AI in 2026: Why Identity and Data Security Can’t Stay Separate AI is no longer a side project but a part of our daily work. People use generative tools to write, summarise, code, and analyse. Teams connect AI apps to SaaS platforms, cloud services, endpoints, and email. Some workflows now run with minimal human review. This speed changes how risk shows up. In older models, “identity” and “data” were treated as separate lanes. Identity teams focused on who could log in and what they could access. Data security focused on what information was being stored, shared, and protected. These lanes made sense when most actions were human and predictable. In 2026, those boundaries are harder to hold. AI agents can access systems, generate output, move files, call services, and pass information from one place to another. The action and the data travel together. If you keep identity and data in silos, you miss the full story and small gaps become easy entry points. Below are 5 shifts that are shaping security decisions in 2026. They all point to one priority: you need controls that connect who (or what) is acting with what data is being used, in real time.
#1 Security is Circling Back to Two Basics: Identity and Data Modern environments are noisy - hybrid cloud, SaaS sprawl, contractors, BYOD and shadow AI tools. In this mess, many controls are layers on top of two truths • •
Identity tells you who or what is acting, and under which permissions. Data tells you what matters most, and what would hurt if exposed.
Perimeters matter less when access is everywhere and workloads move. What matters is context: Which identity? Which device? Which app? Which dataset? Which action? That’s the lens that holds up when AI tools operate across systems at machine speed. This is also where a practical cyber security audit pays off. A good cyber security audit doesn’t only list controls. It maps how identities and data connect across your real workflows— especially the ones involving AI. Our services cover information security audits aligned to recognised frameworks and compliance needs, including ACSC Essential Eight and APRA CPS 234. #2 Rule-Based Risk Models Will Struggle with Non-Deterministic AI Behaviour Traditional security loves certainty. “If X happens, block Y.” It works when behaviour is stable and patterns repeat. AI breaks that comfort. AI systems can change output based on prompts, context, and tool access. Agents may take different steps to reach the same goal. That doesn’t mean they’re “bad.” It means static rules can create more noise than signals. In 2026, many organisations are moving toward risk models that adjust based on real-time context. Instead of only asking “Is this allowed?”, they ask: • • • •
Is this identity human, machine, or agent? Is the action normal for that identity? Is the data sensitive in this context? Is the request coming from a risky location, device, or app?
You can support this shift with targeted testing that reflects real usage. For example, API penetration testing helps you see how agents, integrations, and automated scripts might reach data through APIs you rely on every day. It is also a strong fit when you have SaaS-to-SaaS connections and GenAI tools calling external services. And when you run tests, pair them with disciplined ethical hacking. It gives you an attacker’s view of how small misconfigurations and weak assumptions become a path to data exposure. Understand how baseline security is no longer enough in 2026. #3 CISOs are Becoming Enablers of Trusted Autonomy Security leadership is changing. In the past, the job was often to block risky behaviour. That approach doesn’t scale when the business is pushing automation and AI-enabled workflows into every team. The new job is to enable autonomy with guardrails. That means building systems where AI tools can do useful work, but still respect:
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least privilege access data classification rules approval steps for high-risk actions logging this supports investigation and accountability
This shift aligns with where regulators and policymakers are heading, especially around accountability and traceability. In Australia, government guidance for AI use highlights accountability expectations and risk ownership for AI use cases, including higher scrutiny for high-risk use cases. A cyber security audit can help here too, because trusted autonomy starts with clarity. If you can’t explain who acted, what they accessed, and why, you can’t safely automate. #4 Agentic AI Adoption Will Likely Outpace Reliability In 2026, agentic tools are moving from “assist” to “execute.” They can open tickets, provision access, move files, generate code, and push content. Adoption is fast because the business value is obvious. But reliability often lags. Early deployments commonly suffer from: • • • • •
over-privileged agent identities missing context about data sensitivity weak approval gates logs that don’t show the full chain of actions inconsistent policy enforcement across apps
The biggest failures may not look like dramatic breaches. They can be quiet problems: data shared to the wrong workspace, sensitive content pasted into the wrong tool, or an agent taking an action that was “allowed” but clearly not intended. This is where a strong cyber incident response team matters. A capable team does more than react. It prepares playbooks, tunes detection, and shortens the gap between signal and action. CGI offers incident response and managed security services built around live monitoring and response support. If your business is rolling out agents, plan response as part of rollout. Run exercises. Test your escalation paths. Make sure your cyber incident response team can trace agent actions back to identities and datasets. #5 AI Governance and Regulation Will Grow, Even if It’s Uneven Regulation rarely arrives in a clean package. More often, expectations grow through guidance, sector requirements, and enforcement patterns. Heading into 2026, Australia has been leaning on existing, technology-neutral laws, supported by guidance and policy initiatives rather than one single “AI law.” That still creates real compliance pressure, because organisations must show accountability, data protection, and sensible governance around AI use. In practice, this pushes you toward better answers to basic questions:
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Who (human, machine, or agent) took the action? What data was used, shared, or changed? What controls were in place at the time? What evidence can you provide after the fact?
If your identity tools and data tools don’t connect, these answers take longer, cost more, and leave uncertainty when it matters most. How PCL DSS will expose weak cyber governance at the executive level. What You Should Do Now You don’t need a brand-new security philosophy. You need tighter execution around the new reality: 1. Map AI workflows to identities and data paths. Don’t guess. Track where AI tools read from and write to. 2. Reduce privilege for agents and integrations. Make permissions specific and timebound where possible. 3. Test the paths attackers will use. Run API penetration testing on the services your AI tools depend on. Run it again after major integrations change. 4. Use disciplined adversarial testing. Ethical hacking should reflect your real environment: SaaS, cloud, endpoints, and the “glue” between them. It should also include identity abuse scenarios. 5. Treat response as a design requirement. Ensure your cyber incident response team has visibility into agent activity, not just endpoint alerts. A Final Note The goal is not to stop AI adoption. The goal is to make it safe to scale. When identity and data are managed together, you reduce blind spots and improve decision-making under pressure. That’s how you build resilience in 2026 without slowing the business down. If you are looking for someone to take care of your AI systems, we can help. Check our website at http://cyberneticgi.com/ and get in touch asap! At CGI, we are a 360-degree cyber security agency striving to make it safe for businesses, enterprises and companies. Resource URL: https://www.cyberneticgi.com/ai-in-2026-why-identity-and-datasecurity-cant-stay-separate/
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