Sarah, Chief Information Security Officer (CISO) at a global financial services firm, received an alert in the early morning two months ago. An AI model managing fraud detection had started flagging legitimate transactions at an alarming rate. Within minutes, customer complaints flooded in. The culprit? A sophisticated adversarial attack designed to poison the model's decision-making process. This may be a hypothetical scenario, but it represents the new battlefield of trust and safety, where artificial intelligence (AI) both creates vulnerabilities and ultimately, provides the shield against them.
As organizations accelerate AI adoption across critical operations, the attack surface has been expanding exponentially. AI safety services have evolved from theoretical frameworks into essential infrastructure, creating multilayered defense systems that continuously monitor, detect, and neutralize threats targeting intelligent systems.
understanding the threats
Adversarial attacks on AI and machine learning (ML) systems have surged in both prevalence and sophistication over the last few years, with recent reports emphasizing an expanding threat surface across industries and use cases. The proportion of organizations considering adversarial AI as a primary risk and the documented attack success rates are at record levels.
The World Economic Forum reports that almost half (~47%) of organizations worldwide rank adversarial threats, especially those powered by generative AI, as a primary cybersecurity concern in 2025. Equally startling, the success rates for adversarial attacks against certain production-class AI systems are said to have reached 80%, highlighting their effectiveness at bypassing model defenses.
These attacks against AI systems are fundamentally different from traditional cybersecurity threats. Rather than exploiting code vulnerabilities, attackers manipulate the learning mechanisms themselves: the data the systems train on to build the models, and the algorithms the models are built on. Industry experts indicate that these attacks can be categorized into three primary vectors:
- Input manipulation, where subtle alterations to data fool AI models into making incorrect classifications;
- Model extraction, where attackers reverse-engineer proprietary algorithms; and
- Data poisoning, training datasets are corrupted to embed malicious behaviors.
The sophistication of these threats demands equally sophisticated responses. The onus is on businesses and organizations globally to shift from reactive security postures to proactive AI safety frameworks to anticipate and neutralize these threats before they materialize.
defense starts with anomaly detection
AI safety latches today start with anomaly detection systems that function as sentinels, continuously monitoring model behavior for deviations from established patterns. These systems analyze multiple dimensions simultaneously, including input distributions, prediction confidence levels, processing times, and output consistency.
Consider anomaly detection in manufacturing. Companies in this space may implement behavioral baselines for their predictive maintenance AI. Suppose a system suddenly begins recommending premature equipment replacements, anomaly detection flags are triggered immediately. In such scenarios, investigations have revealed that sensor data had been subtly manipulated, potentially costing millions in unnecessary maintenance.
The sophistication of these systems lies in distinguishing genuine anomalies from benign variations. Advanced detection systems may leverage ensemble approaches, using a combination of statistical methods with deep learning algorithms to identify threats across different operational contexts. They establish dynamic thresholds that adapt to changing business conditions while maintaining vigilance against genuine attacks.
defending against adversarial attacks
Adversarial robustness represents the second critical pillar of AI safety. Current approaches indicate that defensive strategies must operate at multiple levels, whether they consist of preprocessing inputs to detect and neutralize malicious perturbations, hardening models through adversarial training, or implementing verification layers that validate outputs against expected ranges.
Such robustness is particularly important in critical sectors such as healthcare, financial services, aviation, telecommunications, retail, and supply chains. Here, enterprises are best served by multi-tier approaches, where no single defense mechanism is relied upon. Diagnostic AI undergoes continuous adversarial testing and proof testing through red teaming and is systematically probed for weaknesses before attackers discover them. Each model iteration must demonstrate resilience against known attack patterns.
These defensive measures include techniques such as:
- Input sanitization, where data passes through filters designed to remove adversarial noise;
- Model ensembling, which aggregates predictions from multiple architectures to prevent single-point vulnerabilities; and
- Certified defenses that provide mathematical guarantees about model behavior under attack conditions.
real-time response mechanisms
Detection without response is of little value. Modern AI safety services typically have automated response systems that execute predetermined actions whenever threats materialize. These mechanisms operate across three timeframes:
- Immediate automated responses that “shut the door” and isolate compromised systems to prevent cascade failures
- Tactical responses that start the detective work by engaging security teams and initiating forensic analysis
- Strategic responses that update defense parameters and strengthen vulnerable components as long-term security measures.
Automation is crucial in this area. When adversarial attacks target vital systems and networks, response systems must immediately switch to backup models, preserve forensic evidence, and initiate parallel processing through multiple verification pathways. Services must continue to run without a hitch and customer experience must remain uninterrupted while security teams investigate and remediate the threat.
Response orchestration requires careful balance: aggressive enough to neutralize threats quickly yet measured enough to avoid disruptions due to false positives. Today, organizations increasingly implement graduated response frameworks where initial actions remain conservative, escalating only when confidence in threat detection reaches higher thresholds.
building resilient AI ecosystems
AI safety isn't a product but an ongoing self-healing, ever-learning, ever-on practice. Resilient systems incorporate continuous monitoring, regular adversarial testing, model versioning that enables rapid rollbacks, and cross-functional collaboration between data science, security, and operations teams.
Current best practices encompass defense-in-depth strategies where multiple safety mechanisms work in concert. No single technology provides complete protection; instead, layered approaches create resilient ecosystems that degrade gracefully under attack rather than failing catastrophically.
taking action
Organizations deploying AI systems cannot afford passive security postures. Here are a few tips to start the process:
- Conduct comprehensive AI risk assessments that map all your deployed models, their criticality, and current safety measures.
- Implement continuous monitoring for production AI systems, establishing baselines and anomaly thresholds.
- Develop incident response playbooks specifically for AI-related security events, ensuring teams understand how to respond when intelligent systems behave unexpectedly.
Consider partnering with providers who demonstrate robust AI safety capabilities and proven response mechanisms. The question is not whether your AI systems will face threats: it is whether you will detect threats in time and respond effectively when attacks occur.
how Infosys BPM can help
With mature practices in AI trust and safety, comprising sophisticated machine learning-based anomaly detection systems and services, Further, The Infosys Responsible AI Toolkit, an open-source offering, provides a collection of technical guardrails that integrate security, privacy, fairness, and explainability into artificial intelligence (AI) workflows. Infosys BPM harnesses the power of data to build leading-edge AI systems for enterprises globally.
