With 77% of enterprises already victimized by adversarial AI attacks and eCrime actors achieving a record breakout time of just 2 minutes and seven seconds, the query isn’t in case your Security Operations Center (SOC) will likely be targeted — it’s when.
As cloud intrusions soared by 75% previously 12 months, and two in five enterprises suffered AI-related security breaches, every SOC leader must confront a brutal truth: Your defenses must either evolve as fast because the attackers’ tradecraft or risk being overrun by relentless, resourceful adversaries who pivot in seconds to succeed with a breach.
Combining generative AI (gen AI), social engineering, interactive intrusion campaigns and an all-out assault on cloud vulnerabilities and identities, attackers are executing a playbook that seeks to capitalize on every SOC weakness they’ll find. CrowdStrike’s 2024 Global Threat Report finds that nation-state attackers are taking identity-based and social engineering attacks to a brand new level of intensity. Nation-states have long used machine learning to craft phishing and social engineering campaigns. Now, the main focus is on pirating authentication tools and systems including API keys and one-time passwords (OTPs).
“What we’re seeing is that the threat actors have really been focused on…taking a legitimate identity. Logging in as a legitimate user. And then laying low, staying under the radar by living off the land through the use of legitimate tools,” Adam Meyers, senior vp counter adversary operations at CrowdStrike, told VentureBeat during a recent briefing.
Cybercrime gangs and nation-state cyberwar teams proceed sharpening their tradecraft to launch AI-based attacks geared toward undermining the muse of identity and access management (IAM) trust. By exploiting fake identities generated through deepfake voice, image and video data, these attacks aim to breach IAM systems and create chaos in a targeted organization.
The Gartner figure below shows why SOC teams have to be prepared now for adversarial AI attacks, which most frequently take the shape of faux identity attacks.
Source: Gartner 2025 Planning Guide for Identity and Access Management. Published on October 14, 2024. Document ID: G00815708.
Scoping the adversarial AI threat landscape going into 2025
“As gen AI continues to evolve, so must the understanding of its implications for cybersecurity,” Bob Grazioli, CIO and senior vp of Ivanti, recently told VentureBeat.
“Undoubtedly, gen AI equips cybersecurity professionals with powerful tools, however it also provides attackers with advanced capabilities. To counter this, latest strategies are needed to forestall malicious AI from becoming a dominant threat. This report helps equip organizations with the insights needed to remain ahead of advanced threats and safeguard their digital assets effectively,” Grazioli said.
A recent Gartner survey revealed that 73% of enterprises have a whole lot or hundreds of AI models deployed, while 41% reported AI-related security incidents. According to HiddenLayer, seven in 10 firms have experienced AI-related breaches, with 60% linked to insider threats and 27% involving external attacks targeting AI infrastructure.
Nir Zuk, CTO of Palo Alto Networks, framed it starkly in an interview with VentureBeat earlier this 12 months: Machine learning assumes adversaries are already inside, and this demands real-time responsiveness to stealthy attacks.
Researchers at Carnegie Mellon University recently published “Current State of LLM Risks and AI Guardrails,” a paper that explains the vulnerabilities of enormous language models (LLMs) in critical applications. It highlights risks comparable to bias, data poisoning and non-reproducibility. With security leaders and SOC teams increasingly collaborating on latest model safety measures, the rules advocated by these researchers have to be a part of SOC teams’ training and ongoing development. These guidelines include deploying layered protection models that integrate retrieval-augmented generation (RAG) and situational awareness tools to counter adversarial exploitation.
SOC teams also carry the support burden for brand new gen AI applications, including the rapidly growing use of agentic AI. Researchers from the University of California, Davis recently published “Security of AI Agents,” a study examining the safety challenges SOC teams face as AI agents execute real-world tasks. Threats including data integrity breaches and model pollution, where adversarial inputs may compromise the agent’s decisions and actions, are deconstructed and analyzed. To counter these risks, the researchers propose defenses comparable to having SOC teams initiate and manage sandboxing — limiting the agent’s operational scope — and encrypted workflows that protect sensitive interactions, making a controlled environment to contain potential exploits.
Why SOCs are targets of adversarial AI
Dealing with alert fatigue, turnover of key staff, incomplete and inconsistent data on threats, and systems designed to guard perimeters and never identities, SOC teams are at a drawback against attackers’ growing AI arsenals.
SOC leaders in financial services, insurance and manufacturing tell VentureBeat, under the condition of anonymity, that their firms are under siege, with a high variety of high-risk alerts coming in day by day.
The techniques below deal with ways AI models will be compromised such that, once breached, they supply sensitive data and will be used to pivot to other systems and assets throughout the enterprise. Attackers’ tactics deal with establishing a foothold that results in deeper network penetration.
- Data Poisoning: Attackers introduce malicious data right into a model’s training set to degrade performance or control predictions. According to a Gartner report from 2023, nearly 30% of AI-enabled organizations, particularly those in finance and healthcare, have experienced such attacks. Backdoor attacks embed specific triggers in training data, causing models to behave incorrectly when these triggers appear in real-world inputs. A 2023 MIT study highlights the growing risk of such attacks as AI adoption grows, making defense strategies comparable to adversarial training increasingly essential.
- Evasion Attacks: These attacks alter input data in an effort to mispredict. Slight image distortions can confuse models into misclassifying objects. A preferred evasion method, the Fast Gradient Sign Method (FGSM), uses adversarial noise to trick models. Evasion attacks within the autonomous vehicle industry have caused safety concerns, with altered stop signs misinterpreted as yield signs. A 2019 study found that a small sticker on a stop sign misled a self-driving automotive into considering it was a speed limit sign. Tencent’s Keen Security Lab used road stickers to trick a Tesla Model S’s autopilot system. These stickers steered the automotive into the incorrect lane, showing how small, rigorously crafted input changes will be dangerous. Adversarial attacks on critical systems like autonomous vehicles are real-world threats.
- Exploiting API vulnerabilities: Model-stealing and other adversarial attacks are highly effective against public APIs and are essential for obtaining AI model outputs. Many businesses are at risk of exploitation because they lack strong API security, as was mentioned at BlackHat 2022. Vendors, including Checkmarx and Traceable AI, are automating API discovery and ending malicious bots to mitigate these risks. API security have to be strengthened to preserve the integrity of AI models and safeguard sensitive data.
- Model Integrity and Adversarial Training: Without adversarial training, machine learning models will be manipulated. However, researchers say that while adversarial training improves robustness it requires longer training times and should trade accuracy for resilience. Although flawed, it’s a necessary defense against adversarial attacks. Researchers have also found that poor machine identity management in hybrid cloud environments increases the chance of adversarial attacks on machine learning models.
- Model Inversion: This style of attack allows adversaries to infer sensitive data from a model’s outputs, posing significant risks when trained on confidential data like health or financial records. Hackers query the model and use the responses to reverse-engineer training data. In 2023, Gartner warned, “The misuse of model inversion can result in significant privacy violations, especially in healthcare and financial sectors, where adversaries can extract patient or customer information from AI systems.”
- Model Stealing: Repeated API queries will be used to copy model functionality. These queries help the attacker create a surrogate model that behaves like the unique. AI Security states, “AI models are sometimes targeted through API queries to reverse-engineer their functionality, posing significant risks to proprietary systems, especially in sectors like finance, healthcare and autonomous vehicles.” These attacks are increasing as AI is used more, raising concerns about IP and trade secrets in AI models.
Reinforcing SOC defenses through AI model hardening and provide chain security
SOC teams must think holistically about how a seemingly isolated breach of AL/ML models could quickly escalate into an enterprise-wide cyberattack. SOC leaders must take the initiative and discover which security and risk management frameworks are probably the most complementary to their company’s business model. Great starting points are the NIST AI Risk Management Framework and the NIST AI Risk Management Framework and Playbook.
VentureBeat is seeing that the next steps are delivering results by reinforcing defenses while also enhancing model reliability — two critical steps to securing an organization’s infrastructure against adversarial AI attacks:
Commit to repeatedly hardening model architectures. Deploy gatekeeper layers to filter out malicious prompts and tie models to verified data sources. Address potential weak points on the pretraining stage so your models withstand even probably the most advanced adversarial tactics.
Never stop strengthing data integrity and provenance: Never assume all data is trustworthy. Validate its origins, quality and integrity through rigorous checks and adversarial input testing. By ensuring only clean, reliable data enters the pipeline, SOCs can do their part to keep up the accuracy and credibility of outputs.
Integrate adversarial validation and red-teaming: Don’t wait for attackers to search out your blind spots. Continually pressure-test models against known and emerging threats. Use red teams to uncover hidden vulnerabilities, challenge assumptions and drive immediate remediation — ensuring defenses evolve in lockstep with attacker strategies.
Enhance threat intelligence integration: SOC leaders must support devops teams and help keep models in sync with current risks. SOC leaders need to offer devops teams with a gentle stream of updated threat intelligence and simulate real-world attacker tactics using red-teaming.
Increase and keep enforcing supply chain transparency: Identify and neutralize threats before they take root in codebases or pipelines. Regularly audit repositories, dependencies and CI/CD workflows. Treat every component as a possible risk, and use red-teaming to reveal hidden gaps — fostering a secure, transparent supply chain.
Employ privacy-preserving techniques and secure collaboration: Leverage techniques like federated learning and homomorphic encryption to let stakeholders contribute without revealing confidential information. This approach broadens AI expertise without increasing exposure.
Implement session management, sandboxing, and nil trust starting with microsegmentation: Lock down access and movement across your network by segmenting sessions, isolating dangerous operations in sandboxed environments and strictly enforcing zero-trust principles. Under zero trust, no user, device or process is inherently trusted without verification. These measures curb lateral movement, containing threats at their point of origin. They safeguard system integrity, availability and confidentiality. In general, they’ve proven effective in stopping advanced adversarial AI attacks.
Conclusion
“CISO and CIO alignment will likely be critical in 2025,” Grazioli told VentureBeat. “Executives must consolidate resources — budgets, personnel, data and technology — to boost a company’s security posture. A scarcity of knowledge accessibility and visibility undermines AI investments. To address this, data silos between departments comparable to the CIO and CISO have to be eliminated.”
“In the approaching 12 months, we’ll must view AI as an worker moderately than a tool,” Grazioli noted. “For instance, prompt engineers must now anticipate the varieties of questions that might typically be asked of AI, highlighting how ingrained AI has turn out to be in on a regular basis business activities. To ensure accuracy, AI will have to be trained and evaluated just like all other worker.”
