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The Latest Cybersecurity News & Insights From Around The World
AI-generated code is running wild inside the enterprise. Now what?
Restrict access to AI tools and you curb innovation. Open it up and security risks multiply. And then there's a third problem: approved tools behaving in unapproved ways. Security and IT leaders are navigating a new and fast-moving problem - employees using AI to build workflows, automations, and agents faster than anyone can track or govern. The question isn't whether it's happening. It's what to do about it.
Monitor Netskope ADEM scores and remediate with an AI chatbot
Automatically detect when user connectivity degrades in Netskope ADEM and respond instantly with an AI-powered Slack chatbot. In this five-minute flow, we walk through how to monitor Netskope ADEM experience scores for key users and trigger proactive outreach via Slack when performance drops. You'll see how Tines pulls scores on a schedule, creates a case when a threshold is breached, uses an LLM to craft a personalised Slack message, and deploys a Virtual Assistant to help the user troubleshoot in real time.
Secure AI for the real world
AI makes building look easy. That’s the trap. Without a secure, well-designed foundation, workflows break, costs spike, and systems grow fragile. CTOs and CISOs from leading organizations discuss what breaks without a secure foundation, and how to build AI systems that hold up at scale. This session goes deep on the real-world tradeoffs between speed, risk, and trust.
Export Code42 cases to Jira and email automatically
If your security team is managing insider risk or data loss investigations in Code42, keeping Jira and your inbox in sync is tedious. This story from the Tines library solves that by automating the full export process end-to-end. In under five minutes, you'll see how Tines lists all open Code42 cases, deduplicates them to avoid repeat alerts, downloads each full case export as a zip file, creates a pre-populated Jira ticket with key case details, attaches the export to that ticket, and emails it directly to the relevant recipient.
Human-in-the-loop workflows: where intelligent automation meets judgment
Security and IT leaders face a contradictory mandate: move faster with AI and automation while maintaining governance over every action that touches production systems, user accounts, and sensitive data. Most tools force a choice between two failure modes. Either the workflow runs autonomously, and the team hopes nothing breaks, or every action requires manual approval and analysts spend their shifts rubber-stamping low-risk steps until oversight disappears behind a green-checkmark audit trail.
Agentic workflow automation: governing AI agents inside workflows
AI agents don't behave like the playbooks security and IT teams have spent years building. They form intent, select tools at runtime, and chain actions across systems in sequences nobody pre-authored. This means dropping an LLM into an existing automation sequence and expecting it to act like a smarter playbook is the fastest route to ungoverned, unpredictable outcomes.
Compliance workflow automation: making SOC 2, GDPR, and ISO auditable by design
Compliance teams know the pattern well: tracking down a missing access review sign-off at 11 p.m. the night before an audit, piecing together evidence from spreadsheets, email threads, and the gap between HR and IT. Access reviews keep appearing in SOC 2 exceptions, and the controls usually aren't the problem. The manual processes around them are. Many teams respond by buying a dedicated GRC (Governance, Risk, and Compliance) platform. Traditional GRC tools are structured repositories.
How to build AI agents your security team will approve
A security engineer spends three weeks building an AI agent that triages phishing reports. The demo lands well. Then it hits the security review queue, and the questions start: Which tools can it call? What happens if it misclassifies? Who approves an account lockout at 2 a.m.? Where are the logs? Three more weeks pass, and the agent is still sitting in staging. This is the pattern most teams run into. The agent works, but the governance story doesn't.
Intelligent workflow design: seven principles for enterprise teams
Enterprise automation keeps running into the same wall. Teams inherit tools built for a tidy world, then deploy them into one where alerts arrive at odd hours, APIs change without warning, and the "obvious" next step depends on context no playbook anticipated. The usual response, buying a platform, scripting every scenario, and bolting on an AI copilot, leaves the on-call engineer debugging the automation instead of the incident.