Your organisation is already using AI. Who controls it?
AI Security
AI has moved from experiment to business infrastructure in under two years. Employees work with public AI tools daily. Teams build on private models. AI agents execute tasks autonomously across your application landscape. In most organisations, security has not kept pace with any of it.
Only 7 percent of enterprises have real-time security governance for AI in place. By 2028, at least three quarters of all businesses are expected to be running agentic AI. The gap between those two numbers is precisely where attackers are focusing their attention.
Heimdallr helps organisations close that gap; not with generic promises, but with an approach rooted in Zero Trust and built on a current, accurate picture of the threat landscape.
"Most breaches start with access that was never supposed to be permanent"
AI risk now operates on three fronts simultaneously
The threat has long since moved beyond employees pasting sensitive data into a public chatbot. Three distinct fronts have opened up, each with its own exposure profile.
Public SaaS covers the external AI tools your people use every day, frequently outside the visibility of IT and without any governance layer in place.
Private AI covers proprietary models and applications running on your own cloud or enterprise infrastructure, with their own data pipelines and their own vulnerabilities.
Agentic AI is where the risk is growing fastest: systems in which a language model is given autonomy to pursue objectives, connecting to databases and APIs and acting continuously in the background without human intervention.
The third front is where most organisations are least prepared. An AI agent communicates directly with systems, bypassing virtually every security control designed around human behaviour.
"The attack surface did not expand because organisations made bad decisions. It expanded because they made fast ones"
These attacks do not look like software failures
Securing AI requires a different view of risk because most attacks target how models interpret language, not software bugs.
The most common threat is prompt injection, where crafted inputs override a model’s instructions. A growing form, indirect prompt injection, hides malicious instructions in documents or web pages processed by AI agents, causing silent changes in behaviour. OWASP ranks this among the top risks for AI applications, and recent vulnerabilities in major AI assistants have shown how effective it can be.
AI systems also face risks from data poisoning, sensitive information leakage, model theft through inference APIs, and supply chain vulnerabilities introduced by third-party models and components.
"When the vulnerability is the model's willingness to follow instructions, the threat is invisible until it isn't."
The fastest-growing risk is autonomy itself
Every permission granted to an agent can be exploited at scale. A manipulated agent moves data, triggers actions and crosses system boundaries far faster than any human attacker could; and it does so using access your organisation deliberately provisioned.
MCP servers introduce attack surfaces that did not exist two years ago, including the possibility of remote code execution through agent connections. Static credentials remain the weakest link throughout. A recent breach at a major AI platform made the exposure concrete: approximately 1.5 million API tokens were compromised, with keys remaining usable for up to 23 minutes after deletion.
The NCSC expects AI to accelerate offensive operations further. ENISA has identified AI-driven phishing and malware development as a structural trend, not an emerging one. The window to get ahead of this is open now; it will not stay open indefinitely.
"An honest baseline is uncomfortable, but also the only useful starting point."
AI Security implementation
AI Security is a natural extension of our existing security approach. We apply Zero Trust and digital sovereignty principles to AI systems through explicit verification, least-privilege access, and continuous monitoring of models, agents, and their connections.
Every engagement starts with an AI Security scan to identify AI tools, agents, embedded AI features, and MCP connections across your organisation, helping us pinpoint and prioritise the highest-risk areas.
AI risk and exposure mapping
A structured inventory of every AI surface in your environment: tools, models, agents, integrations and the permissions attached to each. The foundation for every control that follows.
Runtime guardrails
Controls that operate at the point where AI acts, not just at the point where humans configure it. Behavioural boundaries for agents, output filtering and anomaly detection built into the systems themselves.
Agent identity and permission governance
Every agent is an identity with access rights. We bring those identities under the same governance framework as human users: inventoried, scoped to least privilege and continuously monitored.
AI governance aligned to NIST, ISO/IEC 42001 and the EU AI Act
Governance that maps to the frameworks your organisation is accountable to, implemented as operational controls rather than documented policies that sit in a folder.
AI red teaming
Structured adversarial testing of your AI systems and agent pipelines, covering prompt injection, indirect injection, model extraction and MCP attack surfaces. Not a one-time exercise; a repeatable capability.
"An agent does not hesitate, reconsider or ask for approval"
The frameworks exist. Operationalisation is where organisations stall
NIST AI RMF and ISO/IEC 42001 provide solid structural guidance. The EU AI Act makes risk management for certain AI systems a legal requirement. The challenge is not knowing what good looks like; it is building the operational capability to get there: knowing which AI systems are running, which agents hold which permissions, and what is actually happening at runtime.
Organisations that invest now in agent inventory, runtime guardrails and structured AI red teaming build a measurable advantage. That same work also feeds directly into EU AI Act compliance, rather than running as a parallel effort alongside it.
Frequently asked questions
What does an AI Security scan involve?
We map every AI surface active in your organisation: public SaaS tools, embedded AI features in existing software, private models, autonomous agents and MCP connections. For each, we assess the permissions in place, the data it accesses and the governance currently applied. The output is a clear risk inventory and a prioritised set of next steps, not a generic maturity framework filled in with your name at the top.
How does AI Security relate to our existing Zero Trust programme?
Directly. Zero Trust establishes the principle that no identity earns implicit trust; AI agents are identities, and the same controls apply. Organisations with a mature Zero Trust foundation can extend it to cover AI actors. Organisations earlier in that journey will find that AI Security work and Zero Trust implementation reinforce each other at every layer.
What is the EU AI Act and what does it require from us?
The EU AI Act classifies AI systems by risk level and imposes proportionate requirements on organisations that develop or deploy them. High-risk systems require documented risk management, data governance, human oversight and transparency measures. Most organisations are further inside the Act’s scope than their initial assessment suggested. Heimdallr’s AI governance work is structured to produce compliance outputs as a by-product of building real controls, rather than as a separate documentation effort.
What makes agentic AI harder to secure than other AI systems?
Scope and speed. An AI agent does not wait for a human to review its next action; it acts, often across multiple connected systems, using permissions that were deliberately granted. A compromised or manipulated agent can exfiltrate data, trigger downstream processes and move across system boundaries in seconds. The security controls that work for human users, which assume a human decision point between intent and action, do not translate directly to agents.
How is prompt injection different from a conventional cyberattack?
A conventional attack exploits a flaw in code or configuration. Prompt injection exploits the way a language model is designed to work: it processes and follows instructions. An attacker who can get malicious instructions in front of a model, whether directly or hidden inside content the model is asked to process, can redirect its behaviour without touching any underlying system. There is no patch that eliminates the risk entirely; it requires architectural controls and runtime monitoring.