The State of IBM i Security: AI Raises the Stakes on Long-Standing Gaps
Many IBM i environments still have correctable exposures in foundational controls, not because teams don’t care, but because the platform has a long-standing, misguided reputation as being inherently secure. The stability and longevity of these systems has allowed lax controls to perpetuate.
As teams adopt AI assistants and agentic workflows, weak controls become easier to discover, easier to exploit, and harder to explain to auditors after the fact.
Foundations that matter even more in an AI era: understand who can access what data (and through which entry point – there are many!), enable auditing across all access points, and translate IBM i security events so non-IBM i security teams can act on them.
Establish a Baseline (Before You Add AI to the Mix)
If you’re not proactively assessing your current security posture, it’s easy to confuse “we’ve never had an incident” with “we’re secure.” In practice, teams get traction by combining a snapshot assessment with repeatable operational monitoring:
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Run a security configuration scan to generate a baseline report and highlight common IBM i exposures.
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Automate monitoring so inappropriate configurations don’t quietly accumulate between audits.
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Perform a periodic deep-dive risk assessment that maps current system settings to best practices and produces auditor-friendly findings and remediation guidance.
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Modernize authentication (for example, SSO and MFA) to reduce password sprawl and improve the consistency of access control.
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Validate with penetration testing when required by governance or auditors and treat remediation as an ongoing program – not as a one-time project.
AI as a Security Enabler: Faster Detection, Better Prioritization, Less Noise
Used well, AI can help security teams move from “more alerts” to “more signal.” Modern AI-powered security capabilities can spot abnormal access patterns, summarize incidents for analysts, and accelerate triage, which is going to improve speed and consistency without removing humans from the decision loop.
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Faster threat detection and triage: AI can help identify anomalies and produce useful summaries so analysts spend less time on “what happened?” and more time on containment and recovery.
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Predictive risk analysis: by correlating signals across tools and time, AI can help teams prioritize which exposures are most likely to turn into incidents next.
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Operational automation: use AI to accelerate repeatable work (classification, routing, evidence collection), while keeping change approval and high-risk actions governed.
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Compliance readiness: AI can help turn raw events into auditor-consumable narratives—especially when IBM i teams must explain platform-specific events to broader security and GRC stakeholders.
AI as a New Attack Vector: Same Risks, Less IBM i Skills Required
Here’s the uncomfortable reality: “free” and company-endorsed AI chatbots can make it dramatically easier for inexperienced users to generate commands, scripts, and step-by-step instructions that change configurations or access data. This doesn’t automatically create a breach, but it does increase the odds of well-intentioned changes being made without understanding IBM i-specific security implications (for example, opening an access path to “get something working,” then forgetting to roll it back).
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Lowered barrier to change: AI can quickly generate “the command to run” or “the setting to flip,” even when the user doesn’t fully understand downstream impact.
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Misplaced trust: generic AI answers may sound confident while missing IBM i nuance (authorities, exit points, adoption, audit journaling, etc.).
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Exception creep: when AI suggests workarounds, teams may normalize one-off exceptions that quietly become permanent exposures.
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Amplified insider risk: if a user already has excessive authority, AI can make it easier to misuse, or even abuse, that authority only now they can do it faster and with fewer mistakes.
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