Oracle on Tuesday described new EBCDIC character set compatibility features in Oracle AI Database for customers transitioning from legacy databases on IBM mainframes.

In its post, Oracle noted that EBCDIC compatibility has historically been one of the top technical challenges for enterprises re-platforming to use newer, ASCII-based databases while continuing to use proven legacy applications.  

“Achieving this goal requires more than simply moving data. It requires preserving the EBCDIC compatibility on which existing applications depend,” wrote Michael Yau, VP for Oracle Database Globalization Engineering. The feature rollout “addresses two fundamental challenges of preserving EBCDIC compatibility: accurate character encoding conversion and preservation of EBCDIC binary ordering.”

He added: “These client character sets implement IBM Character Data Representation Architecture (CDRA) code page definitions, providing source-to-target character mappings that are compatible with IBM’s published standards. This enables accurate and predictable character encoding conversion during data migration and subsequent database client/server communication.”

Yao observed that this is important because these mainframe migrations can be very complex.

“Many legacy EBCDIC applications, such as those written in COBOL, implicitly rely on the EBCDIC binary ordering defined by IBM EBCDIC code pages. SQL predicates that compare character values, perform range searches, or sort query results often assume this ordering,” he wrote. “After migration to an ASCII-based Oracle AI Database character set, these same SQL statements can produce different results, not because the data changed, but because the database’s default binary ordering follows that of the ASCII-based database character set rather than the source EBCDIC code page.” 

Compatibility repair, not modernization

While consultants generally applauded the new features, some questioned whether this will simply shift enterprise dependency on IBM to dependency on Oracle. 

Sanchit Vir Gogia, chief analyst at Greyhound Research, is one of the fans.

“Oracle has repaired one of the oldest silent faults in mainframe migration: EBCDIC ordering, the muscle memory of the legacy estate. Preserve the data and lose the ordering, and a query returns the wrong record while every dashboard stays green,” he said. “The application runs and the query completes. The answer is simply wrong.”

Gogia noted that the new feature is “not a modernization suite. It is a compatibility repair, narrow and genuinely useful, which CIOs who have bled on past migrations will read with equal parts relief and suspicion.”

AJ Thompson, CCO at UK IT consulting firm Northdoor, agreed that the Oracle announcement addresses a genuine technical barrier rather than just being a marketing gimmick, so it is worth taking seriously as a re-platforming enabler. “The two problems it solves, EBCDIC to ASCII character conversion and preserving EBCDIC binary sort ordering, have historically been real blockers for allowing COBOL to move away,” he said.

But, he cautioned, CIOs must also take resiliency challenges seriously. “Mainframes are not chosen primarily for character encoding, they are chosen for benefits like decades of proven uptime, IBM Z’s redundancy architecture, and workload isolation,” he pointed out.

“Oracle’s announcement solves a data compatibility problem, not a resilience or availability one. A client with genuinely mission-critical, zero-downtime workloads will still need convincing on the availability and disaster recovery side before moving [to the cloud], and Oracle’s own resilience claims would need scrutiny on their own merits, quite separate from this EBCDIC work.”

Leverages IT desperation

Mike Wilkes, enterprise CISO at Aikido Security, added that Oracle is leveraging IT desperation to squeeze long-term value from legacy systems. 

“I have always believed that Oracle will own the very last white-knuckle-grip workloads that migrate from on-premises data centers into the cloud,” he said. “This announcement certainly demonstrates that they understand their position in the world of cloud service providers. They are not the biggest, they are not the oldest, and they are not the most technically advanced. But they do own the market share for the trailing edge of cloud adoption.”

He pointed out that the greatest barrier to cloud migration is not containerizing modern applications, it is the decades of business logic buried inside COBOL applications and EBCDIC-encoded data.

“Oracle’s EBCDIC compatibility features acknowledge a practical reality: organizations are not rewriting these systems from scratch,” he said. “If Oracle can reduce the cost, risk, and operational disruption associated with moving those workloads, the announcement represents meaningful value for IT teams that have been delaying modernization because the migration path was simply too complex or too expensive.”

Could cause vendor lock-in

Then again, Wilkes noted, there is the potential for increasing vendor lock-in.

“Compatibility layers almost always increase long-term dependence on the platform providing them,” he said. “Rather than eliminating legacy technology, they abstract it behind Oracle’s database and cloud ecosystem, making future migrations potentially more difficult. Enterprises should view these tools as transition accelerators rather than permanent architecture.”

Thus, he said, if they use the opportunity to gradually modernize applications and data models, there is substantial value, but if they simply relocate technical debt into Oracle Cloud, they may find that they have exchanged one form of legacy lock-in for another.

Easier, but not easy

Matt Kimball, VP and principal analyst with Moor Insights & Strategy, also saw the Oracle move as a good one, but stressed that while it should make things easier for organizations, re-platforming still won’t be easy because EBCDIC migration isn’t just a character-conversion exercise. However, “Oracle’s built-in character-set support and EBCDIC collations move part of that compatibility burden into the database, making it easier to preserve existing application behavior,” he said.

Ishraq Khan, CEO of coding productivity tool vendor Kodezi, agreed. 

“One of the biggest obstacles to leaving mainframes is decades of applications built around EBCDIC encoding and legacy data formats. If these compatibility features reduce the amount of code that needs to be rewritten, they can lower migration risk, cost, and implementation time,” Khan said. But, he added, organizations should also consider whether these features simply make migration easier, or make future moves more difficult.

This article originally appeared on CIO.com.

Oracle’s July 2026 Critical Patch Update, its largest ever, contains 1,449 new security patches spanning 32 product families, from Oracle Database and E-Business Suite to PeopleSoft, GoldenGate, Java SE, and Fusion Middleware.

Fusion Middleware was particularly hard hit, with new security patches for 355 security vulnerabilities, 219 of them remotely exploitable without authentication, meaning they can be exploited over a network without requiring user credentials. Ten of them scored a “perfect” 10.0 on the Common Vulnerability Scoring System (CVSS).

These included easily exploitable vulnerabilities allowing unauthenticated attackers with network access via HTTP to compromise Oracle Data Integrator, Oracle Access Manager, Oracle HTTP Server, Oracle Platform Security for Java, Oracle WebCenter Content, Service Delivery Platform, or Oracle Weblogic Server Proxy Plug-in,

No other products were found to have quite such extreme vulnerabilities, but there were plenty of others scoring almost as badly.

Two critical flaws in Oracle Database Server

The most severe flaw Oracle patched in its flagship database product is CVE-2026-61211, a vulnerability in the RDBMS component’s DBMS_CLOUD package with a CVSS score of 9.9.

This easily exploitable vulnerability allows a low-privileged attacker having Execute DBMS_CLOUD privilege with network access via Oracle Net to compromise the RDBMS, Oracle said in the patch update statement. “While the vulnerability is in RDBMS, attacks may significantly impact additional products (scope change). Successful attacks of this vulnerability can result in takeover of RDBMS,” it warned.

The flaw affects Database Server versions 19.3 through 19.31 and 23.4.0 through 23.26.2.

Sanchit Vir Gogia, chief analyst at Greyhound Research, said the 9.9 score should be read as serious but conditional. Exposure depends on configuration, he said: On customer-managed databases, DBMS_CLOUD is absent until installed, and grants and network access lists determine the radius from there. “Where DBMS_CLOUD is broadly granted and reachable, the emergency is real and the window is seventy-two hours; where it is absent, the accelerated database wave will do.”

Vibhum Dubey, a cybersecurity researcher and red teamer, said the flaw stood out to him because it checks several boxes that concern defenders.

“Database servers often hold an organization’s most valuable data, so even if exploitation is not publicly observed yet, I don’t think this is the kind of issue you leave until the next routine maintenance window if your environment is exposed,” Dubey said.

A second Database Server flaw, CVE-2026-47040, affects Connection Manager in Oracle Net Services, is remotely exploitable without credentials. Oracle’s risk matrix lists six Database Product vulnerabilities in this cycle as reachable over a network with no authentication required, the statement added.

CVE-2026-7383, an OpenSSL-related TLS vulnerability, affects two products, Database Server and Autonomous Health Framework, since both bundle the same third-party component. Oracle’s advisory notes the Database Server patch for that CVE also resolves 19 related OpenSSL CVEs bundled into the same fix.

Oracle GoldenGate received 27 new patches, nine of which do not require authentication to exploit, including CVE-2026-2332, a flaw in the Big Data and Application Adapters component tied to Eclipse Jetty, the statement added.

There were also two critical flaws in Oracle’s TimesTen in-memory database.

The remainder of the release spans E-Business Suite, WebLogic Server, PeopleSoft, Siebel, JD Edwards, Communications, Retail Applications, Utilities Applications, MySQL, Solaris and VM VirtualBox.

Volume repair

Gogia said the volume itself marks a shift.

“At 1,449 patches, against 481 in April 2026 and 309 a year earlier, patch load has outgrown the queue built to hold it,” he said. He recommended a tiered response: “The reachable and the reported inside seventy-two hours, the trusted core inside ten days, the rest by risk before the October release.”

He also flagged a specific risk in how organizations might triage E-Business Suite. “Oracle’s advisory concedes that E-Business Suite exposure sits partly in underlying Database and Fusion Middleware versions outside the E-Business Suite matrix. The fastest way to mis-prioritise this release is to patch by product logo instead of trust boundary.”

Third Tuesday, quarterly cycle

The July release is the third quarterly Critical Patch Update of 2026, and the first since the introduction in May of the monthly Critical Security Patch Update program.

Gogia said Oracle has effectively layered a second cadence on top of the existing one rather than replacing it.

“Quarterly Critical Patch Updates remain and stay cumulative; monthly Critical Security Patch Updates now sit on top,” he said, adding that enterprise adoption of the new rhythm remains low because of “certification obligations, regression exposure and scarce specialist hours.”

Dubey made a similar point about organizational readiness: “In large enterprises, patching is rarely a technical problem. It is an operational one. Database administrators, application owners, infrastructure teams, business stakeholders, and change advisory boards all have to align.”

Niyati Daftary, principal analyst at Gartner, said the release underscores a broader shift in how patching is approached.

“Patching is no longer a race to remediate every vulnerability. It is a discipline of identifying the exposures that matter most and reducing business risk as efficiently as possible,” she said, adding that organizations should prioritize based on exposure, business impact and exploitability, starting with internet-facing assets and mission-critical systems.

Daftary pointed to continuous threat exposure management and adversarial exposure validation as increasingly relevant frameworks, since CVSS scores “measure theoretical severity rather than actual enterprise risk.” Patching alone will not be sufficient, Daftary said, and organizations should continue investing in defense in depth, including behavioral threat detection and incident response.

Oracle’s next cumulative Critical Patch Update will come on Oct. 20, 2026, with smaller Critical Security Patch Updates on Aug. 18 and Sept. 15.

This article first appeared on CSO.

Separating transactional databases from analytical systems was, until recently, considered good architecture. Now, as enterprises adopt AI agents that continuously read, reason over, and act on business data, data warehouse and database vendors are increasingly deciding that separation has become a liability.

Just weeks after Databricks unveiled its Lakehouse Transaction and Analytical Processing (LTAP) offering based on Neon Postgres to bring operational (OLTP) and analytical (OLAP) processing closer together, EnterpriseDB (EDB) has introduced converged analytics capabilities for its managed EDB Postgres AI database service with the same intent.

Both vendors are responding to the same pressure of enabling AI agents for enterprises to operate on fresh operational data without waiting for pipelines and replicas, but EDB argues its approach starts from a fundamentally different place.

“Databricks is building from the lakehouse outward, trying to pull transactional capability in through Lakebase,” said Max Romanenko, chief engineering officer at EDB, while “we’re building from the operational layer with Postgres, which is where enterprises already run their most critical workloads, and expanding from there.”

In contrast to Databricks’ lakehouse-centric LTAP, EDB keeps Postgres as the operational source of truth and uses Apache Iceberg as a shared catalog layer connecting Postgres with ClickHouse, WarehousePG, and Spark compute engines, Romanenko said.

In this way, operational data remains in Postgres while historical and tiered data is stored in Iceberg-managed object storage, allowing analytical engines to query the same data through a common catalog without requiring separate copies or ETL pipelines, he said.

That architectural distinction matters to EDB, according to Romanenko, because the vendor is targeting enterprises that want AI and analytics capabilities without moving sensitive data into a cloud-managed platform: “For us, it’s always been about the data sitting on infrastructure the customer owns and controls.”

Focus on data sovereignty and predictable economics

EDB’s promotion of control “will resonate with CIOs focusing on sovereignty, regulated data, and hybrid deployment,” said Stephanie Walter, practice leader of AI stack at HyperFrame Research. “This should enable them to run AI and analytics closer to the data, on infrastructure that their enterprise controls, without creating yet another proprietary data estate.”

For Ashish Chaturvedi, leader of executive research at HFS Research, EDB’s approach in converged analytics will offer more predictable costs than Databricks LTAP for CIOs already struggling to manage their analytics and AI budgets.

EDB’s per-core pricing model can make costs easier to forecast than consumption-based cloud data platforms, where query volumes, AI workloads, and data processing demands can cause bills to fluctuate, Chaturvedi said.

But predictable bills are not necessarily lower bills, warned, Igor Ikonnikov, advisory fellow at Info-Tech Research Group. “The hardware requirements for high-speed operational data processing are higher and relatively more expensive compared to cheap lakehouse storage,” he said.

EDB’s architecture could also simplify data governance by reducing the number of platforms enterprises need to manage. Since operational, analytical, and AI workloads can access data through a common Postgres-Iceberg foundation, enterprises may be able to avoid deploying and governing multiple specialized data stores, and so have fewer systems to license and secure, according to Devin Pratt, research director at IDC.

Reducing architectural tax for engineering teams

EDB’s converged analytics could also simplify operations for developers and data engineering teams.

Its architecture reduces the number of systems developers must integrate and maintain, while eliminating much of the pipeline work traditionally required to move data between transactional and analytical systems, according to Walter.

And, said Pratt, “Zero-ETL means far less plumbing to build and break, so engineers spend their time creating value.”

EDB and Databricks are not the only ones pursuing converged analytics to support agentic systems and other applications needing immediate access to operational data, historical context, and governance controls.

Snowflake has been expanding support for operational workloads by embracing open table formats,and Microsoft has combined transactional and analytical services under a broader data architecture via its Fabric platform.

Evolution of autonomous databases?

Converged analytics, though, was only one part of EDB’s update to its Postgres AI platform.

It has also made generally available what it calls an “agentic database” feature, designed to automate routine database administration tasks.

The system continuously monitors hundreds of operational and performance metrics, detects anomalies, recommends corrective actions, and, where enterprise policies permit, can automatically apply fixes, the company said.

These automated agents can help enterprises optimize and tune their databases up to 10 times faster, it said.

Walter remained skeptical: “It is more an evolution of autonomous database concepts than a wholly new category. Oracle and other database vendors have offered autonomous database capabilities for years.” Where EDB can differentiate itself, she said, is in extending those autonomous capabilities with AI-driven reasoning, automated remediation, and governance controls that allow enterprises to determine how much authority the system receives.

Licensing can be complicated, particularly when enterprises are forced to double-pay because the software they already own is only licensed for a specific environment, and moving it requires a whole different licensing model. Without proper portability rights, they need to make additional financial investments to run the same workloads in a different home.

AWS says its new Bring Your Own Media (BYOM) service eliminates this duplication. Customers can now reuse their existing Microsoft SQL Server media and licenses on Amazon Relational Database Service (RDS) with no additional licensing fees.

This means enterprises no longer have to justify workload migrations to the cloud against existing licensing commitments and infrastructure investments, AWS said.

“What used to be a licensing barrier between operational SQL Server data and agentic AI is now gone,” AWS data engineer Srikanth Katakam and product marketing manager Colleen Betik wrote in a blog post announcing the service.

Avoiding the double-licensing problem

To drive true value, agentic AI apps must have access to elastic GPU capacity as well as direct, low-latency access to the data they need to reason and make decisions. But, AWS argued, that can be difficult in self-managed data centers and on premises infrastructure with limited access to agentic AI cloud-native services. But a lot of enterprise data lives in Microsoft SQL Server, and until now customers have had to pay for a second license to use a fully managed cloud service like RDS.

Now, with BYOM on Amazon RDS for SQL Server, enterprises can reuse their existing SQL Server Enterprise Edition or Standard Edition licenses through a “lift-and-shift” model. This brings their data into a fully-managed environment.

According to the licensing distribution terms, enterprises must provide their licensed SQL Server Release to Manufacturing (RTM) media to Amazon RDS. They can then upload that media to Amazon S3 and launch BYOM instances. Enterprises must configure AWS License Manager to perform automatic instance tracking.

From there, RDS automates patching, backups, high availability, and monitoring, while providing direct access to agentic AI and analytics services native to AWS. The platform also reports vCPU usage to provide visibility into SQL Server license usage, Katakam and Betik wrote.

“You do not need to choose between protecting your SQL Server licensing investments and giving your data a path to the AWS analytics and agentic AI services redefining what’s possible in the cloud,” they said. 

It’s important to note that this service is only available to enterprises with Microsoft Software Assurance (SA), an add-on which supports mobility and rehosting of existing licenses. Enterprises must verify that their SQL Server licenses comply with Microsoft’s licensing agreement, and that they have a SQL Server License (Standard or Enterprise) with SA. They also must submit a License Mobility Verification Form to Microsoft; it, in turn, will notify AWS once verification is complete.

AWS emphasized that enterprises remain responsible for compliance; it does not block operations if license limits are exceeded.

More control over workloads, loosening Microsoft’s grip

The advantage of this service, explained Mike Leone, principal analyst at Moor Insights & Strategy, is that enterprises can get a Microsoft SQL Server workload onto a managed AWS service without rewriting it for Aurora or paying for the license twice. This means they can modernize on their own schedule rather than being forced into rewrites before they’re ready.

“For a lot of shops, that control over timing is worth more than the license saving itself,” Leone said.

This also loosens Microsoft’s grip on enterprise workloads, because organizations finally have somewhere else to run SQL Server using licenses they already own, he noted. Realistically, their dependency shifts to AWS rather than disappearing entirely, as their data ends up living next to AWS’s AI services.

“For a lot of teams, that’s a trade they’re glad to make for the managed infrastructure and the AI tooling they get in return,” Leone said.

Yaz Palanichamy, a senior advisory analyst at Info-Tech Research Group, also pointed to significant improvements stemming from the migration. Notably, it allows organizations to transition away from static, linear, or reactive storage capabilities towards more dynamically intelligent environments.

“The key is to balance the unification of transactional data towards managed AI and machine learning pipelines,” he said.

Enterprises take on new responsibilities

The catch? Leone noted that enterprises now own the task of licensing compliance. Staying current on SA, the Microsoft maintenance contract that makes any of this legal, and the license mobility rules, “become your headache instead of something baked into the bill,” he said.

Furthermore, he added, lift-and-shift has a way of turning into “lift-and-forget” when enterprises move SQL Server as-is and never actually modernize, “so you end up carrying all the old baggage onto a shinier platform.”

Palanichamy also pointed to skyrocketing AWS costs, since operationalizing AI agents requires a considerable amount of data ingestion and querying. This can potentially be cost prohibitive on RDS for SQL Server.

“One aspect to consider would be the relative time to production value, so that enterprises can better handle the management of volatile AI workloads,” he said.

Just one challenge in the AI race

Although AWS frames licensing complexity as a roadblock to agentic AI, analysts say it’s really just one piece of the puzzle.

Organizations are not in an AI-ready state due to a number of factors, Palanichamy noted. This could be total cost of ownership (TCO)-related, a lack of appropriate acceptable use policies (AUPs), or concerns around security and compliance.

If and when enterprises are ready to adopt AI, they must perform thorough needs analysis/process optimization benchmarking exercises to determine what workflows could benefit from the technology, he said, and, conversely, flag workflows where AI could prove “an impediment or potential disservice.”

Leone also pointed out that moving SQL Server onto RDS doesn’t “magically make your data agent-ready,” nor does it make it smarter. The data proximity and managed plumbing are what actually support agents. Sitting data next to Bedrock and the rest of AWS’s AI services means builders “stop wasting time shipping the data around or building one-off integrations every time an agent needs it.”

Ultimately, though, licensing isn’t the real issue when it comes to AI; it’s more of a migration problem. The real roadblocks are messy data, questionable governance, and teams that aren’t ready to run it in production, and, Leone said, “no change to a license bill touches a single one of them.”