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January 14, 2025

Why AI-powered Email Protection Became Essential for this Global Financial Services Leader

Hear the cybersecurity transformation story of this leading money transmitter, who facilitates more than $9 billion in remittances via thousands of agent locations across the US serving more than two million active customers.
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.
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14
Jan 2025

When agile cyber-attackers don’t stop, but pivot  

When he first joined this leading financial services provider, it was clear to the CISO that email security needed to be a top priority. The organization provides transfer services to millions of consumers via a network of thousands of agent locations across the US. Those agents are connected to hundreds of thousands of global payers to complete consumer transfers, ranging from leading financial institutions to small local businesses.

With this vast network of agents and payers, the provider relies on email as its primary communications channel. Transmitting billions of dollars every year, the organization is a prime target for cyber criminals looking to steal credentials, financial assets, and sensitive data.

Vulnerable to attacks with gaps in email security and visibility

The CISO discovered that employees were under constant attack by phishing emails impersonating his company’s own executives. The business email compromise (BEC) attacks were designed to deceive employees into sharing credentials or clicking on malicious links.

Upon discovering that their Microsoft 365 tenant lacked secure configuration, the CISO implemented necessary changes to strengthen the service, including enabling authentication controls. While his efforts significantly reduced BEC attacks, cyber criminals changed their tactics, sending employees malicious phishing emails from seemingly valid email accounts from trusted domains like Google and Yahoo. The emails passed through the organization’s native email filters without detection.

The CISO also sought to strengthen defenses against third-party supply chain attacks that could originate with any of the hundreds of thousands of third-party agents and payers the company works with around the world. While the larger institutions typically have sophisticated email security strategies in place, the smaller businesses may lack the cybersecurity expertise needed to effectively secure and manage their data, putting the organization at risk.

While the CISO knew the company was vulnerable to phishing and third-party threats, he didn’t have visibility across the flow of email. Without access to key metrics and valuable data, he couldn’t get the crucial insights needed to quickly identify possible threats and adjust security protocols.  

Skilled analysts bogged down with low-level tasks

Like many enterprise organizations, this leading financial services provider relied on a crew of highly skilled analysts to respond to alerts and analyze and triage emails most of their workday. “That shouldn’t be how we operate,” said the CISO. “My role and the role of my staff should be to focus on more strategic projects, support the business, and work on important new product development.”

Balancing user experience with mitigating threats

Enabling greater email security measures without negatively impacting the business, user experience, and customer satisfaction was a daunting challenge the CISO and his security team faced. Imposing restrictions that are too stringent could restrict communication, delay the delivery of important messages, or block legitimate emails – potentially slowing down money transfers, frustrating customers, affecting employee productivity, and impacting revenue. However, maintaining controls that are too permissive could result in serious outcomes like data theft, financial fraud, operational disruption, compliance penalties, and customer attrition.  

Self-Learning AI is a game changer

After conducting a thorough POC with several modern security solution providers, this global financial services provider chose the Darktrace / EMAIL an AI-driven email security platform. The CISO said they chose the solution for two key reasons:

First, Darktrace / EMAIL offers modern capabilities

  • Self-Learning AI uses business data to recognize anomalies in communication patterns and user behavior to stop known and unknown threats
  • Secures the organization’s entire mailflow across all inbound, outbound, and lateral email
  • Protects against account takeover attacks by identifying subtle anomalies in cloud SaaS
  • Catches sophisticated threats like impersonations, session token misuse, adversary-in-the-middle attacks, credential theft, and data exfiltration

Second, they pointed to Darktrace’s experience, innovation, and expertise

  • Deep cybersecurity and industry knowledge
  • Demonstrated customers successes worldwide
  • At the forefront of innovation and research, establishing new thresholds in cybersecurity, with technology advances backed by over 200 patents and pending applications

Moreover, and most importantly, this organization trusted Darktrace to deliver on its promises.  And according to the CISO, that’s just what happened.

Significantly reduced phishing threats and business risk

Since implementing Darktrace / EMAIL, the threat posed by BEC attacks has dropped sharply. “Phishing is not an issue that concerns me anymore. I estimate we are now identifying and blocking more than 85% of threats our previous solution was missing,” said the CISO. The biggest factor contributing to this success? The power of AI.

With Darktrace / EMAIL, this leadingglobal financial services provider is identifying and blocking more than 85% ofthe phishing email threats its previous solution missed.

AI wasn’t originally on the financial service provider’s list of criteria. But after seeing AI in action and understanding its potential to vastly scale their detection and response capabilities–without adding headcount, the CISO determined AI wasn’t an option but an imperative. “AI is essential when it comes to email security, it’s an absolute necessity,” he said.  

Darktrace / EMAIL’s Self-Learning AI is uniquely powerful because it learns the content and context of every internal and external user and can spot the subtle differences in behavioral patterns that point to possible social engineering attacks. Through patented behavioral anomaly detection, Darktrace / EMAIL continuously learns about the organization’s business and users, based on its own operations and data, adjusting security protocols accordingly.  

For example, when clients are transferring large amounts of money, they are required to send photos of their driver’s licenses and passports via email to the organization for verification – accounting for a large percentage of its’ inbound email. Darktrace / EMAIL recognizes that it’s normal for customers to send this sensitive information, and it also knows that it’s not normal for that same sensitive information to leave the organization via outbound mail. In addition, Darktrace identifies patterns in user behavior, including who employees communicate with and what kind of information they share. When user behavior falls outside of established norms, such as an email sent from the CFO to employees the CEO would not typically communicate with, Darktrace can take the appropriate action to remove the threat.  

“After the implementation, we gave the solution two weeks to ingest our data and learn the specifics of our business. After that, it was perfect, just amazing,” said the CISO.  

Boosted team productivity and elevated value to the business

With Darktrace / EMAIL, the organization has successfully scaled its detection and response efforts without scaling personnel. The security team has reduced the number of emails requiring manual investigation by 90%. And because analysts now have the benefit of Darktrace / EMAIL’s analytics and reporting, the investigation process is much easier and faster. “The impact of this solution on my team has been very positive,” said the CISO. “Darktrace / EMAIL essentially manages itself, freeing up time for our skilled analysts–and for myself–to focus on more important projects.”  

The security team has scaled its detection and response efforts without scaling personnel,reducing the number of emails it manually investigates by 90%

Increased visibility delivers business-critical insights

You can’t control what you can’t see, and with zero visibility into critical data and metrics, this financial services provider was at a serious disadvantage. That has all changed. “Something that I love about Darktrace / EMAIL is the visibility that it provides into key metrics from a single dashboard. We can now understand the behavior of our email flow and data traffic and can make insight-driven decisions to continuously optimize our email security. It’s awesome,” said the CISO.  

An efficient user interface also improves productivity and reduces mean time to action by enabling teams to easily visualize key data points and quickly evaluate what actions need to be taken. Darktrace / EMAIL was developed with that experience in mind, allowing users to access data and take quick action without having to constantly log into the solution.

Keeping the business focused on cybersecurity

The leadership of this global organization takes information security very seriously, understanding that cyber-attacks aren’t just an IT problem but a business problem. When it came to evaluating Darktrace, the CISO said numerous stakeholders were involved including C-level executives, infrastructure, and IT, which operates separately from information security. The CISO initially identified the need, conducted the market research, engaged the target vendors, and then brought the other decision makers into the process for the solution evaluation and final decision. “Our IT group, infrastructure team, CTO and CEO are all involved when it comes to making major cybersecurity investments. We always try to make these decisions jointly to ensure we are taking everything into consideration.”

The organization has reached a higher level of maturity when it comes to email cybersecurity. The ability to automate routine email detection and investigation tasks has both strengthened the organization’s cyber resilience and enabled the CISO and his team to contribute more to the business. His advice for other IT leaders facing the same email security and visibility challenges he once experienced: “For those companies that need greater insight and control over their email but have limited resources and people, AI is the answer.”  

Darktrace / Email solution brief screenshot

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  • Gain up to 13 days of earlier threat detection and maximize ROI on your current email security
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  • Stop the 58% of threats bypassing traditional email security

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
The Darktrace Community

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July 7, 2026

Securing AI: Analysis of the Complete Security Stack with Governance and Controls

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Why traditional cybersecurity approaches are not enough for AI

AI adoption outpaces most security programs’ ability to adapt.  That gap is now one of the most consequential sources of cyber risk facing enterprises. As organizations embed generative and agentic AI into development workflows, business operations, and security tooling itself, the question is no longer whether AI will introduce risk. The question is whether organizations understand where that risk actually lives and how to manage it operationally.  

Two recent pieces of guidance underscore this shift:

  1. The upcoming Cybersecurity Framework Profile for AI from NIST
  1. The Five Eyes government guidance on the careful adoption of agentic AI services

Taken together, they point to a critical conclusion. AI security cannot be reduced to model hardening or prompt filtering. It requires a defense in depth strategy that treats AI as both a new attack surface and a force multiplier for defense, while accounting for how AI fundamentally changes scale, speed, and autonomy.  

Recent threat research suggests that today's cyber risk is driven less by initial compromise and more by an adversary's ability to blend into normal operations over time. AI systems create the same exposure in a new form: more autonomy, more scale, and more opportunities for risky behavior to blend into normal operations.

How NIST defines the three core pillars of AI security

The NIST profile organizes AI risk across three inseparable focus areas that span all cybersecurity functions, Secure, Defend and Thwart. These areas are not sequential. They exist simultaneously and must be addressed together.

Secure

This treats AI as an attack surface. It includes models, prompts, agents, pipelines, training and inference data, retrieval augmented generation corpora, and the AI supply chain itself. AI systems are opaque, probabilistic, and non-deterministic by design. Some vulnerabilities are inherent in how models are trained or how data is sourced. Traditional patching does not fully mitigate these risks. This is also where many enterprises are weakest today and, critically, where many security programs stop.  

Defend

This is AI as a defensive force multiplier. AI can improve detection speed, scale, correlation, and response, but only if the right models are used and operationalized correctly. Machine-speed behavior-based detection, response and containment becomes critical in defending non-deterministic systems. Accuracy, explainability, governance, testing, validation, and integration into SOC workflows matter as much as capability. Without those controls, hallucination risk, over automation, and misplaced trust become security risks themselves.  

Thwart

This treats AI as an adversarial accelerant. Threat actors are already using AI to generate targeted social engineering attacks, deepfakes, malware, and autonomous attack agents. Asymmetric warfare is highlighting faster vulnerability discovery and exploitation with a lag on patch development, testing and deployment.  

How this looks in practice

Darktrace researchers observed scaled, automated exploitation of the React2Shell vulnerability within days of disclosure. A vulnerable cloud asset was exploited in under 120 seconds of being deployed. Darktrace research team observed an AI/LLM-generated malware sample used in exploitation activity tied to React2Shell. The significance isn't novelty. It is that AI lowers the barrier to producing usable offensive tooling and compresses the time between experimentation and deployment.  

Tactics are getting more and more creative in order to string together steps of an attack kill chain. This creates a dependency on behavior-based detection, autonomous investigation, autonomous containment, training, resilience investment, and recovery planning across the entire enterprise.

Why agentic AI fundamentally changes enterprise cyber risk

The Five Eyes guidance on agentic AI highlights material changes to the cyber risk profile of an organization. Unlike generative AI systems that produce content for human consumption, agentic AI systems reason, plan, and act autonomously across tools, data, and environments. That autonomy, combined with access to real systems, amplifies the impact of traditional cyber failures and introduces new system level risks that are difficult to predict, observe, and contain.  

Risk in agentic systems does not live in the model alone. It emerges from interactions between models, prompts, memory, tools, APIs, identities, privileges, inter-agent trust relationships, and human assumptions baked into design. Vulnerabilities are often introduced through data, connectors, natural language interfaces, protocols, and drift by design.

In supply-chain incidents, attackers did not need sophisticated exploits to scale impact. They abused trusted systems built for automation and implicit access. Agentic AI inherits that model. Once a system can act across tools, data, and workflows, compromise propagates through trust relationships that were never designed for machine autonomy.

The major agentic AI risk classes include the following:  

  • The identity control for non-human identities or autonomous agents makes it difficult to mitigate over-permissioning, limiting access, scope, and duration, as well as access hygiene
  • Agents are frequently over permissioned
  • Compromised tools inherit agent authority
  • Static secrets enable impersonation
  • Implicit trust between agents enables lateral movement

Design and configuration risks compound this, including privileges evaluated once at startup, poor segmentation, unvetted third party tools, reused authorization decisions outside their original context, and guardrail limitations.  

Behavioral risk  

Agents can optimize for goals in unsafe ways, misinterpret ambiguous intent, chain actions into unintended sequences, change behavior during evaluation, and exhibit deceptive or sycophantic responses.  

Structural risk  

Structural risk follows from agentic systems that are tightly coupled, multicomponent ecosystems. Failures can propagate across agents. Hallucinations cascade downstream. Resource exhaustion becomes systemic. Tool misuse enables indirect prompt injection and command execution. Rogue agents can poison peer agents through trust relationships.  

Accountability

Accountability becomes unclear as autonomy increases. Autonomous agents assume human identity permissions, and humans should have clear ownership of these agents, but they don’t, and this model is flawed. Decision paths are opaque and non-deterministic. Logs are fragmented and difficult to interpret. Reproducing an incident will be impossible without explicit design for observability and forensics. An agent compromise is functionally an insider threat, often with better access and fewer behavioral constraints than a human.  

What does defense in depth look like for AI?

Agentic AI runs on software, networks, identities, and data. It must be governed using the same foundational principles that have proven resilient under uncertainty, including secure by design, defense in depth, zero trust, least privilege, continuous monitoring, behavior-based advanced threat detection and containment, and incident response and recovery.

Core components to a Defense in depth Strategy for Securing the use of AI:

  • Strong, precise identity control plane to include an identity per agent (cryptographic, non‑shared)
    • Privilege monitoring and just‑in‑time access
  • Data Governance
  • Secure‑by‑default configurations
    • Security Posture Management  
    • Zero Trust principles  
  • Strong guardrails, deny‑by‑default policies, and isolation
  • Explicit instruction hierarchies and controlled context
  • Behavioral-based detection across entire enterprise to include inputs, tools, and outputs as well as AI used on the endpoint, across the network, cloud, SaaS, email, and OT
    • Runtime anomaly detection and goal‑drift detection
    • Autonomous containment to mitigate risk and minimize damage
  • Hard boundaries on autonomy and delegation
  • Testing, Evaluation, Validation and Verification  
    • Determine when autonomous action and when human in the loop
    • Adversarial training and agent‑specific testing
    • Simulation, red teaming, and chaos testing
  • Kill‑switches, rollback, and containment mechanisms
    • Forensics data captures, interpretability, autonomous containment, and remediation/recovery plans  

Until standards, tooling, and assurance methods mature, organizations should assume agentic AI systems will behave unexpectedly and design deployments around resilience, behavior-based detection, reversibility, and containment, not efficiency.

How security leaders should prepare for enterprise AI adoption

AI security is not model security alone. Data, pipelines, identities, and agents are first class assets. Many AI attacks succeed through standard cyber failures amplified by AI. Identity, data, and supply chain risk dominate. Behavior-based detection and response are critical, not optional. Logging, provenance, versioning, and forensics data capture of detections are mandatory because you cannot investigate or recover from AI incidents without them.  

Risk will often be visible in behavior before it is clearly defined in policy or guidance. The same pattern has been seen in pre-CVE disclosure detection, where abnormal activity appears before the industry has named or described the vulnerability. AI systems introduce that uncertainty by design.

Security leaders should prioritize controls before AI is fully deployed, avoid generic AI security checklists, integrate AI risk into existing cyber programs, and mitigate the risk of non-deterministic technology with continuous oversight, monitoring, behavior analytics, anomaly detection, autonomous investigation, and autonomous containment.

Visibility has a different connotation with AI. Previously, audit logging worked for software/people, but with Generative AI-based systems, interpretability and explainability is difficult to understand, you cannot "undo" what has been done, or see the logic or control a chain of events. This is why behavioral-based detections and containment becomes critical.  

What capabilities should every AI security program include?

If an organization asked “what must be in place before scaling AI?”:

  1. AI Risk board and approval workflow
  1. IAM + PAM for all AI services and agents
  1. AI asset inventory
  1. Prompt/output DLP with sanctioned AI access – This is not just pre- and post- filters, but behavior-based detections of semantic interface as well as behavior-based analysis of output with associated risk context.  
  1. Shadow AI identification
  1. Secure MLOps – This is an entire paper itself
  1. Runtime guardrails and tool restrictions
    • Including AI Gateway/SASE/Zero trust/
  1. Runtime security with behavior-based detections
    • Complete visibility, monitoring, behavior analytics, anomaly detection, risk/intent/context evaluation of anomalies, autonomous investigation and autonomous containment of all AI assets across endpoint, network, SaaS, SASE, cloud, OT, email, and messaging platforms
  1. Secure data pipelines and data governance
  1. SOC workflow changes from malicious classification workflows to behavior-based detection workflows
  1. Remediation plans for AI-related incidents  

Layered Governance and Security Stack for Securing AI  

The following outline considers governance and security tools that should be considered, well-integrated, deployed, tested, operationalized and embedded within security workflows. These tools and controls map to NIST’s CMF for AI.  

These considerations do not need to be implemented in order. Runtime Detect and Respond will help mitigate risk while Governance, Visibility, and Identity mature.

Category Tooling Controls
Governance & Visibility
  • AI asset inventory / AI CMDB
  • Shadow AI discovery
  • SaaS discovery
  • AI usage on non-endpoint managed systems via network or cloud telemetry
  • MCP server/client usage via protocols
  • Browser telemetry
  • Gateway or SASE telemetry
  • Establish a risk board to set up controls
  • Mandatory registration of AI systems
  • Owner, data classification, intended use, and risk tier
  • Supplier disclosure requirements
  • Risk mitigation plan for AI adoption, innovation, or development
Identity, Access & Agent Control

Non-human autonomous agents should not have the full permissions associated with a human user.

  • IAM with workload identities
  • PAM for AI service accounts
  • Secrets management with short-lived tokens
  • Zero Trust principles
  • Identity, permission, and token hygiene
  • Unique identities per model, agent, and pipeline
  • Least privilege for tools, data, and APIs
  • Explicit approval for autonomous actions
Data Security & Privacy
  • Data classification and labeling
  • Enterprise DLP across endpoint, email, network, cloud, and SaaS
  • Forensics data capture after risky detections
  • Prompt-level DLP through behavior-based semantic analysis with risk and intent context
  • Input/interface analysis for risky data requests
  • Output analysis for sensitive data
  • Data integrity evaluation
  • Retention and redaction policies for prompts and responses
Secure MLOps / LLMOps
  • Secure CI/CD with AI-specific gates
  • Model registries with approval workflows
  • Dependency, container, and artifact scanning
  • SBOM/AIBOM generation
  • IaC security scanning
  • Security posture management
  • Misconfiguration identification
  • Hardening recommendations
  • Signed models and prompts
  • Versioned datasets, configurations, logging, and controls
  • Securing data pipelines
  • Controlled promotion
  • Quality assurance
  • Adversarial testing
Runtime Security

Securing runtime goes beyond guardrails and model firewalls to include behavior-based detections, response, and containment.

  • Detection, monitoring, and SOC integration
  • Centralized visibility into prompts, outputs, and tool calls
  • AI-specific detections
  • Behavior-based detection for AI usage patterns
  • Model drift and behavior monitoring
  • Autonomous containment
  • Behavior-based detection of model inputs and outputs
  • Prompt injection detection
  • Model manipulation, including jailbreaking, poisoning, and related attacks
  • Sensitive data access attempts
  • Behavior-based detection across low-code agents, high-code agents, MCP clients and servers, endpoint, network, cloud, email, SaaS, SASE, IoT, and OT
  • Policy enforcement between users, models, tools, agents, SaaS models/tools, and MCP servers/clients
  • Risk, intent, and context evaluation for detections and response actions
Response & Recovery
  • Autonomous containment
  • AI-assisted playbooks
  • Forensics data capture for AI-related events
  • Model rollback mechanisms
  • Backup and restore for models and datasets
  • Kill switch for agents
  • Autonomous response to agents performing risky behaviors
  • Model and dataset rollback
  • Remediation plans
  • Tabletop exercises
  • Supplier coordination plans
  • Post-incident AI performance validation

AI security requires continuous visibility and behavioral detection

AI changes how fast systems move, how decisions are made, and how risk propagates. It does not change the fundamentals of security. Organizations that succeed will be the ones that apply those fundamentals rigorously, assume failure, and build systems that can detect, contain, and recover when AI behaves in ways they did not anticipate. Security is not what AI is allowed to do. It is whether the organization can understand, trust, and control what AI actually does in practice.  

Take this guidance to understand different initiatives that organizations should be considering. Securing AI is the most critical component to AI safety. As organizations invest more in AI adoption, they should be investing in security in order to mitigate the risk of AI adoption. Organizations should be evaluating their governance and security stack to include well-integrated tools that are deployed, tested, operationalized and embedded within security workflows. While organizations mature in governance, visibility and identity access management, they should be investing in behavior-based detection and autonomous containment to mitigate AI risk.  

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July 6, 2026

NIST Just Proved It: AI Security Can’t Be Solved With Rules

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Static AI guardrails are inherently limited

As organizations adopt generative AI, many still assume that the right set of guardrails will be enough. The problem is you can’t anticipate every way these systems might be misused, abused or attacked. What NIST has done is put a mathematical foundation under that intuition.

In recent research building on Gödel’s incompleteness theorems, which showed that any system built on a fixed set of rules will always have gaps, NIST demonstrates that there is no finite set of guardrails that can be universally robust against adversarial prompts. In plain terms, if your defense is based on a fixed set of rules, there will always be inputs that bypass them. Not because the rules are badly written, but because the problem space is bigger than static rules can ever cover.

This is not new in cybersecurity - detection rules have always had to live with this trade-off. What is different with GenAI is the scale and shape of that problem. These systems are built on human language, and human language is not bounded. It is fluid, contextual and deliberately ambiguous. The number of ways intent can be hidden is effectively limitless. You are not defending against a defined protocol or a fixed exploit chain. You are defending against the entire expressive capacity of people.

So attempting to create a complete set of rules is the wrong starting point. It assumes the problem can be deterministically described. NIST’s work shows that it cannot. Organizations still need a way to manage AI risk, but the traditional approach of defining allowed and disallowed patterns is always going to lag behind what is actually happening. The same input can be benign in one context and risky in another, and static rules struggle to capture that distinction.

The question then is what fills that gap?

AI security must shift from rules to behavior

What's required is a shift in what you are trying to understand. Rules try to describe what should and shouldn't happen. Behavior shows you what is happening. Or to put it another way, if inputs are unbounded and adversaries adapt, the only stable signal is behavior.

In a GenAI context, that means analyzing how an AI model is being used, how prompts evolve over time, how outputs are shaped, and where AI agent interactions start to drift from what is expected. It means moving from static definitions of bad to a more dynamic understanding of intent.

Instead of trying to predict every bad prompt, you focus on identifying when behavior starts to move outside expected norms. Instead of asking whether a single input matches a rule, you ask whether the overall pattern of activity makes sense for the system and how it’s being used.

Guardrails remain important but they are only one layer

This does not eliminate the need for guardrails. They still play a role. But they will never address the entire problem space and are simply one part of your defense in depth approach.

NIST’s proof is useful because it makes this explicit. It removes the assumption that with enough effort, a complete rule set is achievable. It isn’t.

Once you accept that, the shift becomes unavoidable. This is no longer a problem of writing better rules, but of understanding behavior in a space where the possible inputs are effectively unbounded.

For security leaders, that changes the nature of the problem. It is less about defining what should be allowed, and more about recognizing when something is no longer consistent with expected behavior.

That does not remove the need for guardrails, but it does change their role. They set boundaries, but they do not define understanding. The gap between the two is where risk now sits.

In the end, this is what “can’t be solved with rules” really means. Rules will always leave gaps, and those gaps are not theoretical. They show up in how systems actually behave Not what we expect them to do, or what we intended them to do, but what they are doing in practice. That is where the signal is, and increasingly, that is where the security problem sits.

References:

https://www.nist.gov/news-events/news/2026/06/nist-mathematical-proof-supports-transition-continuous-monitor-and-update

https://ieeexplore.ieee.org/document/11475847

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About the author
Andrew Hollister
Principal Solutions Engineer, Cyber Technician
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