Explore effective strategies and learn how organizations can enhance Attack Surface Management using artificial intelligence (AI) automation. Read more.
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Vincent Thiele
Deputy CISO
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10
Jun 2021
Effective attack surface management breaks down into four key parts:
Discovery
Assessment and prioritisation
Risk prevention
Integration
Discovery
The most important part of any attack surface management process is the discovery phase. As we’ve mentioned previously, most organizations miss as much as 30 to 50 percent of their true attack surface. This creates huge blind spots when it comes to understanding the real risks your organization is facing. In fact, we believe that something like 80% of all organizations’ cybersecurity problems exist due to blind spots in their attack surface. Eliminating those blind spots should be your core goal when setting out to discover your attack surface.
One of the biggest contributing factors to these blind spots is scoping. Organizations tend to work within a predefined scope when it comes to discovery – whether that’s within a defined IT infrastructure or a set of IP addresses that the organization uses and wants to protect.
However, the reality is that any would-be attackers out there do not care about your scope. Whether you have a vulnerability in-house, via an external supplier, or in a cloud environment; an attacker will find it and try to misuse it.
On top of this, with modern organizations having so many external dependencies, regulatory bodies are starting to insist that financial services companies look beyond the boundaries of their own networks when analysing risk.
For example, the Digital Operational Resilience of the financial sector (DORA), extends the focus of risk management to cover all external assets of the parties they have relationships with (such as fourth and fifth parties, or parties that you even don't know you have a relationship with), as well as any services and infrastructure that can impact on the organization’s day-to-day resilience.
Taking a zero-scope approach to your attack surface is therefore essential. This means that tracking down the full extent of your attack surface is not something that can be left to human endeavour. Relying on people to trace and discover your connections would be hugely expensive, time consuming, and error prone. So, a prerequisite of effective discovery is automation. AI and algorithmic-based discovery is the only way to uncover the bulk of your attack surface.
Assessment and Prioritization
Monitoring your attack surface is not a one-time, set-and-forget process because your attack surface is not static; it’s constantly evolving and changing. This means you need to be continuously monitoring it for changes, and you need to be alerted when those changes occur, so that you can decide whether or not something needs your attention. This data needs to be dynamic and as close to real-time as you can get.Only once you know what your full attack surface looks like can you really start to analyse the risk types presented to the business. These risks can be extensive and need prioritisation according to the threat presented to the brand. As the discovery process continues, you need an automated risk assessment tool to save time and effort. It will tell you what the most pressing risks are, so you can focus on the mitigating process. Then, even when the discovery process is ongoing, new risks are automatically reported on, and added to right lists. This ensures you stay up-to-date on the actual risks to the organization – removing a lot of the stress from the process for CISOs.
Risk Prevention
There is a flipside to seeing your whole attack surface. Having a comprehensive overview of every risk your organization faces can be terrifying – you are going to uncover a multitude of things you’d never considered. This can leave you facing one big question: “Do I really want to know all of this?” Most CISOs and cybersecurity professionals are already busy with all the things they do know about, so you need to have a way of striping out and solving some of the possibly lower priority but easier to solve risks in your report. This is again where automation can come into play. Automatically categorizing similar risk types throughout your attack surface will save time and effort when reducing risks.
Integration
Finally, it doesn’t matter how much data you have on something; if it’s not actionable it’s ultimately not going to be practical to use. So you need to be able to connect all that data coming from your attack surface monitoring into your existing internal processes and systems (your PSA, ITSM, or ESM tools etc). That way you can build it into your existing cybersecurity structure and minimise extra work.
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Testing a Prompt injection Attack Against an Enterprise AI Agent
Key takeaways
Darktrace successfully detected and quarantined a prompt injection email before it could be processed by an enterprise AI agent.
Prompt injection attacks increasingly rely on natural language rather than traditional malware, making behavioral analysis an important complement to signature-based detection.
Organizations deploying AI agents should combine model guardrails with behavioral monitoring to reduce the risk of malicious instructions reaching enterprise systems.
How behavioral detection helps stop prompt injection attacks
A Darktrace customer running a Gemini AI agent in Google Cloud asked us two simple questions:
“If my agent can read inbound emails and access internal data, what stops an attacker from hiding malicious instructions in the message? Couldn’t the agent be tricked into deleting or exfiltrating sensitive data?”
The scenario centers on an indirect prompt injection attack, where malicious instructions are hidden inside content that an AI model later interprets as trusted input.The same weakness was exposed by EchoLeak (CVE-2025-32711), a zero-click Microsoft 365 Copilot vulnerability enabled data exfiltration from a single well-crafted email..
This blog follows the Darktrace team’s investigation of the customer’s hypothesis and examines how the attack interacted with their existing security stack. The results highlight which defenses held, where gaps emerged, and how behavioral detection mattered more than guardrails. This investigation also demonstrates why behavioral detection is becoming increasingly important for AI security, as prompt injections often contain no traditional indicators of compromise.
How do prompt injection attacks work?
Prompt injection works by carefully crafting the content and structure of the prompt to alter the LLM’s behavior or output in unintended ways. This can cause models to violate guardrails, generate harmful content or enable unauthorised access.
Prompt injection attack example
The well-known example, EchoLeak (CVE-2025-32711), was a zero-click vulnerability in Microsoft 365 Copilot that relied on a carefully crafted email containing hidden instructions that the AI system interpreted as commands rather than content, creating a pathway for unauthorized access to sensitive information without any user interaction.
While Darktrace / SECURE AI is designed to prevent agents from producing unintended outcomes, we wanted to see if we could catch and prevent this threat type earlier in the attack life-cycle, at the email security layer.
How we tested prompt injection attacks on an enterprise agent
Summary:
Claude generated a prompt injection payload.
Hidden instructions were embedded in an email.
The email passed traditional validation checks.
Darktrace analyzed the language and sender behavior.
The email was quarantined before the AI agent could process it.
To test Darktrace / EMAIL against this attack class, we opened Claude, gave it the customer's context and problem statement (Gemini agent with inbox access, internal tool calls), told it we were validating Darktrace / EMAIL's detection of prompt injections, and asked for a test payload. See below:
Figure 1
Figure 2
Despite the guardrails supposedly built into the model, Claude surprisingly gave us the entire exploit in plaintext (albeit very basic), illegible to a human as the text was sent in white text (see Figure 1) but framed as an authoritative override for anything downstream reading the mail programmatically (i.e. the Gemini agent).
How Darktrace detected a prompt injection attack
We then sent the Claude-crafted email from a freemail address to the target recipient’s inbox. Despite the email containing no malicious payload, the freemail address having no malicious reputation, and the validation checks all passing, Darktrace /EMAIL flagged the email as a 93/100 anomaly and moved it to junk, out of scope for the AI agent.
Figure 3: The test email sent with the hidden prompt injection
Figure 4: The email analysis in Darktrace / EMAIL
Figure 5: Darktrace / EMAIL detection of malicious activity
Theinteresting part is what triggered the detection (see Figure 5)
Possible machine prompt content: text in the body detected as instructions written for a machine to execute, not for a human to read
Possible machine prompt content + basic suspicious correspondence: the same content, correlated with sender-side anomalies: freemail domain (yahoo[.]com), unknown correspondent, no prior mail history with the recipient, and suspicious references to payment information
Neither of those is a signature match. Nothing in the email was on a blacklist. There was no malware, no link and no attachment. Darktrace analyzed the context in which the email was delivered and flagged it as likely risky. The anomalous language features and the context of the sender relative to the recipient's normal behavior, combined with the unusual hidden text (prompt) were enough for Darktrace / EMAIL to act on the risk.
Result: Darktrace / EMAIL autonomously junked the email, out of scope for any AI agent parsing the inbox.
Why behavioral security makes a difference detecting prompt injection attacks
Cyberattacks don't look like traditional exploits anymore. They now operate in natural language, not strictly code.
That breaks the traditional stack. AV, firewalls, static scanning and signature-based SEGs all assume a payload to inspect.
A prompt injection has no payload. It's just an instruction, written in natural language, dressed up as anything the attacker wants: an invoice, an HR request, a calendar invite, some simple PowerPoint slides.
EchoLeak proved that hidden instructions can sit inside an email invisible to the user but fully readable by the LLM, and the LLM will follow them blindly.
This test and GTG-1002 proved that the LLM itself can be socially engineered. Tell it you're an authorized tester and it will hand you the attack.
Rules and static classifiers can catch the obvious cases. But natural language has infinite variants, and the attack surface is the model's innate functionality to comply.
The deeper problem here is intent: an LLM can't reliably tell whether an instruction in its context came from its developer, its user, or an attacker who slipped it into an email. To the LLM, everything reads as language and looks like a legitimate ask. This is why behavioural detection wins, as you become aware of intent when you look at the context of an interaction. Does this sender normally send this kind of message to this recipient? Does this prompt fit the user's normal pattern? Is this agent behaving the way this agent normally behaves?
Intent can't be read off a single email, it emerges from behavioral context. Which is how Darktrace enables threat detection, through behavioral understanding.
Why enterprise AI security requires more than guardrails
Claude didn't roll over immediately… the first section of the response was a (slight) pushback, but then it wrote the payload anyway without having to ask twice.
Here the framing of the prompt did all the work. The “testing security capabilities” angle moved the model from refusal to unquestioned compliance to the user prompt.
This isn't the first time this has happened, of course. Anthropic disclosed in November 2025 that a Chinese state-sponsored group they tracked as GTG-1002 ran the first documented AI-orchestrated espionage campaign against ~30 targets by posing as employees of a legitimate cybersecurity firm doing authorised penetration testing.
The takeaway isn't that AI guardrails are ineffective. They raise the cost of low-effort attacks and remain an important first layer of defense. However, for most organizations today, they’re the only line of defense when deploying AI agents. If a prompt injection bypasses those controls, organizations still need a way to detect and stop malicious behavior elsewhere in the attack chain.
Attackers will continue to have working prompt injections easily and quickly. The question is what stops one when it lands in an inbox your agent is reading.
How Darktrace detects prompt injection attacks in emails
Two things Darktrace does that a model-level guardrail or static rules and signatures can't:
Natural language analysis at the email or prompt layer. The email is assessed on its own merits: is this content shaped like instructions for a machine, regardless of what the receiving agent decides to do about it?
Behavioral context around the language. An AI agent behaves like an extremely agreeable human, and it will go above and beyond to comply with the user’s request. That's exactly why you must consider the business context, such sender behaviour, mailing history, and organisational norms, as these matter even more when the recipient is an AI.
Darktrace has been perfecting behavioral anomaly detection for over a decade; the same self-learning approach that catches BEC and account takeover applies directly to prompt injection delivery. Our multi-layered AI stack extracts content from the message, builds behavioural understanding through social graphing and Pattern of Life analysis, and then combines natural language, topic, inducement, sender relationship and anomaly signals before deciding what action to take.
This matters for prompt injection because the threat is not the plain language itself, but the intent behind the language that can cause an AI agent to respond in unexpected ways.
How to secure enterprise AI operations from prompt injection attacks
Email was the entry point in this case, but it is only one of many possible vectors.
Anywhere an agent can retrieve information, an attacker can potentially introduce a prompt injection.
Emails, documents, SharePoint sites, web pages, knowledge bases, chat platforms, and third-party integrations all provide opportunities to influence an agent's behavior. Wherever an agent finds its orders, a prompt injection opportunity exists.
This is why securing AI requires more than blocking malicious inputs. Organizations also need visibility into how agents behave after consuming information from across their environment. If an agent begins accessing unexpected data, taking unusual actions, or operating outside its normal patterns, those behaviors may provide the strongest signal that something has gone wrong.
Effective AI security requires defense in depth: reducing the likelihood of malicious instructions reaching the agent while maintaining the ability to detect and investigate suspicious behavior if they do.
The challenge isn't protecting a single entry point. It's recognizing that, in an AI-powered environment, every source of information is also a potential source of influence.
Are you deploying autonomous agents across your enterprise and want to see this tested in your environment? Let's talk.
Extending AI Security Visibility with Darktrace and Microsoft Agent 365
AI agents are rapidly becoming embedded in everyday business operations, helping employees automate workflows, access information, and accelerate decision-making. As organizations embrace agentic AI, security teams face a growing challenge: understanding how AI is being used, what agents can access, and where risk may emerge.
As agents take on more business-critical work, security teams often need to move across multiple portals to understand risks spanning identity, data, and threat activity. This fragmented view can make it difficult to assess an agent's overall risk posture and determine where attention is needed. Organizations need a way to bring these signals together without disrupting existing security investments or workflows.
Today, Darktrace is announcing an integration between Darktrace / SECURE AI and Microsoft Agent 365 that brings Darktrace's Adaptive AI-driven risk signals directly into the Microsoft 365 Admin Center. By extending the visibility and risk understanding provided by Darktrace / SECURE AI into the Microsoft ecosystem, organizations can gain a more unified understanding of AI agent risk across their environments.
As one of the first security companies to partner with Microsoft to contribute third-party risk signals to the Agent Registry, Darktrace is helping shape how organizations understand and manage AI agent risk.
Extending visibility into the Microsoft Agent 365 experience
Microsoft Agent 365 provides administrators with a centralized registry of AI agents operating within their environment. As organizations expand their use of AI agents, this centralized visibility becomes increasingly important for governance and oversight.
This new integration extends that visibility by allowing Darktrace-generated risk signals to be surfaced directly within the Microsoft Agent 365 experience. By combining Microsoft's agent management and security capabilities with Darktrace's AI-powered risk analysis, organizations gain greater awareness of potential security concerns associated with AI agents operating across their environments.
By integrating Darktrace telemetry into Agent 365, customers can:
Surface Darktrace-detected risks and signals alongside Microsoft-native signals in a single interface
Identify potentially compromised or anomalous AI agents more quickly
Gain unified understanding of context and agent behavior
This approach reinforces a single control plane for AI security while allowing organizations to continue leveraging existing investments across both platforms.
Why unified visibility of AI agent risk signals matters
As AI adoption accelerates across Microsoft environments, organizations must manage new forms of behavior, access patterns, and risk. Security teams need more than inventories and permissions. They need visibility into how AI systems operate and how risk evolves over time.
This integration addresses a critical gap: how to bring behavioral AI security insights into the same control plane as identity, access, and agent management.
With Darktrace and Microsoft Agent 365 together, organizations benefit from:
Unified visibility: A single pane of glass for understanding AI agent risk signals across Microsoft and Darktrace signals
Faster detection of abnormal agent behavior: Darktrace's Adaptive AI highlights deviations that may not be captured by static controls
Operational efficiency: Security teams can triage and prioritize risk signals without switching between systems
Stronger trust in AI deployments: Clear attribution, context, and investigation pathways improve confidence in AI agent usage
Extending Microsoft's AI security model, not replacing it
Securing AI requires a layered approach that combines governance, visibility, threat detection, and risk management. This integration is designed to complement Microsoft's security capabilities, not duplicate them.
Through Darktrace / SECURE AI, Darktrace contributes:
Identification of risk via advanced prompt analysis
Behavioral anomaly detection across AI agents
Cross-environment threat correlation
Autonomous insight into emerging or unknown risks
Microsoft provides:
Centralized agent management
Identity and access governance
Native detection of risk signals and enforcement capabilities
Together, these capabilities create a more complete, layered approach to securing AI-driven enterprises. Organizations gain the governance and policy controls needed to manage AI adoption while benefiting from continuous visibility into how AI is used across the business.
Building the future of secure AI
As AI agents become more deeply embedded in business processes, organizations need more than inventories and static controls. They need to understand how AI is being used, how agents behave, and where risk is emerging across the enterprise.
Darktrace / SECURE AI delivers that understanding through continuous visibility into AI activity, helping security teams assess intent, identify behavioral drift, and uncover emerging risk across both human and agent-driven workflows. Powered by Adaptive AI, it provides the context needed to secure AI as it evolves.
The integration with Microsoft Agent 365 extends those insights into the workflows organizations already use. Agent 365 provides a unified control plane for governing and securing AI agents, while Darktrace contributes complementary behavioral risk signals that can be surfaced within the Agent 365 experience. Together, they give customers broader context on agent activity and risk while preserving the value of their existing Microsoft and Darktrace investments.
As enterprises move from AI experimentation to AI-powered execution, Microsoft and Darktrace help bring together governance, compliance, behavioral understanding, and oversight in a unified approach to AI security. For organizations adopting Microsoft 365 E7, Darktrace / SECURE AI further strengthens that foundation by providing continuous visibility into AI activity, agent behavior, and emerging risk as AI adoption scales.
