Data Engineering & MLOps

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Reversing enterprise security costs with AI vulnerability discovery

Automated AI vulnerability discovery is reversing the enterprise security costs that traditionally favour attackers. Bringing exploits to zero was once viewed as an unrealistic goal. The prevailing operational doctrine aimed to make attacks so expensive that only adversaries with functionally unlimited budgets could afford them, thereby disincentivising casual use. However, the recent evaluation by the […]

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OpenAI Agents SDK improves governance with sandbox execution

OpenAI is introducing sandbox execution that allows enterprise governance teams to deploy automated workflows with controlled risk. Teams taking systems from prototype to production have faced difficult architectural compromises regarding where their operations occurred. Using model-agnostic frameworks offered initial flexibility but failed to fully utilise the capabilities of frontier models. Model-provider SDKs remained closer to

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Commvault launches a ‘Ctrl-Z’ for cloud AI workloads

Enterprise cloud environments now have access to an undo feature for AI agents following the deployment of Commvault AI Protect. Autonomous software now roams across infrastructure, potentially deleting files, reading databases, spinning up server clusters, and even rewriting access policies. Commvault identified this governance issue and the data protection vendor has launched AI Protect, a

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SAP brings agentic AI to human capital management

According to SAP, integrating agentic AI into core human capital management (HCM) modules helps target operational bloat and reduce costs. SAP’s SuccessFactors 1H 2026 release aims to anticipate administrative bottlenecks before they stall daily operations by embedding a network of AI agents across recruiting, payroll, workforce administration, and talent development. Behind the user interface, these

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Strengthening enterprise governance for rising edge AI workloads

Models like Google Gemma 4 are increasing enterprise AI governance challenges for CISOs as they scramble to secure edge workloads. Security chiefs have built massive digital walls around the cloud; deploying advanced cloud access security brokers and routing every piece of traffic heading to external large language models through monitored corporate gateways. The logic was

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IBM: How robust AI governance protects enterprise margins

To protect enterprise margins, business leaders must invest in robust AI governance to securely manage AI infrastructure. When evaluating enterprise software adoption, a recurring pattern dictates how technology matures across industries. As Rob Thomas, SVP and CCO at IBM, recently outlined, software typically graduates from a standalone product to a platform, and then from a

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AI’s software development success and central management needs

A survey carried out by OutSystems, The State of AI Development 2026 [email wall], argues that AI has moved into early production phase for many enterprises, primarily inside the IT function. The survey was based on the responses of 1,879 IT leaders, and warns that adoption of AI is in danger of running ahead of

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KiloClaw targets shadow AI with autonomous agent governance

With the launch of KiloClaw, enterprises now have a tool to enforce governance over autonomous agents and manage shadow AI. While businesses spent the last year securing large language models and formalising vendor agreements, developers and knowledge workers started moving on their own. Employees are bypassing official procurement, deploying autonomous agents on personal infrastructure to

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Autonomous AI systems depend on data governance

Much of the current focus on AI safety has centred on models – how they are trained and monitored. But as systems become more autonomous, attention is changing toward the data those systems depend on. If the data feeding an AI system is fragmented, outdated, or lacks oversight, the system’s behaviour can become more unpredictable.

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KPMG: Inside the AI agent playbook driving enterprise margin gains

Global AI investment is accelerating, yet KPMG data shows the gap between enterprise AI spend and measurable business value is widening fast. The headline figure from KPMG’s first quarterly Global AI Pulse survey is blunt: despite global organisations planning to spend a weighted average of $186 million on AI over the next 12 months, only

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