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February 29, 2024

Navigating Email Security Gaps Beyond DMARC

Discover Darktrace's strategies for navigating email security gaps beyond DMARC. Enhance your defenses with advanced threat detection and mitigation.
Inside the SOC
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
Carlos Gray
Senior Product Marketing Manager, Email
Written by
Stephen Pickman
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29
Feb 2024

Email threat landscape  

Email has consistently ranked among the most targeted attack vectors, given its ubiquity and criticality to business operations. From September to December 2023, 10.4 million phishing emails were detected across Darktrace’s customer fleet demonstrating the frequency of attempted email-based attacks.

Businesses are searching for ways to harden their email security posture alongside email providers who are aiming to reduce malicious emails traversing their infrastructure, affecting their clients. Domain-based Message Authentication (DMARC) is a useful industry-wide protocol organizations can leverage to move towards these goals.  

What is DMARC?

DMARC is an email authentication protocol designed to enhance the security of email communication.

Major email service providers Google and Yahoo recently made the protocol mandatory for bulk senders in an effort to make inboxes safer worldwide. The new requirements demonstrate an increasing need for a standardized solution as misconfigured or nonexistent authentication systems continue to allow threat actors to evade detection and leverage the legitimate reputation of third parties.  

DMARC is a powerful tool that allows email administrators to confidently identify and stop certain spoofed emails; however, more organizations must implement the standard for it to reach its full potential. The success and effectiveness of DMARC is dependent on broad adoption of the standard – by organizations of all sizes.  

How does DMARC work?

DMARC builds on two key authentication technologies, Sender Policy Framework (SPF) and DomainKeys Identified Mail (DKIM) and helps to significantly improve their ability to prevent domain spoofing. SPF verifies that a sender’s IP address is authorized to send emails on behalf of a particular domain and DKIM ensures integrity of email content by providing a verifiable digital signature.  

DMARC adds to this by allowing domain owners to publish policies that set expectations for how SPF and DKIM verification checks relate to email addresses presented to users and whose authenticity the receiving mail server is looking to establish.  

These policies work in tandem to help authenticate email senders by verifying the emails are from the domain they say they are, working to prevent domain spoofing attacks. Key benefits of DMARC include:

  1. Phishing protection DMARC protects against direct domain spoofing in which a threat actor impersonates a legitimate domain, a common phishing technique threat actors use to trick employees to obtain sensitive information such as privileged credentials, bank information, etc.  
  2. Improving brand reputation: As DMARC helps to prevent impersonation of domains, it stands to maintain and increase an organization’s brand reputation. Additionally, as organizational reputation improves, so will the deliverability of emails.
  3. Increased visibility: DMARC provides enhanced visibility into email communication channels, including reports of all emails sent on behalf of your domain. This allows security teams to identify shadow-IT and any unauthorized parties using their domain.

Understanding DMARC’s Limitations

DMARC is often positioned as a way for organizations to ‘solve’ their email security problems, however, 65% of the phishing emails observed by Darktrace successfully passed DMARC verification, indicating that a significant number of threat actors are capable of manipulating email security and authentication systems in their exploits. While DMARC is a valuable tool in the fight against email-based attacks, the evolving threat landscape demands a closer look at its limitations.  

As threat actors continue to innovate, improving their stealth and evasion tactics, the number of attacks with valid DMARC authentication will only continue to increase in volume and sophistication. These can include:

  1. Phishing attacks that leverage non-spoofed domains: DMARC allows an organization to protect the domains that they own, preventing threat actors from being able to send phishing emails from their domains. However, threat actors will often create and use ‘look-a-like’ domains that closely resemble an organization’s domain to dupe users. 3% of the phishing emails identified by Darktrace utilized newly created domains, demonstrating shifting tactics.  
  2. Email Account Takeovers: If a threat actor gains access to a user’s email account through other social engineering means such as credential stuffing, they can then send phishing emails from the legitimate domain to pursue further attacks. Even though these emails are malicious, DMARC would not identify them as such because they are coming from an authorized domain or sender.  

Organizations must also ensure their inbound analysis of emails is not skewed by successful DMARC authentication. Security teams cannot inherently trust emails that pass DMARC, because the source cannot always be legitimized, like in the event of an account takeover. If a threat actor gains access to an authenticated email account, emails sent by the threat actor from that account will pass DMARC – however the contents of that email may be malicious. Sender behavior must be continuously evaluated and vetted in real time as past communication history and validated DMARC cannot be solely relied upon amid an ever-changing threat landscape.  

Security teams should lean on other security measures, such as anomaly detection tools that can identify suspicious emails without relying on historical attack rules and static data. While DMARC is not a silver bullet for email security, it is nevertheless foundational in helping organizations protect their brand identity and must be viewed as an essential layer in an organization's overall cyber security strategy.  

Implementing DMARC

Despite the criticality of DMARC for preserving brand reputation and trust, adoption of the standard has been inconsistent. DMARC can be complex to implement with many organizations lacking the time required to understand and successfully implement the standard. Because of this, DMARC set-up is often outsourced, giving security and infrastructure teams little to no visibility into or control of the process.  

Implementation of DMARC is only the start of this process, as DMARC reports must be consistently monitored to ensure organizations have visibility into who is sending mail from their domain, the volume of mail being sent and whether the mail is passing authentication protocols. This process can be time consuming for security teams who are already faced with mounting responsibilities, tight budgets, and personnel shortages. These complexities unfortunately delay organizations from using DMARC – especially as many today still view it as a ‘nice to have’ rather than an essential.  

With the potential complexities of the DMARC implementation process, there are many ways security and infrastructure teams can still successfully roll out the standard. Initial implementation should start with monitoring, policy adjustment and then enforcement. As business changes over time, DMARC should be reviewed regularly to ensure ongoing protection and maintain domain reputation.

The Future of Email Security

As email-based attacks continue to rise, the industry must recognize the importance of driving adoption of foundational email authentication protocols. To do this, a new and innovative approach to DMARC is needed. DMARC products must evolve to better support organizations throughout the ongoing DMARC monitoring process, rather than just initial implementation. These products must also be able to share intelligence across an organization’s security stack, extending beyond email security tools. Integration across these products and tools will help organizations optimize their posture, ensuring deep understanding of their domain and increased visibility across the entire enterprise.

DMARC is critical in protecting brand identity and mitigating exact-domain based attacks. However, organizations must understand DMARC’s unique benefits and limitations to ensure their inboxes are fully protected. In today’s evolving threat landscape, organizations require a robust, multi-layered approach to stop email threats – in inbound mail and beyond. Email threats have evolved – its time security does too.

Join Darktrace on 9 April for a virtual event to explore the latest innovations needed to get ahead of the rapidly evolving threat landscape. Register today to hear more about our latest innovations coming to Darktrace’s offerings. For additional insights check out Darktrace’s 2023 End of Year Threat Report.

Credit to Carlos Gray and Stephen Pickman for their contribution to this blog

Inside the SOC
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
Carlos Gray
Senior Product Marketing Manager, Email
Written by
Stephen Pickman

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December 23, 2025

How to Secure AI in the Enterprise: A Practical Framework for Models, Data, and Agents

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Introduction: Why securing AI is now a security priority

AI adoption is at the forefront of the digital movement in businesses, outpacing the rate at which IT and security professionals can set up governance models and security parameters. Adopting Generative AI chatbots, autonomous agents, and AI-enabled SaaS tools promises efficiency and speed but also introduces new forms of risk that traditional security controls were never designed to manage. For many organizations, the first challenge is not whether AI should be secured, but what “securing AI” actually means in practice. Is it about protecting models? Governing data? Monitoring outputs? Or controlling how AI agents behave once deployed?  

While demand for adoption increases, securing AI use in the enterprise is still an abstract concept to many and operationalizing its use goes far beyond just having visibility. Practitioners need to also consider how AI is sourced, built, deployed, used, and governed across the enterprise.

The goal for security teams: Implement a clear, lifecycle-based AI security framework. This blog will demonstrate the variety of AI use cases that should be considered when developing this framework and how to frame this conversation to non-technical audiences.  

What does “securing AI” actually mean?

Securing AI is often framed as an extension of existing security disciplines. In practice, this assumption can cause confusion.

Traditional security functions are built around relatively stable boundaries. Application security focuses on code and logic. Cloud security governs infrastructure and identity. Data security protects sensitive information at rest and in motion. Identity security controls who can access systems and services. Each function has clear ownership, established tooling, and well-understood failure modes.

AI does not fit neatly into any of these categories. An AI system is simultaneously:

  • An application that executes logic
  • A data processor that ingests and generates sensitive information
  • A decision-making layer that influences or automates actions
  • A dynamic system that changes behavior over time

As a result, the security risks introduced by AI cuts across multiple domains at once. A single AI interaction can involve identity misuse, data exposure, application logic abuse, and supply chain risk all within the same workflow. This is where the traditional lines between security functions begin to blur.

For example, a malicious prompt submitted by an authorized user is not a classic identity breach, yet it can trigger data leakage or unauthorized actions. An AI agent calling an external service may appear as legitimate application behavior, even as it violates data sovereignty or compliance requirements. AI-generated code may pass standard development checks while introducing subtle vulnerabilities or compromised dependencies.

In each case, no single security team “owns” the risk outright.

This is why securing AI cannot be reduced to model safety, governance policies, or perimeter controls alone. It requires a shared security lens that spans development, operations, data handling, and user interaction. Securing AI means understanding not just whether systems are accessed securely, but whether they are being used, trained, and allowed to act in ways that align with business intent and risk tolerance.

At its core, securing AI is about restoring clarity in environments where accountability can quickly blur. It is about knowing where AI exists, how it behaves, what it is allowed to do, and how its decisions affect the wider enterprise. Without this clarity, AI becomes a force multiplier for both productivity and risk.

The five categories of AI risk in the enterprise

A practical way to approach AI security is to organize risk around how AI is used and where it operates. The framework below defines five categories of AI risk, each aligned to a distinct layer of the enterprise AI ecosystem  

How to Secure AI in the Enterprise:

  • Defending against misuse and emergent behaviors
  • Monitoring and controlling AI in operation
  • Protecting AI development and infrastructure
  • Securing the AI supply chain
  • Strengthening readiness and oversight

Together, these categories provide a structured lens for understanding how AI risk manifests and where security teams should focus their efforts.

1. Defending against misuse and emergent AI behaviors

Generative AI systems and agents can be manipulated in ways that bypass traditional controls. Even when access is authorized, AI can be misused, repurposed, or influenced through carefully crafted prompts and interactions.

Key risks include:

  • Malicious prompt injection designed to coerce unwanted actions
  • Unauthorized or unintended use cases that bypass guardrails
  • Exposure of sensitive data through prompt histories
  • Hallucinated or malicious outputs that influence human behavior

Unlike traditional applications, AI systems can produce harmful outcomes without being explicitly compromised. Securing this layer requires monitoring intent, not just access. Security teams need visibility into how AI systems are being prompted, how outputs are consumed, and whether usage aligns with approved business purposes

2. Monitoring and controlling AI in operation

Once deployed, AI agents operate at machine speed and scale. They can initiate actions, exchange data, and interact with other systems with little human oversight. This makes runtime visibility critical.

Operational AI risks include:

  • Agents using permissions in unintended ways
  • Uncontrolled outbound connections to external services or agents
  • Loss of forensic visibility into ephemeral AI components
  • Non-compliant data transmission across jurisdictions

Securing AI in operation requires real-time monitoring of agent behavior, centralized control points such as AI gateways, and the ability to capture agent state for investigation. Without these capabilities, security teams may be blind to how AI systems behave once live, particularly in cloud-native or regulated environments.

3. Protecting AI development and infrastructure

Many AI risks are introduced long before deployment. Development pipelines, infrastructure configurations, and architectural decisions all influence the security posture of AI systems.

Common risks include:

  • Misconfigured permissions and guardrails
  • Insecure or overly complex agent architectures
  • Infrastructure-as-Code introducing silent misconfigurations
  • Vulnerabilities in AI-generated code and dependencies

AI-generated code adds a new dimension of risk, as hallucinated packages or insecure logic may be harder to detect and debug than human-written code. Securing AI development means applying security controls early, including static analysis, architectural review, and continuous configuration monitoring throughout the build process.

4. Securing the AI supply chain

AI supply chains are often opaque. Models, datasets, dependencies, and services may come from third parties with varying levels of transparency and assurance.

Key supply chain risks include:

  • Shadow AI tools used outside approved controls
  • External AI agents granted internal access
  • Suppliers applying AI to enterprise data without disclosure
  • Compromised models, training data, or dependencies

Securing the AI supply chain requires discovering where AI is used, validating the provenance and licensing of models and data, and assessing how suppliers process and protect enterprise information. Without this visibility, organizations risk data leakage, regulatory exposure, and downstream compromise through trusted integrations.

5. Strengthening readiness and oversight

Even with strong technical controls, AI security fails without governance, testing, and trained teams. AI introduces new incident scenarios that many security teams are not yet prepared to handle.

Oversight risks include:

  • Lack of meaningful AI risk reporting
  • Untested AI systems in production
  • Security teams untrained in AI-specific threats

Organizations need AI-aware reporting, red and purple team exercises that include AI systems, and ongoing training to build operational readiness. These capabilities ensure AI risks are understood, tested, and continuously improved, rather than discovered during a live incident.

Reframing AI security for the boardroom

AI security is not just a technical issue. It is a trust, accountability, and resilience issue. Boards want assurance that AI-driven decisions are reliable, explainable, and protected from tampering.

Effective communication with leadership focuses on:

  • Trust: confidence in data integrity, model behavior, and outputs
  • Accountability: clear ownership across teams and suppliers
  • Resilience: the ability to operate, audit, and adapt under attack or regulation

Mapping AI security efforts to recognized frameworks such as ISO/IEC 42001 and the NIST AI Risk Management Framework helps demonstrate maturity and aligns AI security with broader governance objectives.

Conclusion: Securing AI is a lifecycle challenge

The same characteristics that make AI transformative also make it difficult to secure. AI systems blur traditional boundaries between software, users, and decision-making, expanding the attack surface in subtle but significant ways.

Securing AI requires restoring clarity. Knowing where AI exists, how it behaves, who controls it, and how it is governed. A framework-based approach allows organizations to innovate with AI while maintaining trust, accountability, and control.

The journey to secure AI is ongoing, but it begins with understanding the risks across the full AI lifecycle and building security practices that evolve alongside the technology.

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About the author
Brittany Woodsmall
Product Marketing Manager, AI & Attack Surface

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December 22, 2025

The Year Ahead: AI Cybersecurity Trends to Watch in 2026

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Introduction: 2026 cyber trends

Each year, we ask some of our experts to step back from the day-to-day pace of incidents, vulnerabilities, and headlines to reflect on the forces reshaping the threat landscape. The goal is simple:  to identify and share the trends we believe will matter most in the year ahead, based on the real-world challenges our customers are facing, the technology and issues our R&D teams are exploring, and our observations of how both attackers and defenders are adapting.  

In 2025, we saw generative AI and early agentic systems moving from limited pilots into more widespread adoption across enterprises. Generative AI tools became embedded in SaaS products and enterprise workflows we rely on every day, AI agents gained more access to data and systems, and we saw glimpses of how threat actors can manipulate commercial AI models for attacks. At the same time, expanding cloud and SaaS ecosystems and the increasing use of automation continued to stretch traditional security assumptions.

Looking ahead to 2026, we’re already seeing the security of AI models, agents, and the identities that power them becoming a key point of tension – and opportunity -- for both attackers and defenders. Long-standing challenges and risks such as identity, trust, data integrity, and human decision-making will not disappear, but AI and automation will increase the speed and scale of the cyber risk.  

Here's what a few of our experts believe are the trends that will shape this next phase of cybersecurity, and the realities organizations should prepare for.  

Agentic AI is the next big insider risk

In 2026, organizations may experience their first large-scale security incidents driven by agentic AI behaving in unintended ways—not necessarily due to malicious intent, but because of how easily agents can be influenced. AI agents are designed to be helpful, lack judgment, and operate without understanding context or consequence. This makes them highly efficient—and highly pliable. Unlike human insiders, agentic systems do not need to be socially engineered, coerced, or bribed. They only need to be prompted creatively, misinterpret legitimate prompts, or be vulnerable to indirect prompt injection. Without strong controls around access, scope, and behavior, agents may over-share data, misroute communications, or take actions that introduce real business risk. Securing AI adoption will increasingly depend on treating agents as first-class identities—monitored, constrained, and evaluated based on behavior, not intent.

-- Nicole Carignan, SVP of Security & AI Strategy

Prompt Injection moves from theory to front-page breach

We’ll see the first major story of an indirect prompt injection attack against companies adopting AI either through an accessible chatbot or an agentic system ingesting a hidden prompt. In practice, this may result in unauthorized data exposure or unintended malicious behavior by AI systems, such as over-sharing information, misrouting communications, or acting outside their intended scope. Recent attention on this risk—particularly in the context of AI-powered browsers and additional safety layers being introduced to guide agent behavior—highlights a growing industry awareness of the challenge.  

-- Collin Chapleau, Senior Director of Security & AI Strategy

Humans are even more outpaced, but not broken

When it comes to cyber, people aren’t failing; the system is moving faster than they can. Attackers exploit the gap between human judgment and machine-speed operations. The rise of deepfakes and emotion-driven scams that we’ve seen in the last few years reduce our ability to spot the familiar human cues we’ve been taught to look out for. Fraud now spans social platforms, encrypted chat, and instant payments in minutes. Expecting humans to be the last line of defense is unrealistic.

Defense must assume human fallibility and design accordingly. Automated provenance checks, cryptographic signatures, and dual-channel verification should precede human judgment. Training still matters, but it cannot close the gap alone. In the year ahead, we need to see more of a focus on partnership: systems that absorb risk so humans make decisions in context, not under pressure.

-- Margaret Cunningham, VP of Security & AI Strategy

AI removes the attacker bottleneck—smaller organizations feel the impact

One factor that is currently preventing more companies from breaches is a bottleneck on the attacker side: there’s not enough human hacker capital. The number of human hands on a keyboard is a rate-determining factor in the threat landscape. Further advancements of AI and automation will continue to open that bottleneck. We are already seeing that. The ostrich approach of hoping that one’s own company is too obscure to be noticed by attackers will no longer work as attacker capacity increases.  

-- Max Heinemeyer, Global Field CISO

SaaS platforms become the preferred supply chain target

Attackers have learned a simple lesson: compromising SaaS platforms can have big payouts. As a result, we’ll see more targeting of commercial off-the-shelf SaaS providers, which are often highly trusted and deeply integrated into business environments. Some of these attacks may involve software with unfamiliar brand names, but their downstream impact will be significant. In 2026, expect more breaches where attackers leverage valid credentials, APIs, or misconfigurations to bypass traditional defenses entirely.

-- Nathaniel Jones, VP of Security & AI Strategy

Increased commercialization of generative AI and AI assistants in cyber attacks

One trend we’re watching closely for 2026 is the commercialization of AI-assisted cybercrime. For example, cybercrime prompt playbooks sold on the dark web—essentially copy-and-paste frameworks that show attackers how to misuse or jailbreak AI models. It’s an evolution of what we saw in 2025, where AI lowered the barrier to entry. In 2026, those techniques become productized, scalable, and much easier to reuse.  

-- Toby Lewis, Global Head of Threat Analysis

Conclusion

Taken together, these trends underscore that the core challenges of cybersecurity are not changing dramatically -- identity, trust, data, and human decision-making still sit at the core of most incidents. What is changing quickly is the environment in which these challenges play out. AI and automation are accelerating everything: how quickly attackers can scale, how widely risk is distributed, and how easily unintended behavior can create real impact. And as technology like cloud services and SaaS platforms become even more deeply integrated into businesses, the potential attack surface continues to expand.  

Predictions are not guarantees. But the patterns emerging today suggest that 2026 will be a year where securing AI becomes inseparable from securing the business itself. The organizations that prepare now—by understanding how AI is used, how it behaves, and how it can be misused—will be best positioned to adopt these technologies with confidence in the year ahead.

Learn more about how to secure AI adoption in the enterprise without compromise by registering to join our live launch webinar on February 3, 2026.  

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