privacy Guardrails: Shields sensitive corporate data from improper exposure, prevents unauthorized tool usage, and mitigates prompt injection risks.

Unified Auditability: Logs agent decision-making steps, tool calls, and data transactions to maintain compliance-ready records for external regulators.

Cost and Resource Optimization: Sets usage thresholds and monitors API consumption across underlying language models to eliminate unexpected cloud expenditure.

“Enterprises are deploying AI agents at an incredible pace, but without proper governance, this rapid adoption creates significant security, compliance, and financial risks. Rimini Govern for AI gives organizations the visibility and control they need to safely scale AI initiatives while protecting their enterprise data and technology investments.” Seth Ravin, Chairman and CEO of Rimini Street

Strategic Complement to the Rimini Consult Portfolio

Rimini Govern for AI expands the company’s broader consulting and managed services ecosystem. Designed to integrate seamlessly with existing enterprise systems, the solution allows organizations to adopt emerging agentic workflows without undergoing disruptive, capital-intensive platform migrations.

By establishing clear operational boundaries for autonomous software, the service helps chief information officers, chief security officers, and risk directors leverage artificial intelligence while maintaining strict operational control.

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As organizations rapidly deploy autonomous AI agents and large language model (LLM) applications, applying conventional application security protocols creates operational friction and critical blind spots. According to the findings of The Harness State of AI-Native Application Security Report, 74% of developers view security controls as one of the biggest barriers to innovation when it comes to AI. However, 62% of security professionals note that development teams fail to handle risks associated with AI-native applications. The new integration solves the problem through automation of security processes within developer processes.

“Organizations are innovating with agents at breakneck speed,” said Rahul Sood, GM of Application Security at Harness. “As they innovate and adopt new capabilities, they are forcing security teams to keep pace. This integration allows customers building on Apigee to seamlessly detect and protect agents as they are being deployed so developers can innovate confidently.”

Also Read: Cyberhaven Expands Data and AI Security Platform with ChatGPT and Claude

End-to-End Governance Across the AI Agent Lifecycle

By situating security controls within Apigee which manages traffic between end-users, foundational models, back-end APIs, and agentic tools organizations can secure new AI interaction patterns without building separate infrastructure. The platform delivers three core security layers:

Automated Agent Discovery: Continuously inventories AI assets, MCP servers, and connected tools as they deploy across the network. Agent Security Posture Management (Agent SPM) maps complex asset dependencies and evaluates organizational risks automatically.

Real-Time AI Firewall: Monitors live runtime activity to enforce security policies against prompt injection attempts, tool misuse, unauthorized agent-to-agent communication, and data exfiltration.

Comprehensive Activity Observability (AgentTrace): Maintains a complete audit trail across user prompts, AI reasoning steps, tool calls, and outputs to evaluate non-deterministic system behaviors.

“Securing AI agents and emerging technologies requires a paradigm shift in how we think about enterprise protection,” said Ritika Suri, Managing Director, AI & Data ISV Partnerships, Google Cloud. “Through our collaboration with Harness, we are bringing robust, agent-aware security to our joint customers without compromising on the agility to deliver solutions that help organizations innovate with confidence.”

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Security alerts flag activity that matches predefined detection rules, helping teams identify known threats and suspicious behavior, but they are only one lens into the environment. Threat hunting looks for everything else. Finding and remediating the emerging risks and coverage gaps that alerts were not designed to surface is a critical part of any security program. The problem: Traditional threat hunting demands specialized expertise, custom queries, and analyst time that most teams simply cannot spare. A single hunt can take 10 to 20 hours of work, so hunting tends to happen rarely, reactively, or not at all. AI Threat Hunter removes these barriers by turning hunting into an achievable, routine part of the SOC, with the team deciding what to hunt and the agent doing the work.

With more than 270 hunt packs mapped to real attacker behavior and MITRE ATT&CK, it doesn’t just query your tools. It investigates like a senior analyst, connecting evidence across your security stack and returning a complete, reasoned finding you can act on immediately. Each pack bundles 5 to 10 hunts that run together, so work that once consumed a team’s week now returns results in about one to two hours, with every step fully transparent and in your control. Unlike conversational hunting assistants or solutions that simply help analysts search data, AI Threat Hunter is a repeatable, scheduled operation that takes threat hunting from a nice-to-have to operationalized.

Also Read: Sumsub Introduces First AI-powered Trust Infrastructure as Compliance Operations Evolve 

AI Threat Hunter is already running federated, hypothesis-driven hunts across Splunk, Microsoft Sentinel, CrowdStrike NG-SIEM, Elastic, and Panther. Beyond active threats, each hunt surfaces posture insights such as visibility gaps, policy violations, and misconfigurations, along with detection opportunities. Teams can export threat reports and summaries in the UI, review hunt history, and rely on context memory that filters out known-benign activity in their environment. Additional capabilities are planned for the second half of 2026, including custom hypothesis-based hunts, more data sources, and tighter integration with Dropzone AI’s AI SOC Analyst to enable response actions.

“Attackers already operate at machine speed. Compute, not skill, is their bottleneck now, and defenders can’t out-hire that gap. AI Threat Hunter lets any team proactively identify undetected intrusions at the same speed, turning hunting from a once-a-year luxury into an operational habit and leveling a field that’s been tilting toward attackers for years. Analysts can now stay focused on the judgment only they can give rather than the mechanics of building and running hunts.” said Edward Wu, Founder and CEO of Dropzone AI.

More than 300 enterprises and MSSPs run the Dropzone AI platform today, and the results are consistent: teams reclaim hours and investigate more of their environment than ever. Zapier cut triage from 10 to 15 minutes to under two, an 85% reduction. UiPath saved 700+ analyst hours and cut false positives by 86%. Assala Energy hit 100% alert investigation and 5x faster response with a lean team. AI Threat Hunter brings that same machine-speed advantage to threat hunting, in beta alone it automated the equivalent of 200 years of hunting work.

“AI Threat Hunter ran this without pulling a single person off their queue, and by the end of it we had a clear, evidence-backed finding we could take straight to leadership. That’s the kind of leverage a security team actually needs,” said Greg Spillman, Cyber Fusion Center Technical Lead.

Source: BusinessWire

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The modern enterprise faces an increasingly sophisticated threat landscape where adversaries leverage automated “agentic” swarms to scan for vulnerabilities. Simultaneously, the proliferation of IoT, OT, and unmanaged devices has created a “visibility gap,” making a significant portion of environments invisible to traditional security agents.

The Need for Asset-Centric Contextualization
To be effective, threat intelligence must be contextualized against an organization’s specific asset landscape. Security teams must identify not just the existence of a threat, but which specific assets from medical equipment to manufacturing controls—are exposed to a specific actor’s techniques, tactics, and procedures.

Also Read: Box Unveils Agent Security Controls to Secure AI Operating Across Enterprise Content

The Armis from ServiceNow & Cyware Strength
Armis provides continuous, real-time asset intelligence through Armis Centrix, the Armis Cyber Exposure Management Platform. Cyware complements this with its advanced Threat Intelligence Contextualization Platform and Workflow Automation engine, Cyware Intelligence Suite, using Agentic AI to dynamically prioritize risk, reveal attack paths, and drive preemptive mitigation.

A Proactive Defense Supercharged by AI
The integration supercharges security operations by correlating asset profiles against global threat feeds and historical data specific to each customer and what matters to them right now. This enables automated responses based on real-time telemetry, ensuring high-value assets are shielded the moment a relevant, customer specific threat is identified.

“The integration of Armis and Cyware eliminates the silos between asset management and threat intelligence,” said Sachin Jade, Chief Product Officer at Cyware. “By providing asset-centric threat contextualization, we are giving CISOs the ‘ground truth’ they need. We aren’t just identifying risks; we are using AI-driven orchestration to remediate those risks before the adversary can pivot. This is the future of proactive defense.”

“This partnership empowers joint customers to bridge the critical gap between real-time asset visibility and automated threat response,” said Nadir Izrael, group vice president at Armis from ServiceNow. “Together, we are enabling security teams to shift from reactive, manual firefighting to a proactive, AI-driven defense that protects their entire attack surface.”

Cyware is leading the industry in Agentic AI-powered operationalized threat Intelligence and collective defense, helping security teams transform threat intelligence from fragmented data points to actionable, real-time decisions. We unify threat intelligence management, intel sharing and collaboration, as well as hyper-orchestration and automation eliminating silos and enabling organizations to outmaneuver adversaries faster and more effectively.

From enterprises to government agencies and ISACs, Cyware empowers defenders to turn intelligence into action.

Source: PRNewswire

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Also Read: ReliaQuest and OpenAI Partner to Accelerate Agentic Cyber Defense for the Enterprise

The ability of organizations to configure continuous threat intelligence data sources and analyze enriched security events inside the QRadar interface will bring significant time/savings and thereby enhance the performance of Security Operations Centers (SOCs). The partnership of QRadar with Resecurity in the release highlights the increasing need for interoperable solutions of cybersecurity, which is being done as businesses desire to strengthen their defenses while also benefiting from automated intelligence sharing and unified security operations through the integration of the cybersecurity tools. IBM QRadar already secures a large portion of Fortune-listed companies together with the state government agencies, and a native Resecurity plugin for QRadar will, in turn, enrich the platform’s cyber threat intelligence offerings and, at the same time, empower quicker and more precise security decisions. The integration also reinforces Resecurity’s broader strategy of extending compatibility with leading security platforms, enabling enterprises to operationalize actionable cyber threat intelligence and improve resilience against evolving cyber risks across complex IT environments.

Read More: Resecurity Introduces Native Integration with IBM QRadar SIEM for Threat Intelligence Sharing

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While traditional endpoint and browser controls can inspect sensitive data as it moves into AI tools, they often remain blind to existing data inside cloud-hosted GenAI environments including historical chat logs, custom GPT or Projects configurations, sharing permissions, and attached files. By connecting directly to native compliance APIs, Cyberhaven fills this visibility gap with automated data discovery, policy classification, and real-time incident response.

“Our integration with Anthropic brings Claude governance into the Cyberhaven platform, so customers can secure AI adoption without adding another isolated tool or workflow.”

Also Read: Forcepoint Launches AI Data Security Platform to Secure the Agentic Enterprise Data

Native Compliance Integrations Elevate Risk Context

The integrations allow enterprise security analysts to discover, classify, and govern sensitive business data across both major GenAI ecosystems without disrupting employee productivity:

Claude Enterprise Connector: Integrates via Claude Compliance APIs to continuously discover conversations, Projects, uploaded files, user accounts, and role permissions. The connector evaluates chat prompts and model responses against organizational content policies, raising flagged incidents directly in the Cyberhaven console with complete contextual evidence. Cyberhaven also links discovered data back to its origin, combining Claude activity with broader data lineage captured across the enterprise.

ChatGPT Enterprise Connector: Designed for ChatGPT Enterprise and Edu workspaces, this connector analyzes conversation transcripts, uploaded documents, and custom GPT knowledge files. It applies Cyberhaven’s advanced classification models to detect intellectual property, credentials, customer records, and regulated data.

“Our integration with the ChatGPT Enterprise Compliance API extends that visibility, helping customers support their governance and compliance processes alongside the existing security capabilities available in ChatGPT Enterprise.”

Unifying Security Across Human and Agentic Workflows

By expanding its cloud connector ecosystem, Cyberhaven enables organizations to apply unified data governance across every major corporate vector. Security teams can now apply consistent data loss prevention (DLP) and risk management rules across endpoints, web browsers, SaaS applications, cloud infrastructure, and generative AI platforms.

Both integrations support historical retro-scans within each platform’s data retention window as well as continuous forward scanning for newly created content. The ChatGPT Enterprise connector is available immediately via dedicated API keys in the Cyberhaven Console, while the Claude Enterprise connector is scheduled for general availability in August 2026.

Enterprise security leads, chief information security officers (CISOs), and compliance officers can evaluate connector capabilities, request a platform demonstration, or contact their account representative by visiting Cyberhaven’s official digital platform.

The post Cyberhaven Expands Data and AI Security Platform with ChatGPT and Claude appeared first on ITDigest.

The release comes as modern corporate workflows shift toward agentic AI models. Today, both human employees and autonomous software agents continuously feed sensitive intellectual property, customer data, and operational files into AI applications, LLMs, and enterprise copilots. Historically, managing data loss prevention (DLP) and governing artificial intelligence were treated as separate IT disciplines. However, in an agentic environment, protecting corporate information requires direct governance of the AI systems that process it. Industry research underscores this operational gap, with 79% of enterprise organizations reporting difficulties in securing the data assets powering their AI initiatives.

“For 20 years, security meant keeping sensitive data away from risk. AI flips that,” said Ryan Windham, CEO of Forcepoint. “The data you most want to protect is the data that makes AI worth using. I’d urge every agentic enterprise to stop locking AI down and start securing it where the risk actually lives, in the data itself. Other approaches will only tell you what’s at risk. Forcepoint’s protection travels with your data into AI and proves it can be trusted there.”

Unifying Classification and Protection Across Multi-Agent Ecosystems

The Forcepoint AI Data Security Platform resolves visibility and enforcement gaps by anchoring security policies directly to the data layer. Instead of using only passive monitoring techniques, it deploys context-based policy enforcement in real-time to both structured and unstructured data, regardless of whether the data source is hosted on-premises or in multi-cloud environments like Google Workspace, Databricks, and Snowflake.

The security functions of the platform tackle the full governance of AI data lifecycle across five pillars: discovery classification control, guardrails, and compliance:

Also Read: 1Password and Anthropic Bring Secure Credential Access to Claude 

Comprehensive AI Visibility & Governance: It scans automatically all active AI agents throughout the company, assigning each autonomous action to a direct human attribution, a computer agent, or a user-agent pair.

An AI Agent Gateway and a Least- Privilege Access: method that works as an interface between autonomous agents and enterprise SaaS systems. The least privilege and field-level DLP enforcement by the gateway means agents are forbidden from making sensitive write or delete changes directly through agents, while they need human-in-the-loop approval for such actions, and also agents must not keep directly application credentials with the system.

Prioritize Inline Control of Shadow AI: Beyond detecting unverified shadow AI, it also classifies shadow AI usage, while giving policy controls in line with admin to let, limit, or prevent the unapproved third-party AI tools as well as the personal tenant accounts.

Confidential Document and Prompt Protection: The feature that supports Microsoft Information Protection (MIP) tagging not only helps in isolating confidential documents from AI summaries but also ensures that data protected by regulations such as (PII/PC/PIs) should not leak via prompts or API integrations at all.

Natural Language Governance via ARIA: Features an embedded AI assistant, ARIA, that allows IT and security teams to build, audit, and enforce complex AI data protection policies using plain-English instructions.

“The hardest problem in enterprise security right now is ensuring that sensitive data stays protected once employees put it into AI, whether sanctioned or not,” said Roland Cloutier, Strategic Security Advisor and former Chief Security Officer at TikTok, ADP and EMC, among others. “Forcepoint’s approach is notably different, because it moves past visibility to enforcement that follows the data itself, which is what defensibility to auditors and boards actually requires. Knowing where your data goes is table stakes now.”

Verifiable Auditing for Boards and Regulatory Compliance

By extending its proven security architecture across web, email, network, and endpoints into AI interaction layers, Forcepoint equips enterprises and government agencies with a defensible security posture. The platform automatically generates centralized, board-ready executive reports through Forcepoint Insights. These immutable audit trails track risk trends, document blocked threats, and detail user-agent interactions, providing the empirical evidence required by corporate boards, external auditors, and regulatory bodies.

The Forcepoint AI Data Security Platform is available immediately for global deployment across cloud and on-premises environments. Chief Information Security Officers (CISOs), risk officers, and IT architecture teams can evaluate platform capabilities, access technical documentation, and request an operational demonstration by visiting Forcepoint’s official platform hub.

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However, this transition from passive chatbots to active digital agents has created a severe security paradox. According to Box’s 2026 State of Enterprise AI report, a striking 83% of organizations are actively experimenting with AI agents, yet 90% of IT leaders name security, regulatory compliance, and data trust as the primary barriers keeping those agents from accessing core enterprise content.

To bridge this trust gap and allow organizations to safely move agentic workflows out of sandboxed pilots and into live production, Intelligent Content Management pioneer Box, Inc. announced a suite of security and governance capabilities explicitly engineered to secure AI agents operating across enterprise content.

Sitting directly at the content layer where unstructured enterprise files reside, the new security suite extends Box’s enterprise-grade governance controls to both native Box Agents and third-party AI frameworks including Anthropic’s Claude, OpenAI’s ChatGPT, Microsoft Copilot, and Google’s Gemini connected via open protocols like the Model Context Protocol (MCP).

Also Read: Signeasy Expands Beyond eSignatures to Launch Intelligent Contract Management for Growing Businesses

Technical Architecture: Guardrails, Prompt Injection Shields, and Session Auditing

Instead of attempting to bolt security onto network perimeters or endpoint devices, Box’s framework enforces policy guardrails directly where corporate data lives. This prevents autonomous agents from oversharing confidential files, altering critical records, or executing unauthorized bulk deletions.

The updated security framework coordinates protection across several core functional layers:

Prompt Injection Detection: Automatically validates input prompts and document-embedded instructions before they reach foundation models. It scans for direct, indirect, and cross-agent injection threats, giving IT administrators the option to log, alert on, or outright block malicious attempts.

Deterministic Agent & MCP Guardrails: Allows administrators to establish strict, rule-based permissions governing what custom and third-party agents can execute. Controls include restricting external file sharing, enforcing human-in-the-loop approvals for sensitive deletion commands, and scoping folder creation limits.

Classification-Based Access Policies: Leverages metadata and classification labels (such as “Strictly Confidential” or “M&A Draft”) to automatically exclude specific content tiers from being scanned, read, or indexed by external AI agents.

Agent Activity Oversight & Audit Trails: Provides security operations teams with centralized visual dashboards and threshold-based alerts to monitor third-party agent behavior in real time, maintaining detailed session logs for regulatory compliance audits.

Transforming the Enterprise Content Management and Cybersecurity Industry

The delivery of a content-native security layer for multi-vendor AI agents creates structural waves across the broader Enterprise Content Management (ECM), Cloud Security, and Information Governance sectors.

The Obsolescence of Perimeter-Based Security Models
For decades, cybersecurity vendors protected corporate assets by securing network perimeters, endpoints, and identity access nodes. However, autonomous AI agents fundamentally disrupt this static model. Agents do not act like traditional software scripts; they interpret context, make real-time decisions, and can chain together actions across thousands of files in seconds.

Box’s product rollout highlights the inadequacy of traditional network firewalls in the agentic era. The broader cybersecurity market is being forced into a paradigm shift: security controls must move away from the outer edge and sit directly inside data platforms, managing agent intent at the content layer.

Normalizing Multi-Vendor AI Agent Interoperability
Historically, enterprise software providers attempted to build closed ecosystems, forcing customers to use native AI tools while restricting third-party access under the guise of security.

By introducing multi-vendor Model Context Protocol (MCP) guardrails, Box embraces an open, “bring-your-own-agent” ecosystem. This sets a strong precedent for the ECM industry: vendors will no longer be able to lock customers into proprietary AI models. Instead, content platforms will be judged by their ability to securely govern a heterogeneous mesh of external, autonomous agents.

Operational Impact on Businesses Operating in Regulated Sectors

For enterprises operating in heavily regulated, high-consequence industries such as Financial Services, Healthcare, Legal, and Insurance deploying un-governed AI agents presents severe legal, operational, and financial liabilities.

Getting Value from High-Value Automation without Fines for Compliance
For industries such as finance and healthcare, any slip-up by an AI agent in releasing details of mergers or patient data could lead to substantial fines and harm the organization’s reputation.

With classification-based permissions and automated prompts, risk management professionals can green-light these types of agentic workflows. Enterprises can leverage their AI agents to automatically handle sophisticated multi-document processes such as loan requests, legal document discovery, and insurance claims, knowing that governance mechanisms will stop any potential breaches or leaks.

Enabling Digital Transformation and Productivity
Enterprise IT and security departments sometimes find themselves being roadblocks to innovation because of governance challenges.

Offloading agent monitoring to an automated, centralized governance platform restores organizational velocity. Security teams gain full visibility and control over agent activities, while business units are liberated to deploy cutting-edge AI agents—automating routine document workflows, reducing administrative friction, and turning corporate unstructured content into an active driver of enterprise scalability.

The post Box Unveils Agent Security Controls to Secure AI Operating Across Enterprise Content appeared first on ITDigest.

Trust Infrastructure represents the next evolution of compliance operations, moving beyond user onboarding or fraud prevention tools toward an integrated system that continuously orchestrates verification, AML compliance, and risk assessment. Combining technologies, intelligence, and operational processes within a unified AI-powered layer, organizations can now replace fragmented solutions with an intelligent infrastructure that brings trust and transparency into every digital interaction.

After 11 years of helping businesses navigate increasingly complex regulatory environments and a proven track record of 272% ROI over 3 years, Sumsub identified a common challenge that digital businesses face: compliance operations have become fragmented across multiple vendors, disconnected data sources, and isolated workflows. The result is operational inefficiency, duplicated checks, inconsistent risk decisions, and growing costs for companies, with escalated workload when expanding to new jurisdictions.

Also Read: Reken Launches From Stealth, to Build an Internet Safe for Humans

Without an infrastructure in place, companies need to use between 5-9 separate tools to manage identity, fraud and compliance (User and Business Verification, AML Screening, Risk Scoring, Fraud Networks Detection etc). It may take up to 6 months to launch compliant operations in a new market, with new solutions, added headcount and increased complexity of operations required before the actual growth.

Unlike traditional one-point onboarding solutions, Sumsub’s AI-Powered Trust Infrastructure addresses these challenges by bringing identity data, AML screening, fraud signals, risk profiles, case management, AI-assisted tools and compliance reporting together. This allows firms to make fewer mistakes and build trust into every transaction while getting a comprehensive view of all the decisions and reports required by the regulators.

“Trust has changed significantly. Businesses can no longer verify customers only at the point of onboarding. Instead, they need to continuously understand risk throughout every interaction,” explains Peter Sever, co-founder and Chief Strategy Officer at Sumsub. “AI has enabled a new way of operating. The companies embracing this new reality grow faster, reduce fraud losses, avoid heavy compliance fines and operate far more efficiently than those stitching together disconnected point solutions. At Sumsub, we ensure that businesses move from fragmented processes to a unified trust infrastructure that supports long-term growth, reduces complexity, and improves decision-making.”

Source: PRNewswire

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