As enterprise data lakes evolve into mission-critical lakehouses powering AI models and real-time business intelligence, open table formats like Apache Iceberg have become essential infrastructure. However, a key vulnerability has persisted: native Iceberg snapshots are merely metadata pointers, not full backups. If a table is corrupted by ransomware, deleted by an errant script, or overwritten by a misfiring AI agent, those pointers vanish. Restoring underlying storage files leaves organizations with raw, disorganized Parquet files requiring days of manual metadata reconstruction before analytics engines can query the data.

Rubrik’s new capability closes this vulnerability by backing up both data files and table metadata, automatically re-registering tables in the catalog upon recovery. This allows data teams to restore complete, immediately queryable tables in engines like Amazon Athena, Apache Spark, or Trino.

Also Read: Airties Debuts “Aura” Agentic AI Engine to Transform Connected Intelligence for ISPs

Technical Capabilities: Metadata Rewiring and In-Account Sovereignty

Coverage extends across AWS Glue Data Catalog and Amazon Simple Storage Service (S3) Tables. Key technical highlights include:

Iceberg-Aware Metadata Recovery: Captures full table definitions, history, and schema state alongside raw data. Upon restore, Rubrik re-wires the catalog automatically so analytics engines can resume operations without manual file repair.

In-Account Data Sovereignty: it stands out as the first Iceberg-aware protection solution that enables enterprises to keep immutable backups locked inside their own AWS accounts, satisfying data compliance and sovereignty requirements, and complementing Rubrik’s air-gapped Cloud Vault.

Petabyte-Scale Forever-Incremental Backups: makes use of forever-incremental method of compacted snapshot backups which keeps backup window low for a very large scale deployment and lets the companies select their preferred Amazon S3 storage tier to control cost.

Risk Validation Based on branch: gives ability to run backups right before the risky activities like schema changes or large batch writes by having the ability to restore data on a separate Iceberg branch for validation before promotion production environment.

Transforming the Cloud Data Security, Lakehouse, and AI Infrastructure Industry

Rubrik’s release accelerates a fundamental evolution across the broader Cloud Data Security, Data Management, and AI Infrastructure sectors.

The Obsolescence of “Storage-Only” Cloud Backups
Historically, cloud backup providers treated object storage (like Amazon S3) as simple buckets of unstructured files. However, modern analytical workloads rely on abstraction layers catalogs, schema definitions, and metadata pointers to make raw data usable.

This announcement highlights the inadequacy of storage-only backups for lakehouses. The data security market is shifting toward metadata-aware cyber resilience. Security vendors can no longer claim cloud data protection simply by backing up raw bytes; they must protect the catalog and logical table structures that give that data operational meaning.

De-Risking Autonomous AI Agents on Data Lakes
As enterprises deploy autonomous AI agents with write access to internal data lakes for automated ETL, feature engineering, and reporting, the risk of “agentic data corruption” where an AI agent incorrectly modifies schema or overwrites table states at scale has grown significantly.

By introducing Iceberg-aware recovery and isolated branch testing, Rubrik establishes a baseline safety layer for AI-driven data pipelines. Data engineering teams gain a critical safety net, allowing them to grant AI agents broader operational permissions without risking permanent lakehouse corruption.

Operational Impact on Enterprise Businesses

For top enterprise executives responsible for data lakes, mission-critical analytics, and AI on AWS – like CIOs, CISOs, and CDOs – adopting lakehouse-aware protection has numerous operational benefits:

No more Downtime and Lost Revenue
If ransomware attacks or deletions cause company data lakehouses to stop functioning, dashboards for business intelligence, real-time supply chain simulations, and consumer apps will all become inoperable. Shortening the recovery period from many days of manual reconstruction work to just a few minutes of automated catalog re-registration ensures the smooth operation of business as well as the avoidance of possible losses in revenues.

Compliance made Easy by Securing Data Sovereignty
Organizations like financial services, healthcare providers, and public institutions are at a regulatory risk when their sensitive data is moved for cloud backup as they rely on third-party backup solutions that usually require leaving data in the enterprise clouds. Granting these institutions the ability to keep their immutable and air-gapped backups on the same AWS tenant that they use will ensure full mastery over where they keep their data and how to encrypt it, even while cyber insurance and other compliance requirements get satisfied.

The post Rubrik Launches Lakehouse Protection for Apache Iceberg Data on AWS to Close the Cloud Data Resilience Gap appeared first on ITDigest.

The platform launch addresses an emerging class of operational risk brought into focus by the recent OpenAI-Hugging Face incident. During an internal security evaluation, autonomous AI agents identified unexpected configuration paths that extended beyond their designated environments, ultimately accessing external production infrastructure. The event demonstrated how fast-moving AI agents can uncover operational gaps, chain vulnerabilities together, and execute cloud actions at speeds that surpass traditional, human-dependent security operations.

As Greg Brockman, President and Co-Founder of OpenAI, recently wrote in his blog post “The Defender’s Window”: “The OpenAI-Hugging Face incident was a watershed moment for cybersecurity because it gave a peek into how the capabilities of a typical threat actor will evolve in upcoming months. I’ve spoken with many organizations over the past few weeks, and one theme is clear: they know they need to fundamentally uplevel their cybersecurity practices with unprecedented speed.”

“The Hugging Face incident was a warning shot. It showed what capable agents can already do when they go off-script, and why security teams need to prepare for similar techniques being used intentionally by attackers,” said Evan Reiser, Founder & CEO of Abnormal AI. “Our platform already detects behavioral anomalies from human and non-human identities and I’m excited for us to extend these capabilities to protect enterprise cloud environments.”

Also Read: Harness Launches AppSec Alliance to Secure Application, API, and Agent Workflows

Machine-Speed Defense Architecture for Enterprise Cloud Infrastructure

Legacy cybersecurity tools that rely primarily on static threat signatures struggle to identify non-standard, agent-driven exploits that lack historical precedent. Abnormal AI Cloud Security counters these gaps by applying continuous behavioral analysis—establishing baseline operational profiles for every human identity, service account, API key, and autonomous AI agent across an organization’s digital footprint.

Key architectural capabilities of Abnormal AI Cloud Security include:

Real-Time AI Breach Detection: Continuously evaluates behavioral signals across the cloud estate, flagging suspicious activity the moment an identity or agent strays from established operational norms.

Autonomous Incident Containment: Delivers machine-speed response workflows when threat conditions are met. Security administrators can configure the platform to automatically isolate compromised workloads, revoke API tokens, and contain affected resources based on incident severity.

OpenAI Daybreak Investigation Engine: Utilizes OpenAI Daybreak models to assist security teams during incident triage. The system correlates raw telemetry logs with behavioral context to map out incident blast radiuses and suggest remediation paths.

Bridging the Operational Gap in Agentic Security

By extending behavioral analytics natively into cloud workloads, Abnormal AI gives enterprise security operations centers (SOCs) the toolset needed to govern both human and software-driven actors. The platform allows security teams to maintain high-velocity cloud workflows while deploying automated guardrails that prevent autonomous agents from breaching operational boundaries.

The post Abnormal AI Unveils AI Cloud Security Platform with OpenAI Daybreak Models appeared first on ITDigest.

Addressing this escalating crisis, connectivity cloud leader Cloudflare, Inc. announced a landmark integration with OpenAI to launch Vulnerability Discovery and Remediation. Available in early access through Cloudflare Managed Defense, the new service combines frontier AI models including OpenAI’s GPT-5.6 Cyber via the OpenAI Daybreak Defense Network with real-time traffic intelligence across Cloudflare’s global edge network.

The collaboration introduces a proactive defense layer that maps active production traffic directly to source code vulnerabilities, allowing enterprises to block live exploits at the edge in seconds while automated AI agents draft verified code patches.

Technical Performance: Linking Live Traffic Signals with Frontier AI Code Analysis

The core technological advance of the service is its ability to eliminate the disconnect between static source code scanning and dynamic network activity. By analyzing source code alongside real-time internet traffic patterns, the architecture enables defenders to prioritize and neutralize threats before exploit payloads reach core infrastructure.

Also Read: PwC and Palantir Expand Strategic Alliance to Accelerate Production-Grade Enterprise AI Deployment

Key capabilities of the integrated platform include:

Context-Aware Threat Triaging: Correlates live web traffic and attack patterns with source code structures, immediately highlighting vulnerabilities that are actively under fire.

Instant Edge Protection (Virtual Patching): Generates and deploys custom Web Application Firewall (WAF) mitigation rules tailored to specific attack vectors. This shields applications at the edge in seconds while developers work on permanent software fixes.

AI-Assisted Patch Generation: Leverages OpenAI’s GPT-5.6 Cyber model to investigate codebases and automatically draft context-aware code patches for developer review.

Human-in-the-Loop Governance: Enforces explicit human approval for both WAF rules and code modifications, ensuring security teams retain total control over production deployments.

“If your security team is manually fighting AI-driven attacks, you’re not just burning them out you’re losing,” stated Matthew Prince, co-founder and CEO of Cloudflare. “Pairing Cloudflare’s global network with OpenAI GPT-5.6 Cyber shifts defense from chasing patches one by one to stopping attacks before they land.”

Transforming the Enterprise Cybersecurity and Cloud Infrastructure Industry

Cloudflare and OpenAI’s partnership signals a defining structural pivot across the Cybersecurity, Application Security, and Cloud Edge Infrastructure market.

The Obsolescence of Standalone Vulnerability Scanners
For years, the application security market relied on static application security testing (SAST) and vulnerability scanners that produced overwhelming lists of theoretical flaws.

The launch of edge-based AI remediation marks the decline of passive scanning tools. The cybersecurity industry is transitioning toward unified threat intelligence platforms where network telemetry, edge computing, and AI code analysis operate in a continuous feedback loop. Security vendors will no longer be evaluated on how many bugs they surface, but on how fast their platforms apply virtual patches at the edge.

Establishing Frontier AI as a Core Defensive Engine
Historically, security teams worried that generative AI models would primarily benefit threat actors by accelerating zero-day discovery.

By deploying GPT-5.6 Cyber natively within the OpenAI Daybreak Defense Network, Cloudflare and OpenAI establish a benchmark for AI-driven cyber defense. Enterprise buyers will increasingly expect security infrastructure to feature specialized, defensive AI models capable of autonomous code reasoning and real-time mitigation drafting.

Broad Operational Impact on Enterprise Businesses

For corporate leadership, Chief Information Security Officers (CISOs), and Chief Technology Officers (CTOs) facing surging software vulnerability rates, deploying AI-powered edge defense offers direct strategic and commercial advantages:

Shielding Revenue and Preserving Developer Velocity
To fix a critical vulnerability, usually engineering teams are forced to abandon product feature development in favor of writing, testing, and deploying emergency hotfixes. Virtual patching at the edge can instantly block exploits and protects enterprise applications without having a negative impact on product development.

Reducing Breach Liabilities and Alert Fatigue
Closed vulnerable items in a company might result in significant monetary penalties, disruptions caused by ransomware, and also damage to a company’s reputation. Filtering threats using real-time network context will help SOC teams identify the real problems, cut out alert fatigue, and thereby reduce overall cyber risk exposure.

The post Cloudflare and OpenAI Partner to Redefine Vulnerability Management with AI-Powered Edge Defense appeared first on ITDigest.

That is the part of cloud security that often gets missed. The issue is not simply protecting a server or encrypting a database. It is controlling who can access what, understanding which responsibilities still sit with the enterprise, spotting weaknesses before attackers do, and keeping security controls intact as the environment changes.

This guide breaks down the practical cloud security best practices enterprises need across identity, data, threat detection, compliance, APIs, and containers.

The most effective cloud security best practices include enforcing identity management, encrypting data, continuous threat detection, and maintaining strict compliance.

The Foundation of Cloud Security Is the Shared Responsibility Model

One of the easiest mistakes to make after moving to the cloud is assuming that the provider now owns security.

It does not work that way.

AWS draws a pretty clear line between the whole security of the cloud and security in the cloud really matters. AWS is on the hook for securing the underlying infrastructure that powers their cloud services. Meanwhile the customer stays responsible for the services and all related configurations, also for the applications, the data, and the workloads that get placed into that environment.

That distinction becomes even more important when the type of cloud service changes.

With IaaS, the customer has a larger security responsibility because it controls more of the environment. Operating systems, applications, network settings, identities, and configurations can all sit within the customer’s area of control. With PaaS, the provider manages more of the underlying platform, but the customer still has to secure applications, data, identities, and configurations. SaaS moves more infrastructure responsibility to the provider, yet the customer still controls things such as user access, permissions, and data handling.

So the shared responsibility model is not just something to understand during a cloud migration. It should influence the security architecture from the start.

There is a practical reason for this. When responsibility is unclear, security gaps tend to fall between teams. The cloud provider assumes the customer is handling something. The customer assumes the provider is handling it. The control ends up belonging to nobody.

That is a bad place for a security control to live.

Identity Management and Access Controls Put Identity at the Center

The idea of a fixed security perimeter has become much harder to maintain. Employees work remotely. Applications talk to other applications. APIs connect services. Cloud workloads can move between environments. Automated processes can access sensitive resources without a person initiating every action.

Identity sits in the middle of all of this.

A useful way to think about modern cloud access is to stop asking whether someone is ‘inside’ the network and start asking whether that particular request should be trusted. Who is requesting access? What are they trying to reach? Why do they need it? How sensitive is the resource? Does the request look normal?

That is the thinking behind Zero Trust.

Multi-Factor Authentication is an important starting point because a stolen password should not automatically become a valid entry ticket. Microsoft’s current Azure identity guidance states that MFA can block more than 99.2% of account compromise attacks.

That is a strong reason to make MFA mandatory, particularly for privileged and sensitive accounts. But authentication is only one part of IAM.

Enterprises also need Role-Based Access Control and Conditional Access so permissions can reflect actual responsibilities and circumstances. A finance employee should not automatically have access to production databases simply because the account exists. A developer may need access to production for a specific task without needing unrestricted control over the entire environment.

Least privilege is where this becomes practical. Give an identity the access it needs. Nothing more.

That rule also applies to service accounts, API keys, workloads, and other non-human identities. These identities are easy to overlook because there is no employee sitting behind them. Yet they can still hold powerful permissions and connect directly to critical systems.

A mature IAM strategy therefore looks beyond employees. It keeps track of every identity that can reach the cloud environment and regularly asks whether that access still makes sense.

Data Protection Requires More Than Encryption

Encrypting cloud data is important. Treating encryption as the entire data protection strategy is not.

Before an enterprise decides how to protect data, it needs to understand what it actually has. Sensitive information can sit in databases, object storage, backups, application logs, development environments, and temporary workloads. It can also move between services without anyone thinking of that movement as a separate security event.

That is why data protection needs several layers.

Data at rest needs appropriate encryption. Data moving between systems needs protection in transit. Access to both needs to be controlled. Encryption keys need to be managed separately from the data they protect. Secrets such as passwords, tokens, and API keys also need secure storage rather than being left inside application code or ordinary configuration files.

AWS’s 2026 VPC Encryption Controls capability is a useful example of how this is becoming more operational. It can monitor encryption status across traffic flows, identify resources that unintentionally allow plaintext traffic, enforce encryption across supported network paths, and generate audit logs for compliance and reporting. AWS says supported traffic paths use hardware-based AES-256 encryption.

The important takeaway is not simply ‘use encryption.’ It is ‘keep checking whether the intended protection is actually in place.’

Cloud environments change too quickly for a security setting to be treated as permanent. A network rule changes. A new service is connected. A workload gets moved. A configuration is copied into another environment. The original security decision may no longer hold.

Backups need the same level of attention, honestly. The automated backups help a lot, but enterprises should also think about who gets access to them, how they are safeguarded, how frequently a restore is actually tested, and if they still be restorable when the primary environment is, say, completely out of action.

Data protection is therefore a process, not a switch.

Also Read: Challenges of Managing Hybrid Cloud Environments: How Enterprises Can Improve Security, Visibility and Performance 

Proactive Threat Detection and Response Changes the Security Game

Cloud infrastructure can change faster than a security team can manually inspect it.

A new workload can appear. A permission can be changed. An application can expose a service. A developer can deploy a new container. None of those actions necessarily wait for a security review.

That creates a problem for a purely reactive approach. If the team only starts looking after an alert arrives, the weakness may already have been sitting in the environment for some time.

This is where proactive cloud security becomes important.

Cloud Security Posture Management helps teams, basically spot misconfigurations and those security weak spots across different cloud environments, yeah. Cloud Workload Protection Platforms add a more grounded view into workloads plus runtime activity. Then there is agentless vulnerability scanning, which can also help uncover weaknesses without forcing an agent installed everywhere, so it’s kind of flexible.

The technology matters, but the bigger change is how security teams deal with the information they find.

AWS Continuum, launched in June 2026, is designed to discover, prioritize, validate, and remediate security risks at machine speed. It can take findings from existing tools, use environmental and business context to determine priority, validate exploitability, and apply mitigations within defined guardrails.

That matters because a long list of security findings does not automatically mean an organization understands its risk.

A critical vulnerability on an exposed production workload deserves a different response from a low-risk issue buried inside an isolated development environment. Without context, security teams can spend their time clearing alerts instead of reducing meaningful exposure.

Good cloud security best practices therefore need a clear path from detection to action. Find the weakness. Understand its context. Work out whether it can actually be exploited. Then decide what needs to happen.

Security teams do not need more noise.

They need better decisions.

Cloud Compliance Needs Continuous Governance

Compliance is often treated as an event. The audit happens, evidence gets collected, gaps are documented, and everyone moves on.

Cloud environments do not behave like that.

Configurations change every day. New resources are created. Permissions are updated. Applications are deployed. Infrastructure is removed and rebuilt. A control that was working when an audit took place may not be working months later.

That is where configuration drift becomes a real problem.

Enterprises need automated checks that continuously compare the environment against defined security and compliance requirements. The exact requirements will vary. A healthcare organization may have to address HIPAA. An organization handling personal data may have GDPR obligations. Others may work against SOC 2 requirements or additional industry controls.

Microsoft’s Cloud Security Benchmark v2 shows how quickly the control landscape itself is expanding. Microsoft says its policy-based control measurements have grown from more than 220 to more than 420. The preview also adds a dedicated AI Security control domain with seven recommendations.

The number is useful, but the bigger message is what sits behind it. Cloud environments are becoming more complicated, and governance has to keep up.

That means compliance should connect directly to technical controls. Policies should be measurable. Changes should be visible. Exceptions should have owners. And when a configuration moves away from the expected state, the organization should know about it without waiting for the next audit.

The goal is not to make the cloud look compliant on audit day.

The goal is to keep it controlled on every other day too.

Modern Cloud Security Must Reach APIs and Containers

Cloud-native development has made applications more flexible, but it has also multiplied the places where security problems can appear.

Microservices communicate through APIs. Containers can be created and replaced rapidly. Kubernetes can manage large numbers of workloads across constantly changing environments. A vulnerable dependency, exposed API, leaked secret, or poorly configured container can therefore become a problem before the application reaches production.

Security cannot be bolted onto that process at the end.

This is where DevSecOps becomes useful. Security checks should become part of the CI/CD pipeline. Code can be checked for weaknesses. Dependencies can be reviewed. Container images can be scanned. Infrastructure configurations can be tested. Secrets can be identified before deployment.

The advantage is simple. Developers can fix problems while they are still close to the code and configuration that created them.

That does not mean runtime protection becomes unnecessary. It means runtime security no longer has to carry the entire burden.

The strongest approach combines both. Prevent what can be prevented during development, then monitor what reaches the cloud and respond when something changes.

Conclusion and Next Steps

The hardest part of cloud security is not buying another security tool. It is keeping the basics under control when the environment refuses to stay still.

Identities change. Workloads move. Permissions grow. APIs multiply. Configurations drift. New services appear. That makes cloud security an ongoing management problem rather than a one-time implementation project.

Enterprises should therefore assess security as one connected system. Review identities and permissions. Check encryption and secrets. Test backups. Monitor workloads. Look for configuration drift. Secure APIs and containers. Keep compliance controls tied to the actual environment.

A comprehensive cloud security posture assessment is a sensible place to start. A practical checklist or assessment framework can expose the gaps that individual tools often miss.

The real test is not whether the cloud was secure when it was first deployed.

It is whether the security still holds six months later.

The post Cloud Security Best Practices: How Enterprises Can Protect Data, Applications and Cloud Infrastructure appeared first on ITDigest.

In order to address these visibility issues and help companies secure their cloud transformations, digital engineering giant EPAM Systems entered into a strategic partnership with cloud security specialist Wiz.

The combination of EPAM’s global capabilities in digital engineering, cloud migrations, and managed security, with Wiz’s Cloud-Native Application Protection Platform (CNAPP) solution allows for achieving full-stack visibility, reducing cloud risk, and enhancing cyber resilience in complex cloud environments.

The News: Unifying Cloud Security Posture with Scalable Engineering

The core value proposition behind the EPAM and Wiz alliance is bridging the gap between security visibility and engineering execution. Identifying a security flaw inside a multi-cloud network is only half the battle; enterprise security teams frequently struggle to remediate risks without interrupting live production software or developer workflows.

Also Read: FS Launches 800G Muxponder to Power Next-Gen AI and Cloud Networks

The joint solution addresses these operational challenges through several key capabilities:

Full-Stack Contextual Visibility: it makes use of Wiz’s agentless CNAPP platform to construct a single, queryable risk graph that spans an organization’s complete cloud presence including virtual machines, Kubernetes clusters, serverless functions, identity permissions, and sensitive data stores.

Prioritized Risk Mitigation: it assesses potential attack vectors based on how a mix of misconfigurations, exposed secrets and unpatched vulnerabilities come together, leading to prioritization of truly dangerous threats instead of background noise.

Automatic Remediation Pipelines: With Wiz’s real-time threat detection coupled with the custom IaC coding work of EPAM, one can automate policy enforcement as well as vulnerability patching quickly and safely within CI/CD pipelines of developers.

Continuous Compliance & Governance: by setting up automated compliance monitoring through the major regulatory standards like SOC 2, ISO 27001, HIPAA and GDPR public and hybrid cloud environments will always remain audit-ready.

Transforming the IT Consulting, Cloud Engineering, and Cybersecurity Industry

The collaboration between EPAM and Wiz reflects fundamental operational shifts across the IT Services, Cloud Engineering, and Managed Security ecosystem.

The Shift from “Point-Solution Scanning” to End-to-End CNAPP Platforms
For years, IT service providers and enterprises relied on fragmented point tools—using separate products for container security, cloud security posture management (CSPM), identity analysis, and vulnerability scanning. Managing dozens of isolated scanners generated overwhelming alert fatigue while failing to show how different risks combined to create exploitable attack paths.

EPAM’s integration of Wiz highlights the obsolescence of isolated point tools. The cybersecurity and cloud consulting industry is pivoting rapidly toward unified CNAPP architectures. IT service partners are no longer evaluated solely on their ability to deploy monitoring software; they must deliver integrated security platforms that correlate context across code, infrastructure, identity, and data.

Bridging the Gap Between Security Teams and Software Developers
Historically, cybersecurity departments and software engineering teams operated in friction-heavy silos. Security teams generated static audit reports, while developers viewed security checks as speed bumps that delayed release cycles.

By embedding Wiz’s contextual risk intelligence directly into EPAM’s software engineering workflows, the partnership enforces DevSecOps integration as an enterprise standard. Systems integrators and IT consulting firms must now deliver “security-by-design” frameworks ensuring cloud security controls are built natively into code repositories and deployment pipelines rather than applied as an afterthought.

Broad Operational Impact on Enterprise Businesses

For enterprise organizations navigating digital migrations while attempting to defend expanding attack surfaces, adopting an integrated cloud security model provides clear strategic and financial advantages:

Insulating Cloud Operating Margins Against Cyber Disruptions
Ransomware threats, cloud misconfiguration, and data breaches come at a significant cost in terms of fines, legal fees, and branding impact. The ability to consolidate cloud security posture helps organizations identify and eliminate vulnerabilities before they become exploitable by attackers, safeguarding their resources and maintaining their profit margins.

Reclaiming Capacity for Digital Innovation
With software developers spending valuable time investigating false positives and manually assessing security alerts, the process of digitization is slowed. The process of prioritizing risks and automatically remediating them in the workflow of developers frees up much-needed internal capacity. This enables the development teams to concentrate on delivering features that generate revenues.

The post EPAM Partners with Wiz to Accelerate Enterprise Cloud Risk Reduction and Cyber Resilience appeared first on ITDigest.

Also Read: Tech Mahindra and Cisco Partner to Deliver AI-Driven Security Service Edge Solutions 

Contextualizing the strategic importance of building technological autonomy directly into the underlying cloud architecture, Christian Müller, CEO of Schwarz Digits, explains: “Technological independence begins with infrastructure. Through this partnership, we are taking network security to a new level: we are evolving existing security architectures and focusing on high-performance identity verification. For our customers, this means even stronger protection against cyberattacks.” Reinforcing the vendor’s long-term enterprise commitment to data sovereignty across the region, Misha Kuperman, Chief Reliability Officer at Zscaler, added: “In an era of AI-driven cyber threats, a modern Zero Trust SASE solution is the only effective way to protect sensitive enterprise data. Our partnership with Schwarz Digits is a concrete demonstration of our long-term commitment to Europe: Together, we help our customers eliminate their attack surface and prevent the lateral movement of threats, while still maintaining compliance with European regulations through EU data residency. This enables the highest level of security while aligning with European digital sovereignty regulations.”

Read More: Zscaler and Schwarz Digits Partner to Deliver Sovereign Cloud Security Platform for Europe

The post Zscaler Partners with Schwarz Digits to Deliver Sovereign Cloud Security for Europe appeared first on ITDigest.

Also Read: Orca Security Expands Cloud Security Reach with TD SYNNEX Partnership 

Underscoring the need to secure modern distributed architectures and burgeoning agentic systems, Raj Chopra, SVP & Chief Product Officer, Cisco Security Business Group, added: “Enterprises don’t need another tool to stitch into an already complex security stack. They need a simpler way to secure how work actually happens across users, devices, applications, clouds, and increasingly AI agents. Cisco Secure Access brings zero trust, identity context, and AI-powered protection into one cloud-delivered platform, helping teams enforce policy consistently while giving users seamless access from anywhere. Together with Tech Mahindra’s global managed services and integration expertise, we can help organizations modernize security operations, accelerate secure cloud and AI adoption, and move with confidence in the agentic era.” The strategic partnership expands Tech Mahindra’s managed security footprint while empowering organizations to secure complex data flows across multi-cloud environments reliably.

Read More: Tech Mahindra and Cisco Partner to Bring AI-Driven Security Service Edge to Global Enterprises

The post Tech Mahindra and Cisco Partner to Deliver AI-Driven Security Service Edge Solutions appeared first on ITDigest.

This release tackles a trend in how all software is written; albeit AI has Quite a bit shortened time-to-write and find issues, remediation cycles have not kept pace. Outdated security tools overwhelm engineering teams with business-irrelevant, false-positive results, creating risk for the organization due to existing un-patched exploits. 75% of security practitioners said shadow AI will eclipse shadow IT as a business risk, but few companies have processes in place to inventory or control AI agents.

“While the attack surface has expanded, the real crisis is no longer just discovering vulnerabilities. It is the speed at which organizations can respond to them. Modern AI systems can surface vulnerabilities far faster than security teams can triage, validate, and patch. This creates an operational bottleneck where organizations are flooded with findings but lack the capacity to remediate them, leaving them exposed despite having ‘visibility.'”

Also Read: Orca Security Expands AI Platform with Claude Compliance Integration

Unifying Application, API, and Agent Security on One Platform

Built on top of the Harness AI Software Delivery Platform, the AppSec Alliance provides system integrators, VARs, and MSPs with a complete security stack. With the help of this offering, partners can integrate, deliver, and manage the following functionalities, thus improving risk management across four key technical areas:

Shift-Left SAST & SCA: Provides AI-enabled SAST and SCA capabilities straight in CI/CD pipelines. Using reachability analysis and deduplication, this solution helps users to eliminate false positives and to prioritize high-risk and reachable risks.

Supply Chain Protection for Software & AI: Automatically creates Software Bills of Material (SBOMs) and AI Bills of Materials (AIBOMs). At the same time, it performs artifact provenance and attestation, which is needed to detect zero-day vulnerabilities fast.

Web Application & API Protection (WAAP): Integrates WAF, API discovery, bot protection, and runtime attack prevention capabilities in the native API control plane, which is tailored for the needs of microservices architecture.

AI & Autonomous Agent Security: Offers visibility into all active AI endpoints and agents, performs adversarial model testing, implements runtime firewall policies and collects session telemetry using Harness AgentTrace.

“The Harness AppSec Alliance closes that gap. We’re bringing partners application security technology, built into one platform, so they can walk into an existing account and pick up spend they were never able to compete for before.”

Solving Core Channel and Customer Bottlenecks

By integrating runtime protection, vulnerability prioritization, and automated patching into a single architecture, the AppSec Alliance resolves common operational gaps that legacy security stacks miss:

Closing Runtime Gaps: Traditional web application firewalls often miss multi-step, API-targeted agentic attack chains that unfold over time.

Eliminating Signal Overload: AI-driven triage filters out noise, replacing unmanageable alert volumes with actionable remediation paths.

Accelerating Zero-Day Audits: Replaces manual, multi-month open-source exposure audits with instantaneous cross-estate dependency mapping.

Compressing Patch Windows: Connects vulnerability identification directly to automated developer workflows, ensuring fixes are deployed before exploit windows open.

The Harness AppSec Alliance program is available immediately to qualified enterprise partners worldwide. Managed service providers, technology resellers, and security integrators can evaluate platform documentation, join the ecosystem, or request an operational demonstration by visiting the official Harness partner portal.

The post Harness Launches AppSec Alliance to Secure Application, API, and Agent Workflows appeared first on ITDigest.

This is indicative of a change in approach by companies towards the development of products, where humans working as engineers, enterprise employees, and autonomous AI systems work together within a common software space. Based on the results published in the State of AI Security Report of 2026 by Orca Security, 56% of companies surveyed have deployed their AI agents in the live production environment.

“The future of software development isn’t just human. It’s human and AI working together,” said Gil Geron, CEO and co-founder of Orca Security. “As AI becomes another builder inside the enterprise, organizations need security that understands the complete picture, not isolated tools for every new technology. Orca gives security teams the context to understand risk across cloud, applications and AI in a single platform, helping organizations securely embrace the next generation of builders.”

Also Read: JumpCloud Launches Agentic IAM on Google Cloud to Govern Autonomous AI Identities

Contextual Risk Correlation Across Enterprise Cloud Workloads

Unlike standalone security tools that monitor AI tools in isolation, Orca Security correlates Claude Enterprise activity directly with broader cloud native environments. Powered by Orca’s patented agentless platform, the integration combines real-time AI telemetry with identity, application, and runtime context. This unified approach enables security administrators to isolate high-priority exposures and accelerate incident remediation.

The integration provides enterprise IT and security leads with granular operational capabilities:

Comprehensive Claude Enterprise Inventory: Automatically catalogs users, groups, roles, permissions, projects, and security configurations across connected Claude environments.

Context-Aware Risk Identification: Flags potential security vulnerabilities and compliance misconfigurations, including excessive user privileges, unmanaged shadow identities outside SSO/SCIM frameworks, and insecure retention settings.

Unified Governance Layer: Merges AI audit logging directly into existing cloud security workflows, eliminating the need to maintain separate security platforms.

Frictionless Setup and Data Privacy Architecture

Designed for rapid deployment, the integration operates via read-only Compliance API keys within Anthropic-hosted environments. The connection requires minimal configuration and maintains strict privacy boundaries, ingesting identity settings and workspace metadata without inspecting or storing sensitive chat messages or uploaded corporate files.

The Claude Compliance API integration is available immediately for organizations utilizing both Orca Security and Claude Enterprise. Chief information security officers (CISOs), cloud security engineers, and compliance leaders can review integration blueprints, read technical documentation, and request an enterprise demonstration by visiting Orca Security’s official platform.

The post Orca Security Expands AI Platform with Claude Compliance Integration appeared first on ITDigest.

Instead, modern businesses run on an intricate, highly volatile network of remote human workers, third-party contractors, external suppliers, and autonomous software agents. According to recent global cybersecurity data, traditional perimeter-based networks fail to intercept over 75% of advanced cloud attacks, leaving corporate data assets heavily exposed to zero-day exploitation and lateral threat movement.

To eliminate this structural network friction and shield complex multi-cloud perimeters, AI-native engineering services leader Coforge announced the launch of SecureEdge2Cloud.

Built natively on the Zscaler Zero Trust Exchange platform, this next-generation, AI-powered Zero Trust security offering accelerates secure digital transformation across distributed business landscapes. For the Cybersecurity, Enterprise Cloud Solutions, and Systems Integration industry, this launch defines a new market standard—shifting network defense away from static, box-checking gating and moving it into continuous, machine-speed adaptivity.

Technical Performance: 7-Layer Zero Trust Intersected with Agentic Operations

The core architectural milestone inside the SecureEdge2Cloud framework is its transition beyond basic access control toward a completely self-optimizing security fabric. Rather than forcing corporate IT departments to manage separate security point solutions across different clouds, the platform utilizes an AI-first approach mapped across the full OSI 7 layers framework.

Also Read: IBM and Red Hat Launch Lightwell to Neutralize Open-Source AI Threat Vector

The integrated solution automates multi-vector enterprise safeguards across three primary functional areas:

Autonomous AI Application Safeguards: Following Coforge’s onboarding into the next phase of Zscaler’s Project AI-Guardian ecosystem initiative, the platform features native capabilities to secure generative AI applications, large language models, and autonomous AI agents against prompt injection and data exfiltration.

Continuous Drift Remediation via EvolveOps.AI: The architecture embeds Coforge’s proprietary EvolveOps.AI agentic operations layer. This always-on engine continuously monitors network configurations, detects security policy drift, and reconfigures enterprise control planes automatically before anomalies turn into active security breaches.

Unified Workspace Segments: The platform consolidates visibility across all managed and unmanaged (BYOD) endpoints, third-party partner portals, and private software applications—replacing brittle legacy VPN infrastructure with cryptographically secure, identity-centric Zero Trust Network Access (ZTNA).

Transforming the Cybersecurity and Systems Integration Industry

The delivery of an AI-engineered, outcome-committed Zero Trust framework by a major global systems integrator signals permanent structural transformations across the IT services landscape.

The Obsolescence of Fragmented Security Point Solutions
For the past decade, the enterprise security sector was heavily fragmented. System integrators routinely generated high billable hours by manually stitching together separate data loss prevention (DLP) tools, independent endpoint tracking agents, and disconnected cloud access security brokers (CASBs).

The SecureEdge2Cloud launch highlights the operational failure of this disjointed model. When threats move at machine speed, fragmented databases delay vital incident response times. The enterprise market is entering a rapid platform consolidation era, where systems integrators will be judged entirely by their ability to deliver unified, edge-to-cloud control panes that simplify security architectures out of the box.

Shifting Consultative Focus from Installation to Continuous Optimization
Historically, third-party IT service vendors operated as simple technical deployment plumbers charging static fees to install a vendor platform, verify basic connectivity checklist items, and hand off maintenance to internal teams.

Coforge‘s achievement of Zscaler Delivery Services Authorization (DSA) status, combined with pre-built Z-Deploy accelerators, heavily commoditizes the basic mechanics of setup. This shifts the long-term tech consulting baseline away from routine installation. System integration vendors will increasingly be forced to operate via highly agile “Mod Squad” cross-skilled pods focused entirely on continuous AI-driven optimization, advanced threat modeling, and board-ready risk reporting metrics.

Broad Operational Impact on Enterprise Businesses

For large-scale organizations operating across highly regulated verticals including Banking and Financial Services, Insurance, Healthcare, and Life Sciences deploying an adaptive Zero Trust fabric introduces distinct commercial advantages.

Insulating Corporate Treasuries from Catastrophic Data Breaches
Maintaining legacy, network-centric perimeters that grant broad access once an initial security gate is cleared leaves an enterprise wide open to devastating financial impacts. If an attacker compromises a third-party contractor’s credentials, they can freely traverse internal databases to exfiltrate proprietary source code, healthcare data, or customer financial records.

An identity-centric Zero Trust fabric ensures that every user, device, and software workflow is continuously verified and restricted strictly to authorized tasks. This shields corporate balance sheets from expensive regulatory fines, protects brand reputation, and minimizes unexpected business downtime.

Unlocking Commercial Agility and Secure AI Innovation
A major operational bottleneck for corporate innovation teams is the length of time it takes to vet and approve modern digital tools. Because security compliance officers frequently stall generative AI and cloud expansion projects due to data leak fears, businesses lose market velocity.

Deploying an integrated defense framework that wraps strict, automated security boundaries around shifting digital applications gives corporate boards the clarity needed to confidently scale operations. Enterprises can safely leverage cutting-edge AI automation, onboard global partner networks in minutes, and turn advanced data infrastructure into a powerful driver of long-term corporate growth.

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