Cybersecurity for the AI Era
Security that assumes someone is already inside.
What is Cybersecurity for the AI Era?
The perimeter stopped being the control years ago. Your people are remote, your systems are someone else's cloud, and now you have AI agents holding credentials and making requests at three in the morning. The question isn't how to keep attackers out. It's what they can reach once they're in.
So most of this work is identity. Who is this, what are they allowed to touch, and how would we know if that changed. That covers Zero Trust architecture, IAM and access reviews, machine and service identity, and, increasingly the awkward one, identity for AI agents, which need credentials but can't do MFA and don't get fired when they misbehave.
AI systems are also a new way in. So one phase of the work is threat modelling and red-teaming the AI applications you run: prompt injection, data leaking through outputs, and what an agent can be talked into doing with the access it holds.
We won't sell you a tool to fix this. Most breaches we see aren't a missing product; they're an account that should have been removed years ago, or a service principal with permissions nobody ever scoped. That's boring to fix and it's usually the fix.
Services provided
What we see in security
- 01 It's usually identity
Most breaches we see trace back to identity rather than a missing product: a stale account, an over-permissioned service principal, a credential nobody owns.
- 02 Non-human identities drift
Service accounts, API keys and AI agents multiply faster than anyone reviews them. Non-human identities are usually where access has drifted furthest.
- 03 An agent is an identity
An AI agent is an identity. It needs scoped credentials, a named owner and a way to be revoked, the same as any person who joins.
- 04 Prompt injection is common
Prompt injection is the most common weakness we find in AI applications. Unless a system has been specifically hardened against it, assume it is exposed.
- 05 AI needs its own threat model
Traditional application security testing misses what is particular to AI: injected instructions, data leaking through outputs, and agents persuaded to misuse the access they hold. AI applications need their own threat model.
- 06 Zero Trust is a sequence
Zero Trust is a sequence of decisions, not a product. The order you do it in matters more than the tooling.
Related reading
- Technology & AI
Cybersecurity for the AI Era: Defending AI, and Defending Against It
Cybersecurity for the AI era has two sides: AI-powered attacks and the AI systems you deploy. How to defend both with one identity-centred posture.
- Technology & AI
Red-Teaming Your AI: The Attack Surface You Are Not Testing
AI red-teaming explained for leaders: the threats standard security testing misses, how a structured exercise runs and the fixes that reduce real risk.
- Technology & AI
Zero Trust Is an Operating Model, Not a Product
Why a zero trust operating model beats buying tools: identity as the perimeter, controls for AI agents and machine identities, and a wave-by-wave plan.
Where Ganexa stands out
We start with identity, not products. It's where the actual risk is, and it's the least exciting slide in the deck.
We'll tell you when the fix is unglamorous. Deleting stale accounts beats buying a platform, and we'd rather say so.
AI agents get treated as identities. They hold credentials and act on their own; most access models have no idea what to do with that.
Security that survives us leaving. If a control needs a specialist to operate, it stops working the month we go.
Your engagement roadmap
Know every identity. Trust none by default.
Built for where you are
Organisation with years of accumulated access
“People have joined, moved and left for years and nobody has cleaned up behind them. We suspect there are accounts and service principals with access nobody can explain, and our auditors have started asking.”
We review who and what can reach your critical systems, remove or scope what can't be justified, and leave behind an access review process light enough that your team will keep running it.
What good looks like: every identity with access to a critical system has an owner and a reason, and the review still happens after we've gone.
Enterprise putting AI agents into production
“We're giving AI agents access to real systems. They hold credentials and act on their own, and our access model was designed for people. Nobody can tell me what an agent is allowed to do or how we'd stop one.”
We design an identity model for agents and other non-human identities: how they authenticate, what they may touch, who owns them, and how access is revoked. Then we fit it into your Zero Trust roadmap rather than alongside it.
What good looks like: each agent runs with scoped, owned credentials, its actions are attributable, and switching one off is a documented step rather than a scramble.
Company deploying customer-facing AI
“We're launching an AI chatbot for customer service but our security team doesn't know how to test it. Traditional penetration testing doesn't cover prompt injection or data leakage.”
We threat-model the application and run an AI red-team exercise: prompt injection, jailbreaking attempts, data extraction probes and adversarial inputs. We document each finding, harden the system, and check what the chatbot's own credentials can reach.
What good looks like: the chatbot launches with its security testing documented, known attack routes closed, and access limited to what it needs to answer customers.
What you walk away with
Identity and access review
Who can reach what, including the accounts and service principals nobody remembers creating.
Zero Trust roadmap
Sequenced against your real systems, with the order to do it in and what to skip.
AI-agent identity model
How agents authenticate, what they may touch, and how you revoke it.
AI threat model and red-team findings
How your AI applications can be attacked, what we found when we tried, and the fixes in priority order.
Access review process
Light enough that it still happens after we've gone.
Incident path
Who decides what, at what hour, agreed before it's needed.
Is your security posture ready for the AI era?
In a 30-minute security call, we'll look at who and what can reach your critical systems, where AI agents and other non-human identities sit in that picture, and which of your AI applications are most exposed. Whether you're cleaning up years of access, giving agents credentials for the first time, or putting a chatbot in front of customers, we'll give you a clear starting point.