AI in Surveillance: How AI Is Transforming Compliance Monitoring

What is AI in surveillance?

Artificial intelligence (AI) in surveillance is the use of AI-enabled technologies, principally machine learning and, increasingly, large language models (LLMs), to detect conduct, compliance, and market abuse risks in communications and trading activity that firms must monitor. While compliance teams traditionally relied on lexicon-based systems, which would run communications against fixed keyword lists, AI models interpret context to determine the true meaning of a message and judge whether it is genuinely risky.

In communications surveillance, AI read messages sent via channels like email, instant messaging (IM), text, and even voice transcriptions to uncover misconduct. In trade surveillance, it analyzes trade data to help distinguish manipulative trading from regular business activity. This guide focuses on how AI-enabled surveillance works, what it makes possible, where it falls short, and how it must be governed.

Why is surveillance moving to AI?

Surveillance teams have relied on lexicons for years, which are predefined lists of risky keywords and phrases. While lexicons are fast and transparent, they have structural weaknesses that AI is designed to address:

• False positives. Keyword-based systems flag any message containing a pre-determined “risky” word or phrase, regardless of meaning, so reviewers spend most of their time clearing alerts that are not risks at all.
• No understanding of context. A word that is innocent in one sentence could be risky in another. Since lexicons cannot decipher context, they miss coded language, sarcasm, and risk expressed without pre-determined keywords.
• Volume and channel growth. Business now spans email, chat, collaboration tools, mobile, voice, and social media. The sheer scale of communications has outgrown what keyword-based systems can handle.
• Rising regulatory expectations. As technology advances, regulators increasingly expect firms to detect subtle misconduct and explain how they do it, which standard lexicon-based systems cannot do.

Global Relay’s State of AI in Surveillance 2025 report found 38% of firms were still watching the space to decide whether to adopt AI. A year earlier, 57% of firms said they had no plans to implement AI. With 19% more firms deciding to adopt AI in twelve months, it’s clear that AI is being increasingly integrated into surveillance workflows. For the wider picture, see what the 1LOD 2026 Surveillance Benchmarking Survey tells us.

Lexicon, machine learning, and LLMs: what is the difference?

Various risk management systems coexist in surveillance, and most mature programs use a combination rather than any one alone:

• Lexicon-based detection. Predefined keywords and phrases flag potential risks. Lexicon-based systems are efficient and easy to audit, but cannot determine context and are prone to high false-positive rates.
• Machine learning models. Machine learning models are trained on labeled data to recognize patterns. While these improve on lexicons, they heavily depend on well-curated training sets and can struggle with new or rare behaviors.
• Large language models. LLMs are trained on vast, general datasets, and offer contextual understanding without the domain-specific training that smaller bespoke models require. They analyze whole conversations, evaluate intent and sentiment, and explain their reasoning, though they must be carefully aligned to a firm’s risks and governed for accuracy.

Surveillance teams must strike fine balance between recall (catching everything that matters) and precision (not raising alerts on everything). While lexicons can struggle with these goals, LLMs that are properly aligned can improve both at once. For a deeper look at how these models are trained and applied, see our LLM myth-busters explainer.

How does AI-enabled surveillance work?

A modern surveillance workflow layers AI onto a well-defined data pipeline. The typical stages are:

• Capture and standardization. Communications are captured across every channel and normalized into a consistent and structured format. The quality of this step determines the quality of everything after it.
• Noise reduction. Low-value content such as spam, marketing, and disclaimers is filtered out before analysis. This helps the surveillance model concentrate effort on messages that matter.
• Contextual analysis. The model reads complete conversations rather than isolated sentences to assess intent, sentiment, and implied meaning. By analyzing context, LLMs recognize evasion tactics such as deliberate misspellings or spacing designed to dodge keyword filters.
• Step-by-step reasoning. LLM systems use a chain-of-thought approach, meaning they will read through a conversation before deciding whether to raise an alert and generate a rationale after determining that a message is suspicious.
• Explainable alerting. Each alert comes with an explanation of why it was raised so that both reviewers and regulators can follow the model’s reasoning.
• Human review. Humans remain in the loop and will review alerts that an LLM flagged to verify its reasoning and investigate suspicious messages further.

What are the benefits of AI in surveillance?

• Fewer false positives. Contextual analysis filters out the false positives that flood lexicon-based queues, freeing up time for analysts to focus on genuine risk.
• Higher recall. AI catches subtle, coded, or context-dependent misconduct that keyword lists miss.
• Explainability. Modern models articulate why an alert was raised, which supports defensible decisions and regulatory scrutiny.
• Scale and speed. AI processes volumes of communication that no human team could review, including transcribed voice.
• Adaptability. Advanced, adaptable systems adjust to new risks and channels without surveillance teams having to retrain them from scratch.

What are the risks and challenges?

AI is not a magic wand, and treating it as one can introduce new risks. The main challenges firms must manage are:

• Data quality. As the oldest rule in computing suggests — garbage in, garbage out. Incomplete capture undermines even the best model, which is why AI surveillance relies on well-structured data.
• Explainability and the black box. A model that cannot explain its decisions is difficult to defend to a regulator. Consequently, explainability is a requirement, not a nice-to-have.
• Accuracy, bias, and hallucination. AI models can reflect bias in their data, or produce confident but wrong outputs. This means that their results must be validated by a human rather than trusted blindly.
• Model drift. Performance degrades over time as language, markets, and behavior change, so models need ongoing monitoring and tuning.
• Data security. A common question people ask regarding AI is where sensitive communications data goes, and who can access it, particularly with third-party or shared-cloud models.
• Over-reliance. AI supports human judgment, but does not replace it. Beyond being good practice, keeping analysts in the loop is a regulatory requirement.

How is AI in surveillance governed?

Since AI-enabled surveillance systems makes risk decisions on regulated business activity, model governance frameworks are constantly evolving. These are the key elements of a strong framework:

• AI governance and model risk management. Firms are expected to document, test, validate, and monitor their models. Firms must regularly revise model risk management frameworks to account for advancing AI uses, with clear ownership and controls.
• Explainability and human oversight. Regulators emphasize that automated decisions must be explainable and subject to meaningful human oversight.
• Emerging AI regulation. The EU AI Act, which entered into force in 2024, is introducing obligations for higher-risk AI systems. Through 2026 and beyond, firms can expect added transparency, documentation, and oversight requirements when using AI systems.
• Data security and residency. Where and how models process data matters, especially when considering data security and benchmarking. Some providers host models internally, while some rely on private or public cloud providers.

AI governance also sits within the wider digital communications governance framework firms use to capture, retain, supervise, and investigate regulated business communications.

Where is AI in surveillance heading?

Agentic models, which reason through a problem in steps rather than classifying it in one pass, are the latest and most advanced AI technology. These models are shifting risk detection from pattern-matching toward something closer to how a compliance analyst thinks.

At the same time, generative AI is becoming a communication channel in its own right. Gartner expects that by 2029, 40% of digital communications governance and archiving customers will monitor conversations with internal and external-facing generative AI.

AI-enabled surveillance systems are also increasingly being used to monitor conduct and culture. Yet, per the 1LOD 2026 Surveillance Benchmarking Survey, while around 70% of firms are piloting or deploying AI, not one considers it fully embedded, and 22% of banks admit their infrastructure does not effectively manage market-abuse risk. The technology is arriving faster than the data foundations needed to use it.

What does good AI surveillance look like?

• Built on complete data. First, firms need to capture communications across every channel. AI amplifies good data and exposes bad.
• Explainable by design. Every alert an AI-enabled system raises should be justified using reasoning a reviewer and regulator can follow.
• Human-in-the-loop. While AI systems are meant to analyze and flag potential risks, humans must be investigating alerts and making final decisions.
• Governed and tested. Models should be documented, validated, monitored for drift, and owned within a clear risk framework.
• Secure. Sensitive data must be processed in a controlled and transparent environment.
• Combined risk systems. Lexicons and AI can be used together, with surveillance teams playing to the strengths of each.

Frequently asked questions

What is AI in surveillance?

The use of AI technologies, such as machine learning and large language models, to detect misconduct risk in communications and trading activity by interpreting meaning and context.

Is AI replacing lexicon-based surveillance?

Not entirely. Lexicons are still a useful method for flagging high-risk terms, but AI adds contextual understanding that reduces false positives and catches coded or ambiguous misconduct. Most firms use both together.

How does AI reduce false positives?

By analyzing a message in the context of the whole conversation rather than flagging every message that contains a keyword so that innocent matches are filtered out before they reach a reviewer.

Is AI surveillance explainable?

It needs to be. Modern systems provide an explanation for each alert, often using step-by-step (chain-of-thought) reasoning, so decisions can be justified to reviewers, auditors, and regulators. Explainability is a regulatory expectation, not a nice-to-have.

What are the risks of using AI in surveillance?

Poor data quality, lack of explainability, bias or inaccurate outputs, data security concerns, and over-reliance on automation. All are manageable with strong data, comprehensive governance, and human oversight.

How is AI in surveillance regulated?

Through existing supervision and recordkeeping rules as well as model risk management expectations. AI-specific regulation is also emerging, such as the EU AI Act, which emphasizes transparency, documentation, and human oversight for higher-risk systems.

How Global Relay helps

Global Relay is a market leader in AI-enabled communications surveillance. Global Relay Surveillance uses natively built agentic AI and a best-in-class large language model with an integrated chain-of-thought process, which can analyze whole conversations, rationalize alerts, and cut false positives. Global Relay’s Al-enabled surveillance is aligned to more than 130 compliance risk indicators and hosted in our own private data centers for security and transparency. See our AI-enabled approach to surveillance.

Related reading:

• LLM myth-busters: how AI training for comms surveillance works
• Demystifying AI in surveillance (video series)
• Global Relay’s AI-enabled approach to surveillance
• Digital communication compliance: from email to AI
• What the 1LOD 2026 Surveillance Benchmarking Survey tells us
• The challenge of proving market abuse